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What Is Data Management? Definition, Benefits, Uses

Data Management (DM) comprises a comprehensive collection of consistent and responsible practices, concepts, and processes. These resources align data for business success and implement a Data Strategy. Additionally, they span long-term, abstract planning to hands-on, day-to-day data activities. Since DM spans many outcomes, behaviors, and activities throughout the data lifecycle, companies find organizing it into a framework helpful. […]

Data Management (DM) comprises a comprehensive collection of consistent and responsible practices, concepts, and processes. These resources align data for business success and implement a  Data Strategy . Additionally, they span long-term, abstract planning to hands-on, day-to-day data activities.

Since DM spans many outcomes, behaviors, and activities throughout the data lifecycle, companies find organizing it into a  framework  helpful. Such a methodology includes at least the following:

• Data Strategy:  The basis for prioritizing resources and doing data operations
• Data Governance:  A formalization of Data Management policies, procedures, and roles
• Data Architecture:  The enterprise’s data infrastructure in its totality and its components

Organizations manage these three components, among others, to increase business opportunities, run operations well, and reduce risks.

Data Management Defined

Sometimes, Data Management gets interchanged with its components and practices during business communications. Consequently, it gets used in a particular context, such as a Data Governance program or implementing a specific data platform, while keeping other DM principles aside.

This kind of communication solves problems or deals with matters quickly. However, formalized Data Management definitions take a broad stance to focus on its entire framework, so other components get considered by the business, in general. 

For example, DAMA International’s DMBoK  defines Data Management as the “development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.” This meaning spans all activities with data through the course of its usage.

Data Management also includes any connection between business and data. This concept covers all enterprise data subject areas and structure types to meet the data consumption requirements of all applications and business processes. Additionally, any data events and practices necessary to use data in  business decisions  fall in the context of DM.

Processes and involvements around delivering consistent and real-time data across the company happen under the DM umbrella. This includes improved scalability, visibility, quality, preparation, governance, security, and reliability. 

Data Management Components

The assortment of Data Management practices, concepts, and processes forms different components, describable according to the Data Management framework. DAMA International has provided an evolved DMBoK 2 Wheel, as seen below:

A yellow circle with high-level concepts surrounds Data Management activities conducted across its lifecycle and foundational activities. These ideas inform, guide, and drive the implementation of DM in an organization. They include the Data Strategy and Data Governance.

Foundational activities serve Data Management work done to manage the lifecycle and emerge from Data Governance deliverables. Examples of these outputs include:

• Data security:  Implementing policies and procedures to ensure people and things take the right actions with data and information assets, even with malicious inputs. 
• Metadata Management:  Good Metadata Management “ creates the context  for other data elements, providing a complete picture of the data,” notes writer David Kolinek. This holistic view allows for organizing and locating data, understanding its meaning, and maximizing its value.
• Data Quality Management :  Data Quality  (DQ) describes the degree of business and consumer confidence in data’s usefulness based on agreed-upon business requirements. Data Quality Management aligns DQ expectations with reality.

Lifecycle management activities happen daily on the ground and are the most hands-on and visible parts of Data Management. These activities show up in three categories:

• Planning and Designing : Planning and designing practices combine the high-level conceptual guidance and the foundational activities into practical requirements to implement technically.  For example, Data Architecture represents a planning and designing management activity.
• Enabling and Maintaining : Enablement and maintenance activities focus on DataOps to ensure predictable data communication, integration, and automation. Master Data Management, a method to ensure uniformity and accuracy of an organization’s shared data assets, constitutes a set of enablement and maintenance activities.
• Using and Enhancing:  Activities that use and enhance data directly generate business insights and typify the work of analysts, data scientists, and other business professionals. For example, data visualization describes how the information appears on the screen, determining its usefulness.

Data Management vs. Data Governance

When managers and workers discuss Data Governance, they may substitute the concept of Data Management. The meanings of Data Governance and DM overlap quite a bit in processes around Data Quality, integration, policies, and standards.

However, DM also covers implementations of policies and procedures that do not fall under the mantle of Data Governance through technologies and tools. Day-to-day data activities, such as  data observability , are not categorized as a Data Governance practice but are covered in Data Management practices.

Is Data Management Covered in Data Security?

Data security  represents an essential component of Data Management. Its practices protect digital information from unauthorized access, corruption, or theft throughout its entire lifecycle, encompass every aspect of information security, and closely tie in with Data Governance.

However, focusing only on data security misses important Data Management aspects. For example,  data privacy  focuses on protecting personal data but may not overlap with data security. A business division may learn about an employee’s relationship status on social media, but if the organization fails to inform that person of the discovery, it calls into question the company’s Data Management of data privacy.

Moreover,  gaining insights  through reporting and analytics is a primary driver of DM. Since executives have a stronger drive to find new business opportunities, which requires appropriate access and transparency with data across their organizations, that factors into Data Management frameworks.

The Role of Digital Transformation

While Data Management is a strong foundation of  digital transformation  management, they are not the same. Data Management concentrates on leveraging data to maintain and improve the business. Digital transformation management leverages new technologies to support and advance a company.

For example, say a company, Dynamic, wants to transform its operations digitally through newer generative AI technologies. Dynamic will need to do DM processes to improve  access  to knowledge about its customers, employees, products, or finances. 

Also, Dynamic will need to start with its people to share their knowledge for digital transformation. To get to this point, Dynamic will need to encourage their  teams to work  together, which may involve an outing to bring remote workers together for lunch. Although not a DM event, the lunch would provide a building block for digital transformation.

In the meantime, Dynamic would need to train employees to become  data-literate , improving their work and analysis of enterprise data. Such Data Literacy training may not be relevant to digital transformation but may be relevant to other DM aspects, like human compliance with the General Data Privacy Regulation (GDPR).

Benefits of Data Management

Data, a reusable resource, fuels business opportunities and revenue. Data Management provides the engine to drive data towards that end. 

Additionally, DM saves  companies money  and increases efficiency. It provides a means to identify and handle risks, such as inefficient operations or fines due to a lack of compliance or a data breach.

Moreover, organizations use Data Management to adapt quickly when the business environment changes. Through DM, they can  handle common  and ongoing challenges, like increasing data volumes, new roles for analytics, and compliance requirements. 

Businesspeople see these advantages concretely, with

• Better performance of business activities with more scalability
• Improved customer relationships by customizing their experience
• Enhanced security and privacy
• More effective marketing and sales campaigns
• Improved data access through greater capabilities to share data
• Faster delivery of products and services 
• Improved operations management by streamlining individual activities together
• Better regulation and compliance controls
• Speedier API and system development
• Improved decision-making and reporting, especially with real-time data
• Better  data flow  across business units across the organization
• More consistency across all enterprise work activities
• Faster adoption of AI technologies

Data Management use cases span various applications, technologies, industries, and outcomes. These examples span larger long-term business goals and specific technical implementations.

Long-Term Enterprise-Wide Scenarios

See below for long-term, company-wide examples:

• The  USTRANSCOM  developed and implemented a Data Strategy for better-informed decision-making, customer understanding, and improvements in operations. 
• An institution for  higher learning  implemented a Data Governance program as an enterprise-wide initiative, including a data catalog. This DM implementation led to enhanced collaboration and transparency, with notifications about Data Governance decisions decreasing from 80 to 25 days.
• A healthcare company  implemented  machine learning (ML) technologies to identify and address fraud. 
• A  global financial  services company implemented a robust DM framework to accommodate high transaction volumes.
• European automakers deployed  a secure  data exchange ecosystem, Catena-X, with capabilities to detect a quality issue, reducing the number of vehicles to recall by more than 80%.

Short-Term Project-Based Scenarios

The list below contains use cases conducted on a particular short-term project or sub-group of units:

• A company  implemented digital transformation through a WalkMe app and a Digital Adoption Platform (DAP). The organization engaged customers through technology, people, and processes, leading to a central help content repository.
• A joint research initiative among a couple of universities, the E2e Project, captured  manufacturing data  about air compression through an Internet of Things (IoT) kit and reported energy usage. The manufacturers received recommendations to improve efficiency and target repairs and replacements.
• A  finance group  at an organization integrated its data with other business units across the organization.
• A company implemented data fabric to  follow up  on customer sentiments to predict churn and conduct advanced predictive and prescriptive analytics for optimizing products or processes.

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Data management is the IT discipline focused on ingesting, preparing, organizing, processing, storing, maintaining, and securing data throughout the enterprise. Data management is typically the responsibility of a data architect or database administrator, and the goal is ensuring that the organization’s data is consistent, usable, and secure across all enterprise systems and applications. End-to-end data management is aspirational for most enterprises, but all businesses should have an intentional, overarching data management strategy in place to guide their work.

Table of Contents

How Does Data Management Work?

Effective data management is done using a host of software-based tools that render data consistent across all systems, ensure it is of highest quality, and ensure that it meets security and governance standards. While data management is generally the role of a data architect, it engages nearly every IT discipline.

For example, if a business contracts with outside cloud vendors, data management often falls to the IT application manager, an IT security unit, the database group, an IT vendor contract management group, or even outside users and auditors. It is their responsibility to ensure that the data being furnished by the vendors meets or exceeds the standards that enterprises set for themselves.

When new applications and systems access data from other systems, the application team generally works with the database team to ensure that all data is accessible and usable across all system boundaries. The IT storage group or network group might make decisions about where data is ultimately stored. In short, virtually the entire IT team is involved in data management at some point, with the data architect or data administrator giving direction.

7 Types of Data Management

Organizations can employ different types of data management depending upon their unique datasets. While smaller businesses may use a few data management approaches, larger organizations may require a wider range of comprehensive techniques to best care for their data.

Data Architecture

Data architecture is a framework that helps an organization’s IT infrastructure with its data strategy by setting standards on how data is managed throughout its lifecycle. The ultimate goal is to ensure that data is high quality and reliable to inform strategic business decisions.

Data Modeling

Data modeling is a visual representation of an organization’s data, how it moves through the organization, and how it relates. The model sets rules for these relationships and determines how data moves according to those rules.

Data Pipelines

Data pipelines are automated workflows or pathways that allow data to get to their desired locations after it is processed. This enables a seamless extraction, transformation, and loading (ETL) of data from various sources to target specific destinations such as data warehouses or analytics platforms.

Data Cataloging

A data catalog involves the comprehensive inventorying or categorizing of an organization’s data assets and encompasses important metadata such as data definitions, lineage, usage, and access controls. Data catalogs frequently include additional functions that expedite data exploration and facilitate personalized queries and optimize data use.

Data Integrations

Data integration is the process of combining data from various sources into a complete, accurate, and up-to-date dataset for analysis, reporting, and operational purposes. Specific data techniques such as data replication, synchronization, and API-based connections facilitate seamless data exchange and allow these data to operate collaboratively across platforms or departments within the organization.

Data Governance

Data governance is a set of rules, strategic frameworks, policies, and processes to assure data quality, security, and compliance of an organization’s data assets. Rules and responsibilities are involved to enforce data standards and controls; establishing mechanisms for data stewardship, monitoring, and enforcement, mitigates risks and maximizes the value of data sources.

Data Security

Data security protects digital information from unauthorized access, manipulation, or theft, and includes physical hardware security, administrative controls, software application security, and organizational policies. Encryption, data masking, and redaction procedures help guarantee compliance and defend against cyber assaults, insider risks, and human error.

8 Data Management Best Practices

Best data management practices are essential guidelines for how businesses handle data, transforming it into a strategic resource that can be used to drive development and innovation.

• Define Clear Data Management Goals: To create a clear and attainable data strategy, first identify data requirements and establish quantifiable targets consistent with corporate goals.
• Create a Data Governance Framework: Data governance entails creating roles, responsibilities, and procedures to guarantee that data follows corporate rules and standards.
• Ensure Data Quality Assurance: Data quality assurance is the process of ensuring correctness and dependability through validation, cleaning, and normalization to keep data error-free and consistent.
• Ensure Data Security and Privacy: Encryption, access limits, and regular security audits are all necessary for protecting sensitive data, confidentiality, and integrity, and avoiding unwanted access or cyber threats.
• Streamline Data Integrations: This entails developing effective techniques for merging data from several sources to offer a complete and cohesive perspective and improve data usability.
• Enforce Documentation and Metadata Management: Keeping thorough records of data sources, structures, and metadata is critical for comprehending and managing data; it promotes traceability and helps to preserve the organization’s knowledge base
• Enforce Data Lifecycle Management: Managing the flow of data from creation to retirement ensures that it is relevant, accessible, and safe throughout its lifespan; this approach includes implementing procedures for data preservation, archiving, and disposal.
• Implement Master Data Management: Master Data Management (MDM) establishes a single source of truth for key corporate data, assuring consistency and correctness across all systems and divisions within an organization.

9 Benefits of Data Management

Data management can greatly improve an organization’s performance and decision-making ability. Here are some of the most common benefits:

• Eliminates Data Redundancy: By combining data sources and adopting a single source of truth, data management decreases data duplication across systems, resulting in more effective storage and retrieval processes.
• Improves Data Sharing: Effective data management promotes data exchange inside an organization and with external partners to foster teamwork and innovation.
• Strengthens Data Privacy and Security: Organizations may better safeguard sensitive data from breaches and unauthorized access by using strong data management procedures that ensure compliance with data protection regulations.
• Aids Backup and Recovery: Data management systems frequently feature automatic data backup and data recovery solutions, which are critical for ensuring business continuity in the event of a data loss or system failure.
• Streamlines Processes and Improves Efficiency: Data management may save time and improve operational efficiency by organizing and streamlining data operations, reducing redundancies, and automating repetitive tasks.
• Ensures Regulatory Compliance: Proper data management enables firms to comply with legal and regulatory obligations by keeping correct records and applying essential controls.
• Improves Data Security: A well-managed data environment improves data security by safeguarding it from internal and external threats while also lowering the chance of data breaches.
• Enhances Business Performance: Organizations that use optimized data processes can improve their performance indicators, get insights into client preferences, and increase sales efficiency.
• Gives a Competitive Advantage: Companies may obtain a competitive edge in the market by exploiting high-quality, well-managed data, which allows them to respond more effectively to changes and client needs.

Notable Challenges of Data Management

Data management tools are readily available to help organizations manage various types of data that they collect. Even with these tools, some challenges are inevitable, including data overload, poor quality or insecure data, or data silos, but awareness of the obstacles can help keep you from being caught off guard.

Data Overload

The amount of data being generated can overwhelm organizations of all sizes. Organizations have to not just manage the influx of data, but process and analyze it for valuable insights. A comprehensive data strategy needs to encompass storage, processing, analysis, and security to keep businesses from being drowned by the abundance of data.

Data Quality

The term “garbage in, garbage out” applies to data management—poor quality data can affect the foundation of the decision-making process, leading to missed opportunities. Organizations must have routine data cleaning protocols and quality checks at every step of the data lifecycle to ensure that data remains accurate, consistent, and reliable.

Poorly managed data can lead to breaches. Safeguarding sensitive information must be non-negotiable, and a proactive approach to data security must rely upon a multi-layered defense strategy that includes protocols such as vigilant data monitoring and rapid response.

Data collected from multiple sources can create a challenge for different team members to access if it’s not well organized and properly managed. It can be difficult to find the right solution for storing large amounts of data where it can be accessible and used. The best way to make data accessible is to use cloud storage and have an effective cloud-now storage strategy that lets companies store data and use AI/ML for faster data analysis, visualization, and data-driven decision-making.

Data Compliance

Going through the complex landscape of regulatory requirements is an ongoing challenge for any business. Since laws and regulations continuously evolve, maintaining data compliance can be a moving issue. Using automated compliance tools offers a proactive solution by regularly adapting to changing regulatory frameworks and ensuring continuous adherence to these evolving laws and regulations.

Lack of Skilled Workers

The demand for experienced data managers hinders organizations from fully optimizing their data management. Even though there are tools available to manage data, the shortage of experienced data management specialists capable of managing the entire process hinders an organization from maximizing the entire advantage of its data. Investing in entry-level data managers and providing them with tailored training may cost more but this can help an organization in having a streamline in processing their data.

Bottom Line: Company Strategies Should Evolve With Data Management

Organizations struggle to handle massive amounts of data efficiently in this fast-paced world of data management. Staying ahead requires continuous strategy development to adapt to this constant change, and investing in data management specialists helps organizations gain the competence they need to efficiently navigate the complex data environment. Embracing and adapting to these constant changes and technologies maximizes data utilization, streamlines operations, and improves the decision-making process. Adopting innovative and agile data management practices positions organizations to succeed in an increasingly data-driven world.

If you’re interested in data management, read about the types and challenges of data management , or see our expert picks for the top data management platforms and solutions .

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Data management is the practice of ingesting, processing, securing and storing an organization’s data, where it is then utilized for strategic decision-making to improve business outcomes.

Over the last decade, developments within hybrid cloud , artificial intelligence , the Internet of Things (IoT), and edge computing  have led to the exponential growth of big data, creating even more complexity for enterprises to manage. As a result, a data management discipline within an organization has become an increasing priority as this growth has created significant challenges, such as data silos, security risks, and general bottlenecks to decision-making.

Teams address these challenges head on with a number of data management solutions, which are aimed to clean, unify and secure data. This, in turn, allows leaders to glean insights through dashboards and other data visualization tools, enabling informed business decisions. It also empowers data science teams to investigate more complex questions, allowing them to leverage more advanced analytical capabilities, such as machine learning , for proof-of-concept projects. If they’re successful at delivering and improving against business outcomes, they can partner with relevant teams to scale those learnings across their organization through automation practices.

While data management refers to a whole discipline, master data management is more specific in its scope as it focuses on transactional data—i.e. sales records. Sales data typically includes customer, seller, and product information. This type of data enables businesses to determine their most successful products and markets and their highest valued customers. Since master data is inclusive of personally identifiable information (PII), it also conforms to stricter regulations, such as GDPR.

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The scope of a data management discipline is quite broad, and a strong data management strategy typically implements the following components to streamline their strategy and operations throughout an organization: 

Data processing: Within this stage of the data management lifecycle , raw data is ingested from a range of data sources, such as web APIs, mobile apps, Internet of Things (IoT) devices, forms, surveys, and more. It is, then, usually processed or loaded, via data integration techniques, such as extract, transform, load (ETL) or extract, load, transform (ELT) . While ETL has historically been the standard method to integrate and organize data across different datasets, ELT has been growing in popularity with the emergence of cloud data platforms and the increasing demand for real-time data. Independently of the data integration  technique used, the data is usually filtered, merged, or aggregated during the data processing stage to meet the requirements for its intended purpose, which can range from a business intelligence dashboard to a predictive machine learning algorithm. 

Data storage: While data can be stored before or after data processing, the type of data and purpose of it will usually dictate the storage repository that is leveraged. For example, data warehousing requires a defined schema to meet specific data analytics requirements for data outputs, such as dashboards, data visualizations , and other business intelligence  tasks. These data requirements are usually directed and documented by business users in partnership with data engineers, who will ultimately execute against the defined data model . The underlying structure of a data warehouse is typically organized as a relational system (i.e. in a structured data format), sourcing data from transactional databases. However, other storage systems, such as data lakes , incorporate data from both relational and non-relational systems , becoming a sandbox for innovative data projects. Data lakes benefit data scientists in particular, as they allow them to incorporate both structured and unstructured data into their data science projects. 

Data governance: Data governance is a set of standards and business processes which ensure that data assets are leveraged effectively within an organization. This generally includes processes around data quality, data access, usability, and data security. For instance, data governance councils tend align on taxonomies to ensure that metadata is added consistently across various data sources. This taxonomy should also be further documented via a data catalog to make data more accessible to users, facilitating data democratization across organizations. Data governance teams also help to define roles and responsibilities to ensure that data access is provided appropriately; this is particularly important to maintain data privacy. 

Data security: Data security sets guardrails in place to protect digital information from unauthorized access, corruption, or theft. As digital technology becomes an increasing part of our lives, more scrutiny is placed upon the security practices of modern businesses to ensure that customer data is protected from cybercriminals or disaster recovery incidents. While data loss can be devastating to any business, data breaches, in particular, can reap costly consequences from both a financial and brand standpoint. Data security teams can better secure their data by leveraging encryption and data masking within their data security strategy. 

While data processing, data storage, data governance and data security are all part of data management, the success of any of these components hinges on a company’s data architecture or technology stack. A company’s data infrastructure creates a pipeline for data to be acquired, processed, stored and accessed, and this is done by integrating these systems together. Data services and APIs pull together data from legacy systems, data lakes , data warehouses , sql databases , and apps, providing a holistic view into business performance. 

Each of these components in the data management space are undergoing a vast amount of change right now. For example, the shift from on-premise system to cloud platforms are one of the most disruptive technologies in the space right now. Unlike on-premise deployments, cloud storage providers allow users to spin up large clusters as needed, only requiring payment for the storage specified. This means that if you need additional compute power to run a job in a few hours vs. a few days, you can easily do this on a cloud platform by purchasing additional compute nodes.

This shift to cloud data platforms is also facilitating the adoption of streaming data processing. Tools, like Apache Kafka, allow for more real-time data processing, enabling consumers to subscribe to topics to receive data in a matter of seconds. However, batch processing still has its advantages as it’s more efficient at processing large volumes of data. While batch processing abides by a set schedule, such as daily, weekly, or monthly, it is ideal for business performance dashboards which typically do not require real-time data. 

Change only continues to accelerate in this space. More recently, data fabrics have emerged to assist with the complexity of managing these data systems. Data fabrics  leverage intelligent and automated systems to facilitate end-to-end integration of various data pipelines and cloud environments. As new technology like this develops, we can expect that business leaders will gain a more holistic view of business performance as it will integrate data across functions. The unification of data across human resources, marketing, sales, supply chain, et cetera can only give leaders a better understanding of their customer. 

Organizations experience a number of benefits when launching and maintaining data management initiatives: 

Reduced data silos: Most, if not all, companies experience data silos within their organization. Different data management tools and frameworks, such as data fabrics and data lakes, help to eliminate data silos and dependencies on data owners. For instance, data fabrics assist in revealing potential integrations across disparate datasets across functions, such as human resources, marketing, sales, et cetera. Data lakes, on the other hand, ingest raw data from those same functions, removing dependencies and eliminating single owners to a given dataset. 

Improved compliance and security: Governance councils assist in placing guardrails to protect businesses from fines and negative publicity that can occur due to noncompliance to government regulations and policies. Missteps here can be costly from both a brand and financial perspective. 

Enhanced customer experience: While this benefit will not be immediately seen, successful proof of concepts can improve the overall user experience, enabling teams to better understand and personalize the customer journey through more holistic analyses.

Scalability: Data management can help businesses scale but this largely depends on the technology and processes in place. For example, cloud platforms allow for more flexibility, enabling data owners to scale up or scale down compute power as needed. Additionally, governance councils can help to ensure that defined taxonomies are adopted as a company grows in size. 

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• CAREER FEATURE
• 13 March 2018

Data management made simple

• Quirin Schiermeier

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When Marjorie Etique learnt that she had to create a data-management plan for her next research project, she was not sure exactly what to do.

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doi: https://doi.org/10.1038/d41586-018-03071-1

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Ten Simple Rules for Creating a Good Data Management Plan

William k. michener.

College of University Libraries & Learning Sciences, University of New Mexico, Albuquerque, New Mexico, United States of America

Introduction

Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship between hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project’s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews.

As part of the research life cycle (A), many researchers (1) test ideas and hypotheses by (2) acquiring data that are (3) incorporated into various analyses and visualizations, leading to interpretations that are then (4) published in the literature and disseminated via other mechanisms (e.g., conference presentations, blogs, tweets), and that often lead back to (1) new ideas and hypotheses. During the data life cycle (B), researchers typically (1) develop a plan for how data will be managed during and after the project; (2) discover and acquire existing data and (3) collect and organize new data; (4) assure the quality of the data; (5) describe the data (i.e., ascribe metadata); (6) use the data in analyses, models, visualizations, etc.; and (7) preserve and (8) share the data with others (e.g., researchers, students, decision makers), possibly leading to new ideas and hypotheses.

Earlier articles in the Ten Simple Rules series of PLOS Computational Biology provided guidance on getting grants [ 1 ], writing research papers [ 2 ], presenting research findings [ 3 ], and caring for scientific data [ 4 ]. Here, I present ten simple rules that can help guide the process of creating an effective plan for managing research data—the basis for the project’s findings, research papers, and data products. I focus on the principles and practices that will result in a DMP that can be easily understood by others and put to use by your research team. Moreover, following the ten simple rules will help ensure that your data are safe and sharable and that your project maximizes the funder’s return on investment.

Rule 1: Determine the Research Sponsor Requirements

Research communities typically develop their own standard methods and approaches for managing and disseminating data. Likewise, research sponsors often have very specific DMP expectations. For instance, the Wellcome Trust, the Gordon and Betty Moore Foundation (GBMF), the United States National Institutes of Health (NIH), and the US National Science Foundation (NSF) all fund computational biology research but differ markedly in their DMP requirements. The GBMF, for instance, requires that potential grantees develop a comprehensive DMP in conjunction with their program officer that answers dozens of specific questions. In contrast, NIH requirements are much less detailed and primarily ask that potential grantees explain how data will be shared or provide reasons as to why the data cannot be shared. Furthermore, a single research sponsor (such as the NSF) may have different requirements that are established for individual divisions and programs within the organization. Note that plan requirements may not be labeled as such; for example, the National Institutes of Health guidelines focus largely on data sharing and are found in a document entitled “NIH Data Sharing Policy and Implementation Guidance” ( http://grants.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm ).

Significant time and effort can be saved by first understanding the requirements set forth by the organization to which you are submitting a proposal. Research sponsors normally provide DMP requirements in either the public request for proposals (RFP) or in an online grant proposal guide. The DMPTool ( https://dmptool.org/ ) and DMPonline ( https://dmponline.dcc.ac.uk/ ) websites are also extremely valuable resources that provide updated funding agency plan requirements (for the US and United Kingdom, respectively) in the form of templates that are usually accompanied with annotated advice for filling in the template. The DMPTool website also includes numerous example plans that have been published by DMPTool users. Such examples provide an indication of the depth and breadth of detail that are normally included in a plan and often lead to new ideas that can be incorporated in your plan.

Regardless of whether you have previously submitted proposals to a particular funding program, it is always important to check the latest RFP, as well as the research sponsor’s website, to verify whether requirements have recently changed and how. Furthermore, don’t hesitate to contact the responsible program officer(s) that are listed in a specific solicitation to discuss sponsor requirements or to address specific questions that arise as you are creating a DMP for your proposed project. Keep in mind that the principle objective should be to create a plan that will be useful for your project. Thus, good data management plans can and often do contain more information than is minimally required by the research sponsor. Note, though, that some sponsors constrain the length of DMPs (e.g., two-page limit); in such cases, a synopsis of your more comprehensive plan can be provided, and it may be permissible to include an appendix, supplementary file, or link.

Rule 2: Identify the Data to Be Collected

Every component of the DMP depends upon knowing how much and what types of data will be collected. Data volume is clearly important, as it normally costs more in terms of infrastructure and personnel time to manage 10 terabytes of data than 10 megabytes. But, other characteristics of the data also affect costs as well as metadata, data quality assurance and preservation strategies, and even data policies. A good plan will include information that is sufficient to understand the nature of the data that is collected, including:

• Types. A good first step is to list the various types of data that you expect to collect or create. This may include text, spreadsheets, software and algorithms, models, images and movies, audio files, and patient records. Note that many research sponsors define data broadly to include physical collections, software and code, and curriculum materials.
• Sources. Data may come from direct human observation, laboratory and field instruments, experiments, simulations, and compilations of data from other studies. Reviewers and sponsors may be particularly interested in understanding if data are proprietary, are being compiled from other studies, pertain to human subjects, or are otherwise subject to restrictions in their use or redistribution.
• Volume. Both the total volume of data and the total number of files that are expected to be collected can affect all other data management activities.
• Data and file formats. Technology changes and formats that are acceptable today may soon be obsolete. Good choices include those formats that are nonproprietary, based upon open standards, and widely adopted and preferred by the scientific community (e.g., Comma Separated Values [CSV] over Excel [.xls, xlsx]). Data are more likely to be accessible for the long term if they are uncompressed, unencrypted, and stored using standard character encodings such as UTF-16.

The precise types, sources, volume, and formats of data may not be known beforehand, depending on the nature and uniqueness of the research. In such case, the solution is to iteratively update the plan (see Rule 9 ).

Rule 3: Define How the Data Will Be Organized

Once there is an understanding of the volume and types of data to be collected, a next obvious step is to define how the data will be organized and managed. For many projects, a small number of data tables will be generated that can be effectively managed with commercial or open source spreadsheet programs like Excel and OpenOffice Calc. Larger data volumes and usage constraints may require the use of relational database management systems (RDBMS) for linked data tables like ORACLE or mySQL, or a Geographic Information System (GIS) for geospatial data layers like ArcGIS, GRASS, or QGIS.

The details about how the data will be organized and managed could fill many pages of text and, in fact, should be recorded as the project evolves. However, in drafting a DMP, it is most helpful to initially focus on the types and, possibly, names of the products that will be used. The software tools that are employed in a project should be amenable to the anticipated tasks. A spreadsheet program, for example, would be insufficient for a project in which terabytes of data are expected to be generated, and a sophisticated RDMBS may be overkill for a project in which only a few small data tables will be created. Furthermore, projects dependent upon a GIS or RDBMS may entail considerable software costs and design and programming effort that should be planned and budgeted for upfront (see Rules 9 and 10 ). Depending on sponsor requirements and space constraints, it may also be useful to specify conventions for file naming, persistent unique identifiers (e.g., Digital Object Identifiers [DOIs]), and versioning control (for both software and data products).

Rule 4: Explain How the Data Will Be Documented

Rows and columns of numbers and characters have little to no meaning unless they are documented in some fashion. Metadata—the details about what, where, when, why, and how the data were collected, processed, and interpreted—provide the information that enables data and files to be discovered, used, and properly cited. Metadata include descriptions of how data and files are named, physically structured, and stored as well as details about the experiments, analytical methods, and research context. It is generally the case that the utility and longevity of data relate directly to how complete and comprehensive the metadata are. The amount of effort devoted to creating comprehensive metadata may vary substantially based on the complexity, types, and volume of data.

A sound documentation strategy can be based on three steps. First, identify the types of information that should be captured to enable a researcher like you to discover, access, interpret, use, and cite your data. Second, determine whether there is a community-based metadata schema or standard (i.e., preferred sets of metadata elements) that can be adopted. As examples, variations of the Dublin Core Metadata Initiative Abstract Model are used for many types of data and other resources, ISO (International Organization for Standardization) 19115 is used for geospatial data, ISA-Tab file format is used for experimental metadata, and Ecological Metadata Language (EML) is used for many types of environmental data. In many cases, a specific metadata content standard will be recommended by a target data repository, archive, or domain professional organization. Third, identify software tools that can be employed to create and manage metadata content (e.g., Metavist, Morpho). In lieu of existing tools, text files (e.g., readme.txt) that include the relevant metadata can be included as headers to the data files.

A best practice is to assign a responsible person to maintain an electronic lab notebook, in which all project details are maintained. The notebook should ideally be routinely reviewed and revised by another team member, as well as duplicated (see Rules 6 and 9 ). The metadata recorded in the notebook provide the basis for the metadata that will be associated with data products that are to be stored, reused, and shared.

Rule 5: Describe How Data Quality Will Be Assured

Quality assurance and quality control (QA/QC) refer to the processes that are employed to measure, assess, and improve the quality of products (e.g., data, software, etc.). It may be necessary to follow specific QA/QC guidelines depending on the nature of a study and research sponsorship; such requirements, if they exist, are normally stated in the RFP. Regardless, it is good practice to describe the QA/QC measures that you plan to employ in your project. Such measures may encompass training activities, instrument calibration and verification tests, double-blind data entry, and statistical and visualization approaches to error detection. Simple graphical data exploration approaches (e.g., scatterplots, mapping) can be invaluable for detecting anomalies and errors.

Rule 6: Present a Sound Data Storage and Preservation Strategy

A common mistake of inexperienced (and even many experienced) researchers is to assume that their personal computer and website will live forever. They fail to routinely duplicate their data during the course of the project and do not see the benefit of archiving data in a secure location for the long term. Inevitably, though, papers get lost, hard disks crash, URLs break, and tapes and other media degrade, with the result that the data become unavailable for use by both the originators and others. Thus, data storage and preservation are central to any good data management plan. Give careful consideration to three questions:

• How long will the data be accessible?
• How will data be stored and protected over the duration of the project?
• How will data be preserved and made available for future use?

The answer to the first question depends on several factors. First, determine whether the research sponsor or your home institution have any specific requirements. Usually, all data do not need to be retained, and those that do need not be retained forever. Second, consider the intrinsic value of the data. Observations of phenomena that cannot be repeated (e.g., astronomical and environmental events) may need to be stored indefinitely. Data from easily repeatable experiments may only need to be stored for a short period. Simulations may only need to have the source code, initial conditions, and verification data stored. In addition to explaining how data will be selected for short-term storage and long-term preservation, remember to also highlight your plans for the accompanying metadata and related code and algorithms that will allow others to interpret and use the data (see Rule 4 ).

Develop a sound plan for storing and protecting data over the life of the project. A good approach is to store at least three copies in at least two geographically distributed locations (e.g., original location such as a desktop computer, an external hard drive, and one or more remote sites) and to adopt a regular schedule for duplicating the data (i.e., backup). Remote locations may include an offsite collaborator’s laboratory, an institutional repository (e.g., your departmental, university, or organization’s repository if located in a different building), or a commercial service, such as those offered by Amazon, Dropbox, Google, and Microsoft. The backup schedule should also include testing to ensure that stored data files can be retrieved.

Your best bet for being able to access the data 20 years beyond the life of the project will likely require a more robust solution (i.e., question 3 above). Seek advice from colleagues and librarians to identify an appropriate data repository for your research domain. Many disciplines maintain specific repositories such as GenBank for nucleotide sequence data and the Protein Data Bank for protein sequences. Likewise, many universities and organizations also host institutional repositories, and there are numerous general science data repositories such as Dryad ( http://datadryad.org/ ), figshare ( http://figshare.com/ ), and Zenodo ( http://zenodo.org/ ). Alternatively, one can easily search for discipline-specific and general-use repositories via online catalogs such as http://www.re3data.org/ (i.e., REgistry of REsearch data REpositories) and http://www.biosharing.org (i.e., BioSharing). It is often considered good practice to deposit code in a host repository like GitHub that specializes in source code management as well as some types of data like large files and tabular data (see https://github.com/ ). Make note of any repository-specific policies (e.g., data privacy and security, requirements to submit associated code) and costs for data submission, curation, and backup that should be included in the DMP and the proposal budget.

Rule 7: Define the Project’s Data Policies

Despite what may be a natural proclivity to avoid policy and legal matters, researchers cannot afford to do so when it comes to data. Research sponsors, institutions that host research, and scientists all have a role in and obligation for promoting responsible and ethical behavior. Consequently, many research sponsors require that DMPs include explicit policy statements about how data will be managed and shared. Such policies include:

• licensing or sharing arrangements that pertain to the use of preexisting materials;
• plans for retaining, licensing, sharing, and embargoing (i.e., limiting use by others for a period of time) data, code, and other materials; and
• legal and ethical restrictions on access and use of human subject and other sensitive data.

Unfortunately, policies and laws often appear or are, in fact, confusing or contradictory. Furthermore, policies that apply within a single organization or in a given country may not apply elsewhere. When in doubt, consult your institution’s office of sponsored research, the relevant Institutional Review Board, or the program officer(s) assigned to the program to which you are applying for support.

Despite these caveats, it is usually possible to develop a sound policy by following a few simple steps. First, if preexisting materials, such as data and code, are being used, identify and include a description of the relevant licensing and sharing arrangements in your DMP. Explain how third party software or libraries are used in the creation and release of new software. Note that proprietary and intellectual property rights (IPR) laws and export control regulations may limit the extent to which code and software can be shared.

Second, explain how and when the data and other research products will be made available. Be sure to explain any embargo periods or delays such as publication or patent reasons. A common practice is to make data broadly available at the time of publication, or in the case of graduate students, at the time the graduate degree is awarded. Whenever possible, apply standard rights waivers or licenses, such as those established by Open Data Commons (ODC) and Creative Commons (CC), that guide subsequent use of data and other intellectual products (see http://creativecommons.org/ and http://opendatacommons.org/licenses/pddl/summary/ ). The CC0 license and the ODC Public Domain Dedication and License, for example, promote unrestricted sharing and data use. Nonstandard licenses and waivers can be a significant barrier to reuse.

Third, explain how human subject and other sensitive data will be treated (e.g., see http://privacyruleandresearch.nih.gov/ for information pertaining to human health research regulations set forth in the US Health Insurance Portability and Accountability Act). Many research sponsors require that investigators engaged in human subject research approaches seek or receive prior approval from the appropriate Institutional Review Board before a grant proposal is submitted and, certainly, receive approval before the actual research is undertaken. Approvals may require that informed consent be granted, that data are anonymized, or that use is restricted in some fashion.

Rule 8: Describe How the Data Will Be Disseminated

The best-laid preservation plans and data sharing policies do not necessarily mean that a project’s data will see the light of day. Reviewers and research sponsors will be reassured that this will not be the case if you have spelled out how and when the data products will be disseminated to others, especially people outside your research group. There are passive and active ways to disseminate data. Passive approaches include posting data on a project or personal website or mailing or emailing data upon request, although the latter can be problematic when dealing with large data and bandwidth constraints. More active, robust, and preferred approaches include: (1) publishing the data in an open repository or archive (see Rule 6 ); (2) submitting the data (or subsets thereof) as appendices or supplements to journal articles, such as is commonly done with the PLOS family of journals; and (3) publishing the data, metadata, and relevant code as a “data paper” [ 5 ]. Data papers can be published in various journals, including Scientific Data (from Nature Publishing Group), the GeoScience Data Journal (a Wiley publication on behalf of the Royal Meteorological Society), and GigaScience (a joint BioMed Central and Springer publication that supports big data from many biology and life science disciplines).

A good dissemination plan includes a few concise statements. State when, how, and what data products will be made available. Generally, making data available to the greatest extent and with the fewest possible restrictions at the time of publication or project completion is encouraged. The more proactive approaches described above are greatly preferred over mailing or emailing data and will likely save significant time and money in the long run, as the data curation and sharing will be supported by the appropriate journals and repositories or archives. Furthermore, many journals and repositories provide guidelines and mechanisms for how others can appropriately cite your data, including digital object identifiers, and recommended citation formats; this helps ensure that you receive credit for the data products you create. Keep in mind that the data will be more usable and interpretable by you and others if the data are disseminated using standard, nonproprietary approaches and if the data are accompanied by metadata and associated code that is used for data processing.

Rule 9: Assign Roles and Responsibilities

A comprehensive DMP clearly articulates the roles and responsibilities of every named individual and organization associated with the project. Roles may include data collection, data entry, QA/QC, metadata creation and management, backup, data preparation and submission to an archive, and systems administration. Consider time allocations and levels of expertise needed by staff. For small to medium size projects, a single student or postdoctoral associate who is collecting and processing the data may easily assume most or all of the data management tasks. In contrast, large, multi-investigator projects may benefit from having a dedicated staff person(s) assigned to data management.

Treat your DMP as a living document and revisit it frequently (e.g., quarterly basis). Assign a project team member to revise the plan, reflecting any new changes in protocols and policies. It is good practice to track any changes in a revision history that lists the dates that any changes were made to the plan along with the details about those changes, including who made them.

Reviewers and sponsors may be especially interested in knowing how adherence to the data management plan will be assessed and demonstrated, as well as how, and by whom, data will be managed and made available after the project concludes. With respect to the latter, it is often sufficient to include a pointer to the policies and procedures that are followed by the repository where you plan to deposit your data. Be sure to note any contributions by nonproject staff, such as any repository, systems administration, backup, training, or high-performance computing support provided by your institution.

Rule 10: Prepare a Realistic Budget

Creating, managing, publishing, and sharing high-quality data is as much a part of the 21st century research enterprise as is publishing the results. Data management is not new—rather, it is something that all researchers already do. Nonetheless, a common mistake in developing a DMP is forgetting to budget for the activities. Data management takes time and costs money in terms of software, hardware, and personnel. Review your plan and make sure that there are lines in the budget to support the people that manage the data (see Rule 9 ) as well as pay for the requisite hardware, software, and services. Check with the preferred data repository (see Rule 6 ) so that requisite fees and services are budgeted appropriately. As space allows, facilitate reviewers by pointing to specific lines or sections in the budget and budget justification pages. Experienced reviewers will be on the lookout for unfunded components, but they will also recognize that greater or lesser investments in data management depend upon the nature of the research and the types of data.

A data management plan should provide you and others with an easy-to-follow road map that will guide and explain how data are treated throughout the life of the project and after the project is completed. The ten simple rules presented here are designed to aid you in writing a good plan that is logical and comprehensive, that will pass muster with reviewers and research sponsors, and that you can put into practice should your project be funded. A DMP provides a vehicle for conveying information to and setting expectations for your project team during both the proposal and project planning stages, as well as during project team meetings later, when the project is underway. That said, no plan is perfect. Plans do become better through use. The best plans are “living documents” that are periodically reviewed and revised as necessary according to needs and any changes in protocols (e.g., metadata, QA/QC, storage), policy, technology, and staff, as well as reused, in that the most successful parts of the plan are incorporated into subsequent projects. A public, machine-readable, and openly licensed DMP is much more likely to be incorporated into future projects and to have higher impact; such increased transparency in the research funding process (e.g., publication of proposals and DMPs) can assist researchers and sponsors in discovering data and potential collaborators, educating about data management, and monitoring policy compliance [ 6 ].

Acknowledgments

This article is the outcome of a series of training workshops provided for new faculty, postdoctoral associates, and graduate students.

Funding Statement

This work was supported by NSF IIA-1301346, IIA-1329470, and ACI-1430508 ( http://nsf.gov ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

MIT Libraries logo MIT Libraries

Write a data management plan

A data management plan (DMP) will help you manage your data, meet funder requirements, and help others use your data if shared.

Alternatively, you can use the questions below and any specific data management requirements from your funding agency to write your data management plan. Additional resources for creating plans  are also provided below.

• What’s the purpose of the research?
• What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modeling data?
• How much data will be generated for this research?
• How long will the data be collected and how often will it change?
• Are you using data that someone else produced? If so, where is it from?
• Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
• What documentation will you be creating in order to make the data understandable by other researchers?
• Are you using metadata that is standard to your field? How will the metadata be managed and stored?
• What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
• Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
• What directory and file naming convention will be used?
• What are your local storage and backup procedures ? Will this data require secure storage?
• What tools or software are required to read or view the data?
• Who has the right to manage this data? Is it the responsibility of the PI, student, lab, MIT, or funding agency?
• What data will be shared , when, and how?
• Does sharing the data raise privacy, ethical, or confidentiality concerns ?  Do you have a plan to protect or anonymize data, if needed?
• Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
• Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant? The Technology Licensing Office (TLO) can provide this information.
• Will this research be published in a journal that requires the underlying data to accompany articles?
• Will there be any embargoes on the data?
• Will you permit re-use , redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?
• How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
• Is a discipline-specific repository available? If not, consider depositing your data into a generalist data repository . Email us at [email protected] if you’re interested in discussing repository options for your data.
• How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
• Are software or tools needed to use the data? Will these be archived?
• How long should the data be retained? 3-5 years, 10 years, or forever?

Additional resources for creating plans

• Managing your data – Project Start & End Checklists (MIT Data Management Services) : Checklist (PDF) with detailed resources to help researchers set up and maintain robust data management practices for the full life of a project.
• ezDMP : a free web-based tool for creating DMPs specific to a subset of NSF funding requirements.
• Guidelines for Effective Data Management Plans and Data Management Plan Resources and Examples (ICPSR) : Framework for creating a plan and links to examples of data management plans in various scientific disciplines
• Example Plans (University of Minnesota)
• NSF (by the DART project) : assessment rubric and guidance
• NIH (by FASEB)

See other guides to data management for additional guidance on managing data and select information related to particular formats or disciplines.

Table of Contents

What is data management, quantifying data management principles, data management best practices, data management processes and plans, what is a data management strategy, data management platforms and programs, what about data modeling, what does big data have to do with data management, choose the right program, would you like to take a data management course, what is data management and why is it important.

In the 21st century, data is everything. With massive volumes of it generated every day, it stands to reason that we need to have better data management solutions available. Any business or organization that wants to succeed today need to understand the what, why, and how of data management.

Fortunately, there are lots of resources available, from data management software to data management best practices, and everything in between. Let's begin with learning what is data management.

The  Data Management Association  or DAMA, defines data management as "the development of architectures, policies, practices, and procedures to manage the data lifecycle."

To put it in simpler, everyday terms, data management is the process of collecting , keeping, and using data in a cost-effective, secure, and efficient manner. Data management helps people, organizations, and connected things optimize data usage to make better-informed decisions that yield maximum benefit.

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There is a handful of guiding principles involved in data management. Some of them may have higher weight than others, depending on the organization involved and the type of data they work with. The principles are:

• Creating, accessing, and regularly updating data across diverse data tiers
• Storing data both on-premises and across multiple clouds
• Providing both high availability and rapid disaster recovery
• Using data in an increasing number of algorithms, analytics, and applications
• Ensuring effective data privacy and data security
• Archiving and destroying data in compliance with established retention schedules and compliance guidelines

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Data scientists face many challenges when setting up a successful, viable data management system. These best practices offer ways to address those obstacles and make it easier to implement an effective data management system.

• Identify your data by creating a discovery layer.  Putting a discovery layer over your organization's data tiers enables data scientists and analysts to search and browse for useful datasets.
• Develop a data science environment to repurpose your data more efficiently.  Data science environments automate a significant amount of activities. This practice brings in tools that remove the need for manual data transformation, making it easier to conduct testing.
• Maintain performance levels across your growing datasets by using autonomous technology.  Bring in AI and machine learning methods to continuously monitor database queries and optimize indexes when those queries change. This practice maintains rapid performance and eliminates the need to perform time-consuming manual tasks.
• Stay ahead of compliance requirements by using discovery.  Compliance demands are always increasing, so it's smart to use new data discovery tools to review data, including detecting, tracking, and monitoring your data wherever it resides.
• Manage and integrate multiple data storage platforms with a common query layer.  By employing a standard query layer that spans the many kinds of data storage, you can access data centrally no matter where it resides or what format it is in.

We can also break down data management into five distinct processes. Not every organization uses each method. Like the principles, it depends on the business or organization in question:

Cloud Data Management

Data analytics and visualization, etl and data integration, master data management, reference data management.

Alternately, data management can be understood as a combination of any of these disciplines:

• Business Intelligence and Analytics
• Data Architecture
• Data Governance and Data Stewardship
• Data Integration
• Data Modeling
• Data Quality
• Data Security
• Data Warehousing
• Data Storage and Big Data
• Document and Content Management
• Master and Reference Data Management
• Metadata Management

Since data is so huge today, organizations need a sound data management strategy that works with the massive amounts being generated. Three critical components of a good data management strategy include:

Data Delivery

Data governance, data operations.

These three practices taken together will result in better data quality, more robust data security, and a better quality of data-driven insights for making more informed business decisions.

There are several different data management systems available, including:

• Document databases
• ER model databases
• Graph databases
• Hierarchical databases
• Network databases
• NoSQL databases
• Object-oriented databases
• Relational database

Data management platforms and data management programs are two indispensable management tools.

Data management platforms, also called DMP, are platforms that valuable store data like customer data (e.g., mobile identifiers, cookie IDs, etc.), and campaign data. DMPs help advertisers and marketing professionals build customer segments. The segments grow based on demographics, browsing history, geographical location, device type used, and other factors.

Here is a list of some popular DMPs:

• Salesforce DMP
• SAS Data Management

And here are some of the better data management programs available today:

• Matillion: Facilitates cloud data warehouse operations such as loading and transforming data
• SolarWinds Backup: Ideal for backup and recovery
• Panoply: A cloud data management tool that collects, sorts, combines, stores, and optimizes data without the need for data coding or modeling
• Segment: Collects data from the web and mobile apps and makes the information readily available to your teams
• Tableau: Analyzes big data and quickly translates it into actionable insights. Ideal for analytics and visualization
• Collibra: Automates workflows deliver user-friendly code, compares data from different parts of your business, and perform accurate data mapping.
• Dell Boomi: A master data management tool that enables data stewarding defines models governs data and deploy data models.
• Data From: A SQL-based data transformation platform that manages cloud data warehouses' processes. It runs updating schedules to keep data current and ensure data reliability by creating data quality tests.
• Stitch Data: A cloud-based ETL platform that's pre-integrated with dozens of data sources, facilitating the movement of data. It features error handling and alerting, easy scheduling, and automatic scaling.
• Amazon Web Services: This well-known cloud provider offers a growing set of tools ideal for cloud data management.
• Microsoft Azure: Another well-known cloud provider that offers cloud data management system tools and analytics.
• Talend: An open-source data integration tool that enables users to cleanse, integrate, mask, and profile data, complete with MDM functionality and the ability to manage many source systems via a strong GUI.

Data modeling is the practice of determining through extensive data analysis what is necessary to align business objectives with the information systems the business runs on. A  data modeler  documents complex software systems in easily understood diagrams for the benefit of non-technical people. These conceptual diagrams represent datasets and workflows in visual form and map them to the relevant line of business requirements and goals.

Common data modeling techniques include entity-relationship diagrams, data mappings, and schemas. Note that data models must be updated whenever the organization brings in new data sources, or regular-ass updates occur, so this process is ongoing.

In a word, everything! Big data , by its very nature, begs for a robust data management system. An efficient data management system takes big data and turns it into actionable items. It's a competitive world out there, and the businesses that stay ahead of the pack are the ones that make the best decisions, and the right information, in turn, creates the best decisions.

The above line of logic shows the importance of data in decision making , and the best way to achieve this is an alliance between big data and the right data management strategy.

If you're considering a career in data science, Simplilearn offers courses that equip you with the essential skills and expertise to thrive in this dynamic field. To help you choose the right course, we have provided a detailed comparison for your reference:

Program Name Data Scientist Master's Program Post Graduate Program In Data Science Post Graduate Program In Data Science Geo All Geos All Geos Not Applicable in US University Simplilearn Purdue Caltech Course Duration 11 Months 11 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more 8+ skills including Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more 8+ skills including Supervised & Unsupervised Learning Deep Learning Data Visualization, and more Additional Benefits Applied Learning via Capstone and 25+ Data Science Projects Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance Upto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program

If data modeling appeals to you and a career in the field piques your interest, then Simplilearn can help you get started. The Caltech Post Graduate Program in Data Science , presented in collaboration with Caltech CTME, provides you with training by an industry expert on the most up-to-date Data Science and Machine learning skills. You will gain hands-on exposure to key technologies, including R, SAS, Python, Tableau, Hadoop, and Spark.

According to Glassdoor , a data scientist earns an annual average of $113,309. The digital world has an increasing need for data scientists, and compensation is undoubtedly an attractive incentive! Check out Simplilearn today, and get that new career going.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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• Directories
• Prepping: Writing a Data Management Plan
• Prepping: Ethical Considerations
• Implementing: Organizing and Format
• Implementing: Metadata and Documenting Your Process
• Implementing: Storing and Securing Your Data
• Wrapping Up: Preparing Your Data for Sharing
• Wrapping Up: Publishing and Sharing Your Data
• Wrapping Up: Preserving & Archiving Your Data
• Scholarly Publishing and Open Access
• Start Your Research
• Research Guides
• University of Washington Libraries
• Library Guides
• UW Libraries
• Research Data Management

Research Data Management: Prepping: Writing a Data Management Plan

What will i find in this guide.

Jump to the topic:

• How do I write a data management plan? Using DMPTool to create funder-compliant data management plans

Why should I plan for data management?

What goes into a data management plan, how do i write a data management plan.

Data Management Plans (DMPs) are written documents that detail how data will be handled throughout its lifecycle. They outline how data will be collected, stored, processed, analyzed, shared, protected, and preserved, while addressing compliance with relevant legal, ethical, and institutional requirements.

DMPTool is an online platform designed to help researchers create and manage data management plans. It offers templates from funders like the NIH and NSF, guidance and best practices, and resources tailored to specific funding agencies and research institutions. Additionally, DMPTool allows researchers to collaborate on DMPs, enabling multiple team members to contribute to the plan.

You can create a DMPTool account using your UW NetID. Learn more about DMPTool and how to use it in our DMPTool guide .

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Research data management (RDM) is a foundational aspect of responsible research practices. It promotes the integrity, reproducibility, and usability of research data. Planning for data management involves making several ethical, legal, and practical decisions  before  you begin your research project. Tackling these questions before you begin your research streamlines the work, protects your data, and is often required by common funding institutions and organizations.

Federally funded research generally requires you to write a DMP. The following list contains common federal funders and their DMP policies:

• Department of Defense
• Department of Energy

If your funder is not on this list, make sure you familiarize yourself with their policies around data management plans.  SPARC provides an up-to-date listing of data sharing policies by funding organization.

While research data management plan can differ, they typically includes similar key components that cover the entire lifecycle of research data. Subsequent pages in this guide will take a deeper look at some of the following components of data management plans.

• Data Description:  A detailed description of the types of data that will be collected or generated during the research project. This can include formats, types (e.g., qualitative, quantitative), volumes, and any associated metadata.
• Roles and Responsibilities :  A clear delineation of roles within the research team regarding data management. This should indicate who is responsible for data collection, storage, sharing, backup, and other critical tasks.
• Data Collection Methods:  An explanation of how the data will be collected, including instruments or software used, data sources, and any standard protocols followed during data collection.
• Data Documentation and Metadata :  Information on how data will be documented to ensure usability and reproducibility. This can include metadata standards, data dictionaries, and other documentation practices to describe and contextualize the data.
• Data Storage  and Security:   Information about where and how the data will be stored, both during and after the project.   Your plan should also include how you plan to keep your data secure.
• Data Organization and Format : Information about file naming conventions, folder structures, file formats, etc.
• Compliance with Legal and Ethical Requirements:  Details on compliance with relevant laws, regulations, and ethical guidelines. This can include issues like informed consent, data anonymization, HIPAA, or other applicable rules. Read about ethical considerations in data management here ,
• Data Publishing and Sharing :  Information on whether and how data will be shared with others, including plans for data repositories, access policies, and any embargo periods before data becomes publicly available. This section might also address data licensing and intellectual property rights. Read about where you can deposit your data in our Data Publishing and Sharing guide .
• Data Preservation and Archiving :  Plans for long-term data preservation, including which data will be archived, where it will be stored, and for how long.

Tools & Resources

• DMPTool An online tool to assist in writing data management plans for NSF, NIH, NEH, IMLS, or GBMF. Login using your NetID.
• SPARC SPARC provides an up-to-date listing of data sharing policies by funding organization.

If you have questions about writing a data management plan or would like to  request a consultation  with a member of the Scholarly Communications and Publishing Team, please email  [email protected] .

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• Last Updated: May 30, 2024 3:26 PM
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Data management topics

Data Management, Defined

Data management systems today, big data management systems, data management challenges, data management best practices, data management evolves, what is data management.

Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management is to help people, organizations, and connected things optimize the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization. A robust data management strategy is becoming more important than ever as organizations increasingly rely on intangible assets to create value.

Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices. The work of data management has a wide scope, covering factors such as how to:

• Create, access, and update data across a diverse data tier
• Store data across multiple clouds and on premises
• Provide high availability and disaster recovery
• Use data in a growing variety of apps, analytics, and algorithms
• Ensure data privacy and security
• Archive and destroy data in accordance with retention schedules and compliance requirements

A formal data management strategy addresses the activity of users and administrators, the capabilities of data management technologies, the demands of regulatory requirements, and the needs of the organization to obtain value from its data.

Data Capital Is Business Capital

In today’s digital economy, data is a kind of capital, an economic factor of production in digital goods and services. Just as an automaker can’t manufacture a new model if it lacks the necessary financial capital, it can’t make its cars autonomous if it lacks the data to feed the onboard algorithms. This new role for data has implications for competitive strategy as well as for the future of computing.

Given this central and mission-critical role of data, strong management practices and a robust management system are essential for every organization, regardless of size or type.

Today’s organizations need a data management solution that provides an efficient way to manage data across a diverse but unified data tier. Data management systems are built on data management platforms and can include databases , data lakes and data warehouses , big data management systems, data analytics, and more.

All these components work together as a “data utility” to deliver the data management capabilities an organization needs for its apps, and the analytics and algorithms that use the data originated by those apps. Although current tools help database administrators (DBAs) automate many of the traditional management tasks, manual intervention is still often required because of the size and complexity of most database deployments. Whenever manual intervention is required, the chance for errors increases. Reducing the need for manual data management is a key objective of a new data management technology, the autonomous database .

Data Management Platforms

The most critical step for continuous delivery of software is continuous integration (CI). CI is a development practice where developers commit their code changes (usually small and incremental) to a centralized source repository, which kicks off a set of automated builds and tests. This repository allows developers to capture the bugs early and automatically before passing them on to production. Continuous Integration pipeline usually involves a series of steps, starting from code commit to performing basic automated linting/static analysis, capturing dependencies, and finally building the software and performing some basic unit tests before creating a build artifact. Source code management systems like Github, Gitlab, etc., offer webhooks integration to which CI tools like Jenkins can subscribe to start running automated builds and tests after each code check-in.

A data management platform is the foundational system for collecting and analyzing large volumes of data across an organization. Commercial data platforms typically include software tools for management, developed by the database vendor or by third-party vendors. These data management solutions help IT teams and DBAs perform typical tasks such as:

• Identifying, alerting, diagnosing, and resolving faults in the database system or underlying infrastructure
• Allocating database memory and storage resources
• Making changes in the database design
• Optimizing responses to database queries for faster application performance

The increasingly popular cloud database platforms allow businesses to scale up or down quickly and cost-effectively. Some are available as a service, allowing organizations to save even more.

What is an Autonomous Database

Based in the cloud, an autonomous database uses artificial intelligence (AI) and machine learning to automate many data management tasks performed by DBAs, including managing database backups, security, and performance tuning.

Also called a self-driving database , an autonomous database offers significant benefits for data management, including:

• Reduced complexity
• Decreased potential for human error
• Higher database reliability and security
• Improved operational efficiency
• Lower costs

The increasingly popular cloud data platforms allow businesses to scale up or down quickly and cost-effectively. Some are available as a service, allowing organizations to save even more.

In some ways, big data is just what it sounds like—lots and lots of data. But big data also comes in a wider variety of forms than traditional data, and it’s collected at a high rate of speed. Think of all the data that comes in every day, or every minute, from a social media source such as Facebook. The amount, variety, and speed of that data are what make it so valuable to businesses, but they also make it very complex to manage.

As more and more data is collected from sources as disparate as video cameras, social media, audio recordings, and Internet of Things (IoT) devices, big data management systems have emerged. These systems specialize in three general areas.

• Big data integration brings in different types of data—from batch to streaming—and transforms it so that it can be consumed.
• Big data management stores and processes data in a data lake or data warehouse efficiently, securely, and reliably, often by using object storage.
• Big data analysis uncovers new insights with analytics, including graph analytics , and uses machine learning and AI visualization to build models.

Companies are using big data to improve and accelerate product development, predictive maintenance, the customer experience, security, operational efficiency, and much more. As big data gets bigger, so will the opportunities.

Most of the challenges in data management today stem from the faster pace of business and the increasing proliferation of data. The ever-expanding variety, velocity, and volume of data available to organizations is pushing them to seek more-effective management tools to keep up. Some of the top challenges organizations face include the following:

Data Management Principles and Data Privacy

The General Data Protection Regulation (GDPR) enacted by the European Union and implemented in May 2018 includes seven key principles for the management and processing of personal data. These principles include lawfulness, fairness, and transparency; purpose limitation; accuracy; storage limitation; integrity and confidentiality; and more.

The GDPR and other laws that follow in its footsteps, such as the California Consumer Privacy Act (CCPA), are changing the face of data management. These requirements provide standardized data protection laws that give individuals control over their personal data and how it is used. In effect, it turns consumers into data stakeholders with real legal recourse when organizations fail to obtain informed consent at data capture, exercise poor control over data use or locality, or fail to comply with data erasure or portability requirements.

Addressing data management challenges requires a comprehensive, well-thought-out set of best practices. Although specific best practices vary depending on the type of data involved and the industry, the following best practices address the major data management challenges organizations face today:

The Value of a Data Science Environment

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract value from data. Data scientists combine a range of skills—including statistics, computer science, and business knowledge—to analyze data collected from the web, smartphones, customers, sensors, and other sources.

With data’s new role as business capital, organizations are discovering what digital startups and disruptors already know: Data is a valuable asset for identifying trends, making decisions, and taking action before competitors. The new position of data in the value chain is leading organizations to actively seek better ways to derive value from this new capital.

Learn more about what the best data management can do for you, including the benefits of an autonomous strategy in the cloud and scalable, high performance database cloud capabilities .

Data management related products

• Oracle Autonomous Database World’s first self-driving database
• Oracle Database World's leading converged, multi-model database management system
• Oracle Exadata Unmatched Oracle Database performance, scale, and availability
• Oracle Autonomous Data Warehouse Data warehousing without complexity

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50+ Amazing DBMS Project Ideas For Beginners To Advance Level Students

Are you looking to delve into the world of Database Management Systems (DBMS) and explore its myriad possibilities? In our blog on DBMS project ideas, we unravel the fascinating realm of DBMS and its importance in skill development. As data continues to be the lifeblood of modern enterprises, mastering DBMS is an invaluable asset.

We’ve curated over 50 DBMS project ideas, catering to beginners, intermediate learners, and advanced students, ensuring that there’s something for everyone. In the first section, we present 17+ easy DBMS project ideas perfect for beginners, followed by 17+ intriguing projects for intermediate-level students. For the advanced enthusiasts, we’ve got 17+ stunning DBMS project ideas that will truly challenge your skills.

Additionally, we’ll provide tips on how to select the right DBMS project to suit your learning goals. Stay tuned with us to explore the world of DBMS project ideas and embark on a journey of database mastery.

What is a DBMS?

Table of Contents

A Database Management System (DBMS) is like a digital organizer for storing and managing information. It’s a special computer software that helps people keep data in a structured way, so it’s easy to find and use. Think of it as a virtual file cabinet that stores things like names, numbers, and other important stuff.

The DBMS lets you add, change, and search for information without the need to understand the technical details. It’s like having a librarian who arranges books on shelves, so you don’t have to go hunting through a messy pile to find what you need. In a nutshell, a DBMS is a tool that makes handling data simpler and more organized, like a digital secretary for your information.

Importance of DBMS projects In Skill Development

Here are some importance of DBMS projects in skill development: 

1. Learning Data Organization

DBMS projects are crucial for skill development because they teach you how to organize and structure data. When you work on these projects, you learn how to arrange information in a systematic and efficient way. This skill is valuable in many professions where handling data is essential, such as business, research, and computer science.

2. Problem-Solving Skills

DBMS projects require problem-solving abilities. You need to figure out how to design databases, make them work smoothly, and troubleshoot issues. These problem-solving experiences help you develop critical thinking skills, which are handy not only in database management but also in various aspects of life.

3. Real-World Application

Working on DBMS projects gives you practical experience. You get to apply what you learn in a real-world context. This hands-on practice is an effective way to understand the concepts and skills you’re developing. It’s like learning to ride a bike by actually riding one, not just reading about it.

4. Collaboration and Communication

DBMS projects often involve teamwork. You’ll need to communicate and collaborate with others to design, implement, and maintain databases. This fosters your ability to work with a team, exchange ideas, and convey your thoughts effectively, which are valuable skills in any career.

5. Career Opportunities

Developing DBMS skills through projects can open up career opportunities. Many businesses rely on databases to store and manage their information, so having these skills can make you more attractive to employers. A strong background in DBMS is helpful for your job growth whether you want to work in IT, data analysis, or management.

50+ DBMS Project Ideas For Beginners To Advance Level Students

In this section we will discuss DBMS project ideas for beginners to advance level students: 

I. 17+ Easy DBMS Project Ideas For Beginners – Level

1. student information system.

Develop a database system to manage student records, including personal details, course registrations, and grades. This project will help you understand data modeling and CRUD operations in a DBMS.

Skills Required

• SQL for database operations.
• Database design and normalization.
• Basic user interface development.

2. Library Management System

Create a system for tracking books, patrons, and borrowing history in a library. This project will involve database design and implementing search and borrowing functionalities.

• Basic front-end development for user interface.

3. Employee Attendance Tracker

Build a system that records and manages employee attendance. This project will help you learn about data capture and management in a business context.

• Basic web development for data input.

4. Inventory Management System

Design a database to keep track of products, sales, and stock levels for a small business. This project will provide insight into inventory control and reporting.

• Basic front-end development for data visualization.

5. Task Management Application

Make a system for managing jobs that lets people add, change, and mark as finished tasks. This project will help you learn more about how to change data and connect with users.

• Web development for task management interface.

6. Online Bookstore

Develop an online bookstore where users can search for, view, and purchase books. This project will teach you e-commerce fundamentals and user experience design.

• Web development for the e-commerce platform.

7. Hospital Information System

Design a system to manage patient records, appointments, and medical history for a clinic or hospital. This project will involve complex data relationships and user access control.

• Security principles for patient data protection.

8. Blogging Platform

Create a blogging platform where users can write and publish articles. This project will enhance your knowledge of content management and user interaction.

• Web development for the blogging platform.

9. Budget Tracking Application

Build a budget tracker that allows users to input and manage their expenses and income. This project will help you understand financial data management.

• Web development for the budget tracking interface.

10. Music Library Organizer

Develop a system to organize and manage music collections. This project will involve metadata management and search functionality.

• Basic front-end development for music library interface.

11. Restaurant Reservation System

Create a system for making and managing restaurant reservations. This project will involve table management and reservation scheduling.

• Web development for the reservation system.

12. Event Management Database

Design a database for managing event details, attendees, and scheduling. This project will teach you event planning and data organization.

• Basic web development for event management.

13. Job Portal

Build a job portal where users can search for and apply to job listings. This project will enhance your understanding of job data management.

• Web development for the job portal.

14. Movie Database

Create a movie database with information on films, actors, and reviews. This project will involve data integration and user-driven content.

• Basic front-end development for the movie database.

15. Online Quiz System

Design an online quiz system where users can take quizzes on various topics. This project will teach you about quiz creation and user performance tracking.

• Web development for the quiz system.

16. Customer Relationship Management (CRM) System

Develop a CRM system for businesses to manage customer interactions and data. This project will involve user accounts, lead tracking, and customer communication.

• Web development for the CRM interface.

17. Real Estate Management System

Create a database to manage property listings, agents, and customer inquiries in the real estate industry. This project will involve complex data relationships and property search functionality.

• Web development for the real estate management system.

18. Online Auction Platform

Design an online auction platform where users can list items for bidding. This project will teach you about online auctions and real-time data updates.

• Web development for the auction platform.

II. 17+ Interesting DBMS Project Ideas For Intermediate – Level

Here are 17+ Interesting DBMS Project Ideas For Intermediate – Level: 

1. Human Resources Management System

Develop a comprehensive HR system that manages employee records, payroll, benefits, and attendance. This project will give you experience in complex database design and HR processes.

• SQL for complex database operations.
• Database design, normalization, and optimization.
• Web development for HR interface.

2. Hospital Management System

Design a sophisticated system for hospitals, including patient records, appointment scheduling, billing, and pharmacy management. This project will challenge your data modeling and security skills.

3. Online Banking System

Create a secure online banking platform with account management, fund transfers, and transaction history. This project will provide insights into financial data management and security.

• Database design and security.
• Web development with strong security practices.

4. E-Learning Platform

Build an e-learning platform with course management, student profiles, and progress tracking. This project will help you understand e-learning database structures.

• Database design and optimization.
• Web development for e-learning features.

5. Inventory Forecasting System

Design a system that predicts inventory requirements based on historical data and market trends. This project involves data analytics and forecasting.

• SQL for database operations and data analysis.
• Database design and data modeling.
• Statistical and data analysis skills.

6. Social Media Analytics Tool

Develop a tool that tracks and analyzes social media management tools metrics for businesses. This project will give you experience in data integration and analytics.

• Data integration and data collection.
• Data analysis and visualization tools.

7. Hotel Reservation System

Create a hotel reservation system with real-time availability, pricing, and booking. This project will involve complex data relationships and booking algorithms.

8. Supply Chain Management System

Build a supply chain management system that tracks products from manufacturing to delivery. This project will involve complex data flows and logistics.

• Understanding of supply chain logistics.

9. Customer Support System

Design a customer support system with ticket management, knowledge base, and customer profiles. This project will improve your customer relationship management skills.

• Web development for customer support features.

10. Online Voting System

Create an online voting system for elections or polls. This project will challenge your understanding of secure data handling and vote tallying.

11. Project Management Tool

Develop a project management system with task tracking, team collaboration, and reporting features. This project will enhance your project planning and management skills.

• Web development for project management features.

12. Retail Analytics Dashboard

Build a data analytics dashboard for retailers to track sales, inventory, and customer behavior. This project involves data integration and visualization.

13. Flight Reservation System

Design a flight reservation system with real-time flight data, booking, and seat management. This project will involve complex data relationships and booking algorithms.

14. Online Auction Platform

Create an advanced online auction platform with real-time bidding, notifications, and user profiles. This project will challenge your real-time data updates and auction management skills.

• Web development for real-time bidding and notifications.

15. Asset Tracking System

Design a system for businesses to track their assets, such as equipment and vehicles. This project will involve complex data relationships and asset tracking algorithms.

Understanding of asset tracking and management.

16. Hotel Revenue Management System

Develop a system that optimizes hotel room pricing based on demand and market conditions. This project will involve data analysis and pricing strategies.

• Pricing strategy and data analysis.

17. Library Information System

Create an advanced library information system that manages books, patrons, reservations, and late fees. This project will involve complex data relationships and library management.

• Web development for library management features.

18. Online Food Ordering System

Design a comprehensive online food ordering system with real-time order tracking and restaurant management. This project will challenge your real-time order processing and delivery management skills.

• Web development for real-time order tracking and restaurant management.

III. 17+ Stunning DBMS Project Ideas For Advance – Level

1. big data analytics platform.

Develop a platform for processing and analyzing large-scale datasets. This project will involve distributed database systems and complex data processing.

• NoSQL database systems like Hadoop or Cassandra.
• Data modeling for scalability.
• Distributed computing and data analysis tools.

2. Healthcare Data Integration System

Create a system that takes medical data from different sources, like medical equipment and electronic health records, and analyzes it. This project will test how well you can combine facts and use what you know about healthcare.

• SQL and NoSQL for data integration.
• Data modeling for healthcare.
• Healthcare data standards and privacy regulations.

3. Blockchain-Based Voting System

Create a secure voting system using blockchain technology for transparency and security. This project involves complex data encryption and distributed ledger systems.

• Blockchain development.
• Cryptography and data security.
• Understanding of election systems.

4. Predictive Maintenance System

Build a system that predicts equipment maintenance needs based on sensor data. This project will involve data analytics and predictive modeling.

• SQL for data analysis.
• Data modeling for predictive maintenance.
• Statistical analysis and machine learning.

5. Autonomous Vehicle Data Management

Design a database system for managing data generated by autonomous vehicles, including sensor data, GPS, and vehicle status. This project will challenge your data handling and real-time processing skills.

• Real-time data processing.
• Data modeling for autonomous vehicle data.

6. AI Chatbot for Customer Support

Develop an AI-driven chatbot for handling customer support inquiries. This project will involve natural language processing and sentiment analysis.

• SQL for data management.
• Natural language processing.
• Machine learning for chatbot training.

7. Sports Analytics Platform

Create a sports analytics platform for tracking player statistics, game performance, and team strategies. This project will challenge your sports data knowledge and analytics skills.

• Data modeling for sports analytics.
• Data visualization and analytics tools.

8. Financial Portfolio Management System

Design a system for managing investment portfolios, including asset allocation, risk assessment, and performance tracking. This project will involve complex financial data and risk analysis.

• Data modeling for finance.
• Financial analysis and portfolio management.

9. Smart Home Automation System

Build a system for controlling smart home devices and managing user preferences. This project will involve IoT integration and user experience design.

• IoT device integration.
• User interface and user experience design.

10. Genome Data Analysis Platform

Create a platform for analyzing genomic data, including DNA sequencing and genetic variations. This project will challenge your understanding of bioinformatics and data analysis.

• Data modeling for genomics.
• Bioinformatics tools and data analysis.

11. Stock Market Prediction System

Develop a system that predicts stock market trends and recommends investments. This project will involve data analysis and predictive modeling for finance.

• Machine learning for stock market prediction.

12. Smart Agriculture System

Design a system for monitoring and controlling agricultural processes using sensor data and automation. This project will involve IoT integration and agriculture knowledge.

• Agricultural knowledge and data analysis.

13. Energy Management System

Create a system for monitoring and optimizing energy consumption in buildings and industrial facilities. This project will involve data analysis and energy efficiency concepts.

• Data modeling for energy management.
• Energy efficiency principles and data analysis.

14. Virtual Reality Content Management System

Develop a CMS for virtual reality content, including 3D models, textures, and interactive experiences. This project will challenge your VR content management and user interface design skills.

• VR content management.
• User interface and VR experience design.

15. Space Exploration Data System

Design a system for managing data from space exploration missions, including telemetry, imagery, and scientific data. This project will involve data handling and scientific data analysis.

• Data modeling for space exploration.
• Scientific data analysis and visualization tools.

16. AI-powered Language Translation System

Create a language translation system using AI and natural language processing. This project will involve complex data processing and language understanding.

• Machine learning for language translation.

17. Weather Prediction and Analysis System

Develop a system for weather data collection, prediction, and analysis. This project will involve data integration, meteorology, and predictive modeling.

• Data modeling for meteorology.
• Weather data analysis and predictive modeling.

18. Video Game Analytics Platform

Build an analytics platform for tracking player behavior, in-game performance, and virtual economies in video games. This project will challenge your understanding of gaming data and analytics.

• Data modeling for game analytics.
• Data visualization and gaming industry knowledge.
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Tips For Choosing The Right DBMS Projects

Here are some tips for choosing the right DBMS projects: 

Tip 1: Define Your Needs First

Before choosing a DBMS project, it’s important to know what you need. Think about the kind of data you want to store, how you’ll use it, and what features are essential. This will help you pick a project that aligns with your specific goals, ensuring you don’t waste time on something that won’t meet your needs.

Tip 2: Consider Your Skills

Your existing skills matter. If you’re just starting with databases, opt for simpler projects to build a foundation. If you’re more experienced, challenge yourself with complex tasks. By matching the project’s difficulty level with your skills, you’ll learn and progress at a comfortable pace.

Tip 3: Research Different DBMS Options

There are various DBMS options available, each with its strengths and weaknesses. Do some research to find out which one suits your project best. MySQL, PostgreSQL, and SQLite are some popular choices. Choose the one that fits your needs and is compatible with your skills and resources.

Tip 4: Plan for Scalability

Think about the future. If your project might grow over time, consider a DBMS that can scale with it. This means the database can handle more data and users as your project expands. Planning for scalability ensures your project won’t outgrow the DBMS’s capabilities.

Tip 5: Budget and Resources

Consider your budget and available resources. Some DBMS software is free and open-source, while others require licenses and can be expensive. Additionally, you’ll need hardware and support. Make sure your project aligns with your financial and resource constraints to avoid unexpected costs.

In the dynamic landscape of data management , DBMS projects emerge as indispensable tools for honing crucial skills. This blog has illuminated the essence of Database Management Systems (DBMS), elucidating their role as digital organizers for structured data. It has underscored the significance of DBMS projects in fostering problem-solving abilities, real-world application, and teamwork skills. 

Additionally, the comprehensive compilation of 50+ DBMS project ideas, tailored for beginners to advanced-level students, serves as a wellspring of inspiration and practical learning. Empowered with the knowledge of selecting the right DBMS projects based on individual needs and skills, readers are equipped to embark on a transformative journey of skill development and data management proficiency.

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Master Data Management: What It Is and Why It Matters

What is Master Data Management (MDM)?

Master data management (MDM) involves creating a single master record for each person, place, or thing in a business, from across internal and external data sources and applications. This information has been de-duplicated, reconciled and enriched, becoming a consistent, reliable source. Once created, this master data serves as a trusted view of business-critical data that can be managed and shared across the business to promote accurate reporting, reduce data errors, remove redundancy, and help workers make better-informed business decisions.

What is the difference between Master Data Management (MDM) technology and MDM as a discipline?

As a discipline, MDM relies heavily on the principles of  data governance  with the goal of creating a trusted and authoritative view of a company’s data. Data governance and MDM have become critical to successful business practices as organizations put increasing importance on data-driven decisions in today’s global marketplace —and as a growing number of systems contribute digital records of the people, places, and things that matter most to a business.

As a technology, MDM solutions automate how business-critical data is governed, managed, and shared throughout applications used by lines of business, brands, departments, and organizations. MDM applies  data integration , reconciliation, enrichment,  quality , and governance to create master records. Automation and artificial intelligence (AI) are used to identify, match, and merge data across the systems that hold it, and then the clean data is shared with the applications, systems, and analytics that need it. In merging records, MDM can also correct for inconsistencies in records, capture where the data came from, and create an audit trail of changes. Providing transparency within a trusted framework offers visibility into how each master record is created or modified.

What is a master record?

Master data management creates a master record (also known as a “ golden record ” or “best version of the truth”) that contains the essential information upon which a business or organization relies. The master record contains what an organization needs to know about critical “things”—a customer, location, product, supplier, and so on—to facilitate a task or action such as a marketing campaign, a service call, or a sales conversation.

One easily understood type of master data is reference data. Reference data is a subset of master data. Some examples of  reference data  are:

• Latitude and longitude
• Zip codes and area codes
• Three-letter airport codes used by airlines
• Healthcare codes (for example, ICD-10) used between organizations to understand the care provided

What do I need to know about Master Data Management (MDM)?

MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference. Organizations using MDM enjoy peace of mind that data throughout the enterprise is accurate, up-to-date, and consistent.

The categories into which master data is classified are called domains. Common MDM domains include:

• Customer master data management—both business-to-business (B2B) and business-to-consumer (B2C)
• Product master data management
• Supplier master data management
• Reference data master data management
• Location master data management
• Asset master data management
• Employee data master data management

But you can also master more specific elements like account, patient, provider, beneficiary, contract, claims, projects, movie, character, airports, aircraft, vehicles, sites, and more. It all depends on the business challenges with which you want to align your data.

Why do I need Master Data Management (MDM)?

Having multiple sources of information is a widespread problem, especially in large organizations, and the associated costs can be very high. Because data changes over time, it’s easy for it to get out of sync and become fragmented, incomplete, inaccurate, and inconsistent. As it degrades, the people that use it lose trust in it. Consider the impact on a sales call if the account manager accesses customer information that is incomplete or inaccurate:

• Is the location the right one, or has the customer’s address changed?
• How confident is the account manager in knowing which products the customer owns and uses?
• Are there any open service items?

The wrong answer to any of these questions could put a new sale—or existing relationship—at risk. In this example, MDM would ensure that a trusted customer profile is created to eliminate such issues in a company’s data.

MDM addresses the challenges associated with disparate applications that create, capture, and access data across multiple systems, applications, and channels. This includes SAP, Marketo, Salesforce, DemandBase, web portals, shipping systems, invoicing systems, contract systems, and more. With a trusted source of reliable, current data, organizations can get a better view of their products and suppliers, drive customer engagement, and offer a consistent experience to employees as well as customers.

Other issues addressed by MDM include:

• Manual data entry and errors such as transposing characters, miskeyed entries, and incomplete data fields
• Different name usage (Jim and James; GE or General Electric),
• Duplicate data entries and replication of data
• Data that has been updated in one system, but not in any others

MDM is of particular interest to large, global organizations, organizations with highly distributed data across multiple systems, and organizations that have frequent or large-scale merger and acquisition activity. Acquiring another company creates wide-reaching data integration challenges that MDM is designed to mitigate. Thus, MDM can accelerate the time-to-value from an acquisition.

MDM also helps prevent disjointed customer experiences in companies with segmented product lines, multiple interaction points and channels, and distributed geographies. With MDM, companies gain confidence that the data they rely on remains trusted and authoritative.

What are the benefits of Master Data Management (MDM)?

By providing one point of reference for critical business information, MDM eliminates costly redundancies that occur when organizations rely upon multiple, conflicting sources of information. For example, MDM can ensure that when customer contact information changes, the organization will not attempt sales or marketing outreach using both the old and new information.

Common business initiatives addressed by MDM include:

• Customer experience
• Mergers and acquisitions
• Governance and compliance
• Operational efficiency
• Supplier optimization
• Product experience

Master Data Management (MDM) Webinar Series: How to Succeed as a Data-Driven Company

Curious about what master data management (MDM) brings to an end-to-end data strategy? Our webinar series covers everything from MDM basics to the difference between MDM and data quality.

• An Introduction to a 360-degree View of Data
• Building a Business Case for MDM
• MDM and Data Quality

December 2021 Gartner® Magic Quadrant™ for Master Data Management Solutions:  For the six straight time, Informatica has been named a Leader in the December 2021 Gartner Magic Quadrant for Master Data Management Solutions.

Intelligent Master Data Management for Dummies : Learn how to deploy intelligent MDM and take the first steps toward capturing the full value of your data.

Three Ways Informatica’s Intelligent Data Management Cloud Improves Master Data Management

Master Data Management (MDM) Products

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Data & Knowledge Management Intern

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NEW YORK, UNITED STATES OF AMERICA

Type of Contract :

Starting date :.

01-Jul-2024

Application Deadline :

10-Jun-24 (Midnight New York, USA)

Post Level :

Duration of initial contract :, time left :, languages required :.

English  

Expected Duration of Assignment :

UNDP is committed to achieving workforce diversity in terms of gender, nationality and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.

UN Women, grounded in the vision of equality enshrined in the Charter of the United Nations, works for the elimination of discrimination against women and girls; the empowerment of women; and the achievement of equality between women and men as partners and beneficiaries of development, human rights, humanitarian action and peace and security.

The Strategic Planning Unit (SPU) within the Strategy, Planning, Resources and Effectiveness Division (SPRED) focuses on assisting UN Women and partners to achieve higher quality development results through an integrated approach that links strategic planning and Results-Based Management (RBM) with more effective and new ways of working.  SPU is the custodian of RBM as well as field and HQ level strategic planning and results-based reporting in UN Women.

SPU seeks a Data & Knowledge Management Intern to support results planning and reporting processes including by developing targeted data analyses, and internal knowledge management and communications

Duties and Responsibilities

Under the direct supervision of the Data Analysis & Monitoring Specialist, the intern will be responsible for the following:

• Support SPU on results planning and reporting processes by :
• supporting targeted data analyses (in Excel or Stata);
• Support in developing and updating PowerBi dashboards;
• Support internal knowledge management and communications by:
• assisting in preparing PowerPoint presentations for technical leads and senior management, when needed;
• Support in keeping SPU’s intranet data-related page up-to-date;
• Maintaining monitoring documents on the data workstream
• Supports the team with other functions, as needed.

Learning objectives for the internship in this area:

The intern will receive first-hand experience in strategic planning areas of work, exercising their data analysis and visualization expertise, and will acquire in-depth knowledge of UN-wide data streams and data use cases at UN-Women.

Competencies

Core Values:

• Respect for Diversity
• Professionalism

Core Competencies:

• Awareness and Sensitivity Regarding Gender Issues
• Accountability
• Creative Problem Solving
• Effective Communication
• Inclusive Collaboration
• Stakeholder Engagement
• Leading by Example

Please visit this link for more information on UN Women’s Core Values and Competencies:  https://www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/About%20Us/Employment/UN-Women-values-and-competencies-framework-en.pdf

Functional Competencies:

• Excellent organizational and communication skills
• Excellent analytical skills, including advanced knowledge of Excel is desirable
• Problem-solver, proactive mindset
• Prior knowledge of Results Based Management desirable
• Advanced knowledge of PowerPoint
• Experience developing PowerBI dashboards

Required Skills and Experience

• University studies in one of the following disciplines: development studies or other relevant subjects: statistics, mathematics
• Meet one of the following:
• Be enrolled in a graduate school programme (second university degree or equivalent, or higher);
• Be enrolled in the final academic year of a first university degree programme (minimum Bachelor's level or equivalent);
• Have graduated with a university degree and, if selected, must commence the internship within a one-year period of graduation; or
• Be enrolled in a postgraduate professional traineeship program which is part of a degree programme and undertake the internship as part of the program requirements.
• Excellent communication skills (written and oral) in English are required;
• Working knowledge of another UN language is an advantage.

Renumeration:

Interns who are not in receipt of financial support from other sources such as universities or other institutions will receive a stipend from UN Women to partially subsidize their basic living costs for the duration of the internship.

Application Information:

• All applicants must submit a completed and signed P.11 form with their application.
• Due to the high volume of applications received, we can ONLY contact successful candidates.
• Successful candidate will be required to provide proof of enrollment in a valid health insurance plan at the duty station of the internship, proof of school enrollment or degree, a scanned copy of their passport/national ID and a copy of a valid visa (as applicable).  

In July 2010, the United Nations General Assembly created UN Women, the United Nations Entity for Gender Equality and the Empowerment of Women. The creation of UN Women came about as part of the UN reform agenda, bringing together resources and mandates for greater impact. It merges and builds on the important work of four previously distinct parts of the UN system (DAW, OSAGI, INSTRAW and UNIFEM), which focused exclusively on gender equality and women's empowerment.

At UN Women, we are committed to creating a diverse and inclusive environment of mutual respect. UN Women recruits, employs, trains, compensates, and promotes regardless of race, religion, color, sex, gender identity, sexual orientation, age, ability, national origin, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, competence, integrity and organizational need.  

If you need any reasonable accommodation to support your participation in the recruitment and selection process, please include this information in your application.  

UN Women has a zero-tolerance policy on conduct that is incompatible with the aims and objectives of the United Nations and UN Women, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination.  All selected candidates will be expected to adhere to UN Women’s policies and procedures and the standards of conduct expected of UN Women personnel and will therefore undergo rigorous reference and background checks. (Background checks will include the verification of academic credential(s) and employment history. Selected candidates may be required to provide additional information to conduct a background check.)

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12 best reporting tools for better project visibility in 2024.

Updated on June 4, 2024 by Luke Henderson

Published on May 29, 2024 by Luke Henderson

Keeping your team aligned on pending tasks is not enough. You need to make your team’s achievements visible with the right reporting tools, such as project reporting software, resource management software, time tracking software, etc.

These online reporting tools use data to showcase your team’s efficiency. We understand you may not have the time to research the best reporting tools, so we’ve done the grunt work for you.

In this guide, we’ve created a list of reporting tools and compared their features, pros and cons, and pricing to help you make the right choice. Let’s go!

But First, What Are Online Reporting Tools?

Online reporting tools empower organizations to collect, organize, analyze, and transform complex data into easy-to-understand graphs and charts.

This data is sourced from multiple data sources such as Excel sheets , databases, cloud applications, and social media. The reporting tool extracts insights and presents repeatable trends for the team to understand.

These tools create data in easy-to-digest formats such as graphs, tables, pie charts, dashboards, and so on.

12 Free Reporting Tools to Keep Updated in Real-Time

1. nifty: best project reporting software  .

When it comes to data-backed, gorgeous reports, Nifty takes the lead by offering a seamless experience. You’ll find it incredibly easy to create stunning visualizations and create reports that bring your data to life.

Nifty is a diverse and affordable project management platform that combines reporting and collaboration into one tool. Its reporting functionality is extensive and easy to understand, and you can customize your requirements for a variety of use cases and roles.

What Makes Nifty a Good Reporting Tool?

Nifty offers a bird’s eye view of your timelines and projects within your workspace—an essential component of reporting. After all, you can’t report what you don’t see. The tool’s reporting feature enables project managers to report on critical touchpoints within a project such as ongoing as well as completed tasks, time logs , and activities.

Dashboard-style Reports: Nifty provides a robust reporting system by bringing your workspace and project tasks into an interactive dashboard with highly visual reports: 

You can effortlessly generate and share these visual reports with stakeholders at one-click. More importantly, you can dive into mission-critical project metrics such as status, project, assignee, tag, and more.

Time tracking: If you’re looking to boost productivity and manage team workloads, Nifty’s time tracking feature is right up your alley. With meaningful time logs, you can track billable hours and gain valuable insights, as shown here:

Your team can monitor time spent on tasks and track billable hours. You can instantly access automated reports and filter insights of your project’s time logs as per team members or tasks. You can also look at “tracked hours” from the Team Overview tab and balance your team’s workload without much effort.

• Users can report the whereabouts of their tasks, projects, and documents, all from one tool
• Nifty’s time tracking feature produces professional-looking reports, which users can download in PDF or .CSV format
• You can connect your reports to the project with the time-tracking feature
• Project milestones functionality automates status reporting and makes it easy for your team to visualize the project schedule easily
• The software is intuitively designed and is not overly complicated to use
• The tool offers advanced project management features in the paid plans
• Free forever plan: $0 (with unlimited users and tasks)
• Starter plan:  Starts at $5/user
• Pro plan:  Starts at $10/user
• Business plan: Starts at $16/user

Use Nifty for all your project reporting. Get Started Free

2. Zoho Analytics: Best for 360-degree Business Data Reporting

Zoho Analytics is a great tool for converting business data into insightful reports and dashboards. Owing to its data analytics capabilities, it can handle vast amounts of data effortlessly and access clear, impactful visuals that make data interpretation a breeze. You can also combine data from different sources to build comprehensive, cross-functional reports.

More importantly, sharing and collaborating on your reports and dashboards has never been easier. With this powerful business intelligence software by your side, you can rest easy knowing your data is protected with robust security features like role-based access controls.

• The platform is intuitive and simple to use for creating reports, thanks to its drag-and-drop report builder
• It allows users to connect with Zia, an AI-powered data analytical assistant who can offer valuable business insights quickly and powerful visualizations
• The tool seamlessly integrates with different data sources, consolidates the information, and analyzes it easily
• Some users feel that the platform has a steep learning curve, particularly for newer project managers who want to create custom reports but have no data analysis background
• The free version offers restricted customization options
• Some users also complain of slow loading times for data uploading within reports
• Basic:  US$24/month, billed annually
• Standard:  US$48/month, billed annually
• Premium:  US$115/month, billed annually
• Enterprise:  US$455/month, billed annually
• Custom:  Contact sales

3. Celoxis: Best for Data-backed Teams

Want comprehensive, real-time insights across your projects and teams? Leverage Celoxis’ powerful data analytics platform with its robust reports and dashboards.

You can create detailed reports using custom fields. Plus, you can dive deeper with custom drill-down charts and perform bulk actions on multiple records at once.

For convenience, you can download these reports and dashboards as PDFs and schedule email deliveries to keep everyone informed.

• It offers multiple shared dashboards, each of which you can tailor to your specific needs
• It allows integration with popular tools like Jira, Salesforce, etc.
• Its user interface is highly customizable, particularly for workflows and reports
• You can customize widgets to fit your viewing preferences and resize them for optimal viewing on your dashboards
• Some users find the pricing to be too steep, especially for smaller teams with shoestring budgets
• Avid users also feel that the analytics can do with more fields and be improved upon
• Manager:  $25/month, billed annually
• Team:  $15/month, billed annually

Leave your project reporting hard work to us. Get Started

4. Jira: Best for Agile Teams

Atlassian developed Jira, a robust project management tool that focuses exclusively on agile methodologies. Its powerful reporting features allow you to create customized dashboards and reports, track goals, and use flexible data charts to monitor your projects.

Jira excels at providing in-depth analytics for on-point performance. You can also take a proactive approach and spot potential issues to make informed decisions.

Additionally, Jira’s ability to track key performance indicators (KPIs) makes it an essential tool for monitoring project performance and success. If you want to tailor your reports to show key metrics such as project status , issue types, velocity, and team performance, Jira allows you to do it and share said reports easily with key stakeholders.

• The tool offers comprehensive reporting and insights into your project progress as well as team performance
• It generates reports easily and helps the team track bugs with advanced filters
• It offers agile tools , including scrum and kanban boards, to address the specific needs of your team
• The platform offers extensive visualization tools and integrates with numerous tools, such as Slack, Microsoft, Google Workspace, Zoom, and many more. You can even build your own integrations using Jira’s API
• It is a complex tool with a steep learning curve
• Some users also feel that the UI is too overwhelming at times
• Free:  $0
• Standard:  $7.16/user/month
• Premium:  $12.48/user/month
• Enterprise:  Custom pricing

5. HubSpot: Best for Business Reporting

HubSpot’s Dashboard and Reporting software empowers your team with powerful analytics. Designed with business users in mind, it offers user-friendly features that make it accessible to non-technical users. You can connect your CRM data to your sales, marketing, and customer service data in one place and build a single source of truth.

• It provides advanced reporting permissions to help keep your data safe and secure
• It helps you to build high-level or specific custom dashboards for your team and leadership or use pre-built dashboards with popular use cases
• You can combine multiple reports and track multiple metrics for your teams 
• Its dashboard template library helps you to map your reporting to your business needs
• Some users find the platform to be too pricey
• Marketing Hub Professional : Starts at S$1,120/mo (includes 3 seats)
• Marketing Hub Enterprise: Starts at S$5,100/mo (includes 5 seats)
• Free Tools : $0
• Marketing Hub Starter: Starts at S$21/mo/seat
• Starter Customer Platform : Starts at S$21/mo/seat

Do you know Nifty also offers great business reporting capabilities? Know more

6. ProWorkflow: Best for Graphical Data Reports

If you manage remote or hybrid teams , ProWorkflow is perfect for you. It lets you see all your projects, tasks, time logs, contacts, workflows, and resources in one place. You’ll love how it handles invoicing, messaging, file sharing, and timelines, making it an all-in-one tool for managing your workflow.

Additionally, ProWorkflow excels at handling and visualizing raw data, allowing you to turn raw data into actionable insights for informed decision-making.

Plus, if you’re using Microsoft products, it integrates nicely with Microsoft Teams and Outlook. Just keep in mind that it doesn’t have as many integrations as some other tools, so you might need to manually enter some data.

• Users love the time tracking feature as it is accurate and easy to use
• The dashboard has tools that are logically located, allowing users improved control over projects
• The tool’s comprehensive mobile app allows for remote working, especially when it comes to managing organizational data
• The platform is known to have an extremely high learning curve, as per some users
• It also lacks technical support and tutorials to help users get up to speed
• Considering it isn’t an all-in-all reporting tool, it falls short of advanced reporting capabilities
• Some users also report that its customization is difficult to set up
• Professional: $18/month, paid yearly
• Advanced: $27/month, paid yearly

7. Clockify: Best for Time Tracking Reports

Clockify is a popular free-time tracker for teams. If you wish to use online time reporting software, Clockify enables the team to see how they’re spending their time and get visual reports.

Some important time-related metrics Clockify enables you to see include:

• Which employee took time off
• What kind of revenue does each project bring?
• Which team member is working on what and on which day?
• It is extremely simple to use as it works as a timesheet app—but within your browser
• It allows you to deep dive into the tracked time by way of Detailed, Summary, and Weekly reports
• The Dashboard feature helps you to see where you’re spending the most time and what your team is focusing on. It gives you a complete overview of all tracked hours across teams, as well as a total of all billable hours
• The Summary Time Report lets you dive deeper into your tracked data where you can break it down and analyze it using filters for project, department, user, group, tag, and date
• It does not allow for easy customization of time reports
• The Import functionality is too tedious for large-scale projects with multiple tasks, as per some users
• Basic:  $3.99/user/month, billed annually
• Standard:  $5.49/user/month, billed annually
• Pro:  $7.99/user/month, billed annually
• Enterprise:  $11.99/user/month, billed annually

8. Bamboo HR: Best for HR Reporting

Want to build full-fledged HR reports within seconds? Bamboo HR is one of the best online reporting tools for analyzing people’s data. The tool helps you build a centralized database of HR data so that you can drive strategic decisions.

Most organizations struggle with tracking disjointed and scattered sensitive employee data. Bamboo HR makes this process easy by pulling all the data together in one place. 

• The tool is extremely easy to navigate as per some users, helping them generate instant HR reports with the least resistance and effort
• It offers 49 built-in reports to help you make sense of the data and streamline data management
• Some users complain of limited integration options within the tool
• Others also claim that the system can be challenging to navigate post-implementation

Pricing: Custom pricing

9. Reportei: Best for Digital Marketing Reports and Dashboards

If you need to create digital marketing reports on the fly, Reportei is perfect for you. It focuses on social media data and analytics, making it best suited for agencies, freelancers , and marketing professionals who want to spend less time creating reports and more time analyzing results.

If you wish to adjust your report metrics with ever-changing campaign objectives, Reportei makes the job simpler for you.

• The marketing timeline feature lets you add events and milestones throughout your project and helps you track progress efficiently
• The platform offers a host of built-in tools to accelerate your digital data analysis
• It integrates seamlessly with platforms such as Facebook, Instagram, LinkedIn, YouTube, etc., and offers easily-assembled reports at one click
• The tool’s AI feature is not up to the mark, as per some users
• Other users also complain that managing templates is not practical or easy
• It doesn’t allow users to create unlimited dashboards with lower-priced plans

Pricing: 

• Starter: USD $24, paid monthly
• Pro:  USD $39, paid monthly
• Premium: USD $79, paid monthly

Create a marketing reports dashboard with Nifty. Get Started

10. Power BI: Best for Marketing Reports

Power BI is an online reporting tool that converts complex data into visually appealing insights. It seamlessly integrates data from various sources and presents it in a way that’s easy for your team to understand.

Compared to other tools like Google Data Studio, Power BI offers robust integration capabilities, making it a versatile choice for building reports and designing data visualizations.

Perfect for both large enterprises and growing businesses, Power BI handles substantial data loads effortlessly. This powerful business intelligence tool from Microsoft allows you to create automated reports, interactive dashboards, and comprehensive visualizations of your marketing data.

• If you are familiar with other Microsoft products, such as Excel, this tool is easier to use
• You can securely share insights with colleagues and embed interactive reports into your apps
• Its data transformation capability provided by Power BI (Power Query) helps transform data on the go
• It’s great for building project and status reports as it offers a user-friendly interface
• With over 120 free connectors and integration with data sources such as Excel, Salesforce, and Azure SQL Database, Power BI stands out in the analytics arena
• Some users report that the tool is difficult to use if dealing with large amounts of data
• Its reporting and dashboard functionality tends to become slow at times
• The process of uploading reports from the desktop to a cloud platform can be time-consuming
• Power BI Pro: $10/user/month
• Power BI Premium Per User: $20/user/month
• Power BI Embedded: Custom pricing

11. nOps: Best for Cloud Reporting

Need to pull up cost reports and share them among departments? Or maybe you want weekly reports on your team’s performance? Either way, nOps is the tool for you. You can also get instant reports of security violations. nOps has been branded as feature-rich for users, which includes its reporting capabilities.

• Its graphical user interface (GUI) and detailed reports in CSV format help drive agile data analysis
• The tool is easy to set up and use
• The software offers actionable and descriptive insights for the team to use
• It is also highly configurable, allowing you to set up notifications and alerts as you need
• The dashboard interface can be more customizable
• Some users want the data to be more illustrative 
• Others report difficulty in downloading the remediation report of high-risk vulnerabilities within the organization
• Customers also report glitches during reporting 

12. Wrike: Best for Collaborating on Project Reports

Wrike is one of the best tools for project management and data reporting. The Project Dashboards feature helps track productivity, metrics, and project progress while facilitating collaboration among multiple users.

As one of the leading data reporting tools, it also allows you to bring more transparency to your executive team with visual and engaging project reports.

That’s not all. You can customize it to create unique dashboards with widgets tailored to your projects. If you prefer, you can use project reporting templates for generating quick reports.

• Once tasks are assigned, you can generate reports to track the amount of work completed within a specific timeframe
• Its reporting tool is comprehensive, and the in-built automation helps reduce repetitive tasks
• The Project Management Dashboards drive visibility into project progress and empower managers with the-moment insights
• You can create dashboards for multiple use cases, such as individual, team, or executives
• Some users claim that the software lacks features for agile reporting
• The tool also does not offer a complete overview of the entire company
• Its reporting and analytics capabilities can be more intuitive
• Free: $0/user/ month
• Team:  $9.8/user/ month
• Business:  $24.8/user/ month
• Enterprise: Custom pricing
• Pinnacle:  Custom pricing

3 Top Features of Free Reporting Tools You Should Look For

The right online reporting tool combines project management features with seamless data visualization capabilities. It should be versatile enough to help you create marketing reports, financial reports, and project management reports.

If you’re looking for a checklist of must-have features for reporting tools, read on:

• Collaboration:  Your project’s reports are vital to your stakeholders and your team at large. You should be able to share them with them in various formats, such as email, chat, and so on. 
• Data visualization : The tool should be able to present important data, such as essential KPIs, in the form of dynamic reports, immersive dashboards, and interactive charts—using Excel sheets just won’t cut it. Put another way, the reports the tool provides must be easy to understand and intuitive.
• Analytics:  Given that your team will be dealing with different data sources during the project, the online reporting tool should pull real-time data using strategic templates. With updated information as the backbone of all your decisions, you’ll be able to forecast your project’s needs easily.

Give Your Reporting an Edge with Online Reporting Tools

As a project manager , it’s your responsibility to make processes easier for the team. This includes helping them “decode” (for lack of a better term) complex data. After all, data is the backbone of high-performing organizations—you use data to understand where your team is outperforming and where it’s falling short of client expectations.

This is where investing in a reporting tool pays off. The software does all the background work—from pulling real-time data across sources to converting it into highly visual, simplified reports. So, whether you want to get buy-in from executives or motivate your team, an online reporting tool will do the job for you.

So, take your pick from the list of reporting tools presented here and empower the team with the plug-and-play reporting software of their dreams. Sign up for Nifty now.

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Data Management

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Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior's Keweenaw Bay

Keweenaw bay water resources (fall 2022).

Team:  Khaim Syed-Raza (Project Lead), Sofia Vahutinsky, Lisa Siewart, Nora Whitelaw-McDonald

Summary:  The Keweenaw Bay Indian Community (KBIC) has a shoreline south of Lake Superior that is contaminated with copper stamp sands from legacy mining. The stamp sands have been capped with sandy-loam soils and restored native species, but erosion and flooding threaten to re-deposit these stamp sands onto wetlands and into the bay. Erosion and flooding also endanger the loss of beaches and shorelines, infrastructure, wetland restoration projects, and coastal highways, which has driven shoreline armoring. However, while shoreline armoring can effectively protect the intended areas, it can also exacerbate erosion in nearby unarmored regions, so its net impact on the shoreline is yet to be quantified. The DEVELOP team partnered with KBIC and the Environmental Protection Agency (EPA) to utilize imagery from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) to analyze turbidity proxies. These analyses showed that seasonal variation in the study area was most significant during the rain-dominated season. Still, spatial variability across our study period was unclear and did not show a clear trend. These results will better inform future shoreline management efforts and support resilience in the face of more coastal erosion. 

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