While media debates whether data is the new oil or not, one thing is clear: Like oil, data needs a lot of processing. From Facebook to growing startups, any successful organization that handles a growing volume of data, must be able to organize, access, secure and process data to convert it into insights and decisions.
There are many tools and vendors to consider, particularly in terms of the needs of the business and the task at hand. However, regardless of the task, the goal is to ultimately find a data management product to make data as useful as possible while minimizing cost, risk, and resource consumption.
This is a list of data management software, however, it is not comprehensive. We prepared a regularly updated, comprehensive sortable/filterable list of leading vendors in data management software, feel free to check it out.
Data Management Software
Data management is a broad discipline, with many different focuses and tools to manage these focuses. Data Management Software (DMS) merges records from several databases, extracts, filters, summarizes the data without loss of integrity and interference.
Some vendors and softwares contain multiple functionalities and can eliminate the need for a dedicated tool.If you’re in search of a bit more background about data management, be sure to check out our blog post on the topic.
We can structure data management software around these topics
- Open source data management software: There are numerous open source data management tools that serve a variety of the functions below.
- Data design:
- Data architecture and data model design software: First, companies need to model their data structures
- Master and reference data management:These are the foundations of best practice database management and help organizations manage their data across different business units
- Database management:These modelled data structures need to be created in databases
- Document collection and analysis:Documents and other unstructured content pose challenges for especially traditional databases. Various document collections solutions facilitate unstructured content management
- Metadata management: Metadata is valuable as the simplest metadata fields such as update and creation times allow companies to identify issues in their data and analyze the data creation and update processes
- Data quality management: Once data federation (collection) begins, data quality needs to be monitored and there are numerous solutions to measure and increase data quality
- Data analysis: Finally, numerous solutions of differing complexity enable companies to analyze this data
Open source data management software
Before we categorizing data management tools based on their feature, we thought you may prefer open source solutions for their transparency and lack of licensing fees. Therefore we start with open source data management table:
|Airtable||2012||Private||-Airtable is a cloud-based database software|
-Free plan offers unlimited data tables, 1,200 records per base, 2GB file attachment space per base, and up to 2 weeks of revision and snapshot history.
|GraphDB-Ontotext||2000||Private||-GraphDB is a graphical database that offers cloud and on-premise deployment.|
|MariaDB||2009||Private||-MariaDB covers similar features to MySQL with some added extensions.|
-Fortune 500 companies using MariaDB: Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens
|Cubrid||2008||Private||-CUBRID is an open source DBMS optimized for OLTP.|
|FirebirdSQL||2005||Private||-CouchDB is an online document database and storage solution for businesses.|
-The tool provides ACID semantics through multi-version concurrency control.
Data Architecture and Data Model Design
Data architecture is the models, policies, or rules that govern which data is collected, how it stored, and how it is used. It is then further split into enterprise architecture or solution architecture.
Data modelling defines and analyzes data requirements necessary for business processes within information systems. There are three different types of data models produced, which progress from the conceptual model, to the logical data model, and finally arrive with the physical data model.
All of these categories help to organize and map data, improving its reliability and also transparency within an organization.
Some useful tools related to these products include:
-Database management to reduce redundancy
|Teradata||1979||Public||-Big Data architecture that can be built from multiple data platforms|
|Looker||2011||Private||-Data analysis without SQL|
|Tableau||2003||Public||-Rapid ad hoc analysis without programming|
-Automatic updates or live connection
Reference and Master Data Management
Reference data is a subset of master data that can be used for classification throughout an organization. Some of the most common reference data include postal codes, currency, codes, and other classifications – but it can also be ‘agreed upon’ data within an organization. Managing this type of data is important as it often serves as reference for a number of systems.
There are a number of tools available to assist with reference data management, here are a few:
|ASG metaRDM||1986||Private||-Focus on compliance support|
|Collibra Reference Data Accelerator||2008||Private||-Easy deployment and implementation|
|Informatica Cloud - MDM Reference 360||1993||Public||-Utilizes INFA Cloud MDM foundation|
|Kalido by Magnitude Reference Data Management||2014||Private||-Embedded workflow engine for stewardship and governance|
Master Data Management (MDM) is a comprehensive method for defining and managing the essential data of an organization in order to provide a point of reference. Software for this field supports the identification, linking, and synchronization of customer information across disparate data sources. This information is used in support of a number of initiatives related to data stewardship and governance.
Some popular MDM tools and vendors include:
|Orchestra Networks EBX||2000||Private||-Includes functionality for master, meta, and reference data|
|Dell Boomi||1984||Public||-Features such as ‘Boomi Suggest’ and ‘Boomi Assure’ to help with development and testing|
|Stibo Systems||1976||Private||-Emphasis on multidomain MDM|
|Profisee||2007||Private||-Solutions built by industry|
To learn over 100 master data management vendors and tools, feel free to check our sortable and transparent vendor list where we sorted vendors based on popularity, maturity of the business and user satisfaction.
Database management has a variety of objectives ranging from performance, to storage, to security and more. Tools aim to control data throughout its entire lifecycle, leading to better business intelligence and better decision making.
Some general tasks that should be met with the right database management software include:
- Application tuning
- Response time testing
- Throughput testing
- Performance management
It is important to keep in mind the difference between DBMS and RDBMS. DBMS is a general term for different types of database management technologies that have been developed over the last 50 years. In the 1970’s, a relational database management system (RDBMS) was born and quickly became the dominant technology in the field. The most important factor in RDBMS is its row-based table structure that can connect related data elements, which is achieved via database normalization. Since 2000s, non-relational or no-SQL databases like MongoDB started gaining popularity but relational databases are still important for storing structured data.
Some vendors that work within this discipline include:
|Oracle Enterprise Manager||1977||Public||-Self management capabilities built into database kernel|
-For Linux, Windows, Solaris, IBM AIX, UP-UX
|IBM DB2||1983||Public||-For Linux, Unix, and Windows|
|MongoDB||2007||Public||-Works with AWS, Azure, and Google Cloud|
-Several versions: Enterprise Advanced, Stitch, Atlas, Cloud Manager
Document, Record, Content Management
Enterprise content management, sometimes called document management, is the process of storing, managing, and monitoring documents from daily business activities.
Some general functionalities that any solution should include are:
- Document scanner for making digital copies of paper texts
- Optical character recognition (OCK) to convert scanned documents
- User based access
- Document assembly to create using a cabinet-and-folder structure
- PDF converter
- Document storage and backup
- Integration options
- Collaboration tools and version control
|Alfresco||2005||Private||-Range of workflow and collaboration options|
|Dokmee/Office Gemini||2006||Private||-A lower cost option than some|
|eFileCabinet||2001||Private||-A strong option for remote teams|
Metadata management is the administration of data describing other data. It also entails processes for ensuring that data can be integrated and utilized throughout the organization. It is important for maintaining the consistency of definitions, clarity of relationships, and data lineage.
Some common tasks associated with metadata management that should be fulfilled with any software or tool include:
- Metadata repositories for documentation and management and to perform analysis
- Data lineage to specify the data’s origin and where it has moved over time
- Business glossary to communicate and govern key terms
- Rules management to automate the enforcement of business rules
- Impact analysis detailing any information dependencies
|Adaptive Metadata Manager||1997||Private||-Over 20 years of experience with a number of partnerships|
|Data Advantage Group||1999||Private||-Known for ease of implementation|
|Informatica Metadata Manager||1993||Public||-Concentration on information governance and analytics|
|Smartlogic Semaphore||2005||Private||-Captures inconsistent and incomplete metadata related to information assets|
Data catalogs automates metadata management and makes it collaborative. To learn more about data catalog technology, feel free to read our article.
Data Quality Management
According to IBM, US economy loses $3.1 trillion annually due to poor data quality. When we talk about the condition and usability of the data for its intended function, we’re talking about data quality. Some major processes associated with ensuring high data quality include:
- Parsing and standardization: Breaking down text fields into their components and formatting their values into consistent layouts based on the chosen criteria. Some common layouts are defined by industry standards, user-defined business rules, or knowledge bases of values and patterns.
- General “cleansing”: Updating data values to fall within domain restrictions, integrity constraints or other business rules that determine minimum data quality for the organization
- Profiling: Data analysis to capture statistics (metadata) to obtain insight into the quality of the data and locate data quality issues
- Monitoring: Process to ensure conformance of data to set quality rules for the organization.
- Enrichment: Increasing the value of internally held data by adding related attributes from external sources
Any data quality tool you consider should include functionality for all of the above and more. Some major vendors include:
|Talend Open Studio for Data Quality||2005||Public||-Open source with over 400 built-in data connectors|
|Ataccma||2007||Private||-Machine learning, self-service data preparation, data catalog|
|BackOffice Associates (BOA)||1996||Private||-Range of prepackaged reports available|
|Innovative Systems: Enlighten||1968||Private||-Address validation and geocoding feature|
Data Warehousing and BI Management
A data warehouse is the consolidation of data from a wide range of sources that sets the foundation for Business Intelligence (BI). All data here is stored in the same format, but intelligent algorithms such as indexing enable effective analysis.
Business Intelligence is the set of methods and tools used by organizations to take data and make better informed decisions based upon it. BI platforms describe either what is happening with your business at the exact time or what has happened – preferably in real time.
To better understand the tools for each of these, the following table compares the major differences:
|What it is||Source||Output||Audience|
|Business Intelligence||System to derive business insights||Data from data warehouse||Reports, charts, graphs||Executives, management|
|Data Warehouse||Data storage, historical and current||Data from different sources||Data in consistent format for BI tools||Data engineers, data and business analysts.|
Some examples of tools for these processes:
|Microsoft Power BI||BI||2013*||Public||-Similar interface to Excel|
|QlikView||BI||1993||Private||-Includes data mining and analytics|
|Cognos||BI||1969||Private||-Multidimensional and relational data sources|
|Tableau||BI||2003||Public||-Widely regarded as one of the best options in terms of visualizations|
|Teradata Data Warehouse||DW*||1979||Public||-Uses AMPs (Access Module Processors) to increase data processing speeds|
|Amazon Redshift||DW||2012*||Public||-Completely managed tool - no need for DBA|
|Oracle Data Warehouse||DW||1977||Public||-Includes some BI functionality|
*DW = data warehousing
*Year of product founding, not company founding
Data warehouses often exist in close conjunction to an ETL (Extract, Transform, Load) solution that takes data from many different sources and ‘transforms’ it into a single, usable format for the data warehouse. To learn more, see our about ETLand ETL tools blog posts.
Data analysis is the result of all this processing of data. Data analysis is the process of inspecting, cleansing, transforming, and modeling data in order to find useful information. Data analysis also includes data mining, statistical applications (descriptive statistics, exploratory data analysis), and a wide range of techniques for analyzing statistical data, such as hypothesis testing or regression analysis.
For more on data management
If you are interested in learning more about data management, read:
- 4 Metadata Management Best Practices (With a list of Top Tools)
- 4 Ways Augmented Data Management Changes Traditional Data Management
And if you believe your business would benefit from a data management platform, we have a data-driven list prepared.
Go through it, and we will help you choose the best one tailored to your needs:
Find the Right Vendors
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch like Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Jul 12, 2021 at 06:30
Awesome! Thanks for this informative and detailed article. Data management is useful and beneficial as it safeguards valuable information. It is why managing your data with the best data protection provider is very important.Reply
May 31, 2019 at 06:45
Informative article! Opentext-AI is also a powerful data visualization tool, would love to see Opentext-Magellan on this list!Reply
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Jul 25, 2019 at 19:57
please i am interested in learning data management… i need the assistance i can getReplySee AlsoJob Application Letter Format, Samples & Examples.Salesforce was named one of the best companies to work for in 2020. Here's how to nail the interviews and land a six-figure job at the software giant.Best Construction Project Management Software 2022Top 10 Project Management Software Reviews for 2020 - 2021
Like Amazon and Azure, the Google Cloud Platform also offers a wide array of cloud-based data management tools. It also provides a useful workflow manager that's leveraged to tie-up different components together.
1. Tableau. A business intelligence platform, tableau can be accessed on cloud or as a software. This is one of the most common big data management tools that provides hassle free connection to different sources of data.What is the best way to manage data? ›
- Build strong file naming and cataloging conventions. ...
- Carefully consider metadata for data sets. ...
- Data Storage. ...
- Documentation. ...
- Commitment to data culture. ...
- Data quality trust in security and privacy. ...
- Invest in quality data-management software.
Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.Which is best tool for data analysis? ›
Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic.What are data management skills? ›
Data management skills are the abilities you use to effectively manage and use information. Data management skills involve looking for patterns, understanding database design concepts and being able to participate in short and long-term planning about database projects.What is data management types? ›
Types of Data Management (DM) tools. PIM (Product Information Management) MDM (Master Data Management) DM (Data Modelling) DW (Data Warehouse)What is an example of data management? ›
Using a data management platform provides you with control over your data for multiple use cases. For example, a data management platform could collect customer data from multiple sources, then analyze and organize it to segment your customers by purchase history.What are the 3 main types of databases? ›
hierarchical database systems. network database systems. object-oriented database systems.What are 3 examples of a database? ›
Some examples of popular database software or DBMSs include MySQL, Microsoft Access, Microsoft SQL Server, FileMaker Pro, Oracle Database, and dBASE.
The data management function is a core set of business processes, such as Finance, Human Resources, or Facilities Management, that provides resources to facilitate the establishment and adoption of best practices across data management disciplines, and which will always be needed across the full lifecycle of patient ...What is data management in simple words? ›
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.How do you master data management? ›
- Identify sources of master data. ...
- Identify the producers and consumers of the master data. ...
- Collect and analyze metadata for your master data. ...
- Appoint data stewards. ...
- Implement a data governance program and data governance council. ...
- Develop the master data model. ...
- Choose a toolset.
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.What are the benefits of good data management? ›
- Increasing the impact of your research. ...
- Avoiding duplication of effort. ...
- Making it easier to share. ...
- Ensuring research integrity and validation of results. ...
- Ensuring accountability.
The easiest solution to this issue is to implement better data processes. This means defining roles and expectations, naming conventions or taxonomies, timeframes, etc. With more specific processes in place, it can be easier to prevent data issues as well as to identify and resolve them more quickly.Why is data so important? ›
Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals. A baseline is what a certain area looks like before a particular solution is implemented.What are data tools? ›
Data Tools means Software which, among other features, configures and runs Data Pipelines and collects Configuration Data. As of the Effective Date, Data Tools include StreamSets Data Collector and Transformer.What is a data analytics tool? ›
Data analysis tools are software and programs that collect and analyze data about a business, its customers, and its competition in order to improve processes and help uncover insights to make data-driven decisions.What are the 5 types of data analytics? ›
- Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
- Prescriptive data analytics. ...
- Diagnostic data analytics. ...
- Descriptive data analytics.
Grade 12 Data Management does require logic along with some pattern skills and most student believe it to be the most easiest and interesting math because it's less theoretical than other branches of mathematics like Advanced Functions and Calculus.How can I improve my database skills? ›
- Make SQL Part of Your Work Day.
- Document Your SQL Learning Experience.
- Produce Reports using SQL for your business.
- Share Your SQL Knowledge with Others.
- Volunteer or Freelance on an SQL or Database Project.
- Learn SQL Early in Your Career.
Managing data can be difficult because there are many different ways in which data can come into existence. Data can come from different sources like social media, IoT devices, sensors, databases, and more.What is data management framework? ›
A data management framework is a model of the people, processes and policies that you need to succeed at managing enterprise data. We put data management frameworks together to help ensure you have all of the right elements you need to deliver great data to your business.What are the components of data management? ›
- Roles and Responsibilities. ...
- Types of Data. ...
- Data Formats and Metadata. ...
- Access, Sharing, and Privacy. ...
- Policies and Provisions for Re-use & Re-distribution. ...
- Data Storage and Preservation. ...
- Step 1) Take business ownership of data management. ...
- Step 2) Connect data sources. ...
- Step 3) Manage your metadata. ...
- Step 4) Plan how to deliver your data. ...
- Step 5) Adopt consistent data governance policies.
- The Oracle. Oracle is the most widely used commercial relational database management system, built-in assembly languages such as C, C++, and Java. ...
- MySQL. ...
- MS SQL Server. ...
- PostgreSQL. ...
- MongoDB. ...
- IBM DB2. ...
- Redis. ...
MySQL, SQL Server, MongoDB, Oracle Database, PostgreSQL, Informix, Sybase, etc. are all examples of different databases.What are types of SQL? ›
- Data Definition Language (DDL) Statements.
- Data Manipulation Language (DML) Statements.
- Transaction Control Statements.
- Session Control Statements.
- System Control Statement.
- Embedded SQL Statements.
Depending upon the usage requirements, there are following types of databases available in the market − Centralised database. Distributed database. Personal database. End-user database.
Database Systems or DBMS is software that caters to the collection of electronic and digital records to extract useful information and store that information is known as Database Systems/ Database Management Systems or DBMS. The purpose of a standard database is to store and retrieve data.Is Excel a database? ›
As a spreadsheet program, Excel can store large amounts of data in workbooks that contain one or more worksheets. However, instead of serving as a database management system, such as Access, Excel is optimized for data analysis and calculation.What is data management services? ›
Data management as a service is a type of cloud service that provides enterprises with centralized storage for disparate data sources. The label "as a service" references a pay-per-use business model that does not require the customer to purchase or manage infrastructure for data management.What is data analysis and management? ›
Data analytics is a discipline focused on extracting insights from data, including the analysis, collection, organization, and storage of data, as well as the tools and techniques to do so.How do you define data quality? ›
Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.How data is used in management planning? ›
A data management plan helps achieve optimal handling, organising, documenting and enhancing of research data. It is particularly important for facilitating data sharing, ensuring the sustainability and accessibility of data in the long-term and allowing data to be reused for future research.What is data management plan document? ›
What is a Data Management Plan? A data management plan, or DMP, is a formal document that outlines what you will do with your data during and after a research project. Many funding agencies, especially government funding sources, require a DMP as part of their application processes.Is MySQL a data management tool? ›
MySQL is a database management system.
It may be anything from a simple shopping list to a picture gallery or the vast amounts of information in a corporate network. To add, access, and process data stored in a computer database, you need a database management system such as MySQL Server.
As a spreadsheet program, Excel can store large amounts of data in workbooks that contain one or more worksheets. However, instead of serving as a database management system, such as Access, Excel is optimized for data analysis and calculation.Is GitHub a data management tool? ›
GitHub - girder/girder: A data management platform for the web, developed by Kitware.
- #1) InterBase.
- #2) Microsoft SQL.
- #3) Database Performance Analyzer.
- #4) MySQL.
- #5) PostgreSQL.
- #6) MongoDB.
- #7) OrientDB.
- #8) MariaDB.
In terms of data security, the SQL server is much more secure than the MySQL server. In SQL, external processes (like third-party apps) cannot access or manipulate the data directly. While in MySQL, one can easily manipulate or modify the database files during run time using binaries.Why MySQL is the best database? ›
MySQL is unquestionably the most reliable and secure database management system many renowned companies like Netflix and Amazon use. MySQL prevents your application's sensitive data from cyberattacks with data protection features.What is data management system? ›
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.How do you create a data management plan? ›
- What's the purpose of the research?
- What is the 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? ...
- Who is responsible for managing the data?
Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization.Is PixieDust a data management tool? ›
PixieDust is an open source Python helper library that works as an add-on to Jupyter notebooks to improve the user experience of working with data. It also fills a gap for users who have no access to configuration files when a notebook is hosted on the cloud.Is Kubeflow a data management tool? ›
A machine learning pipeline tool like Kubeflow takes over the job of building, managing, and monitoring data processing pipelines.How do I manage data in Salesforce? ›
- Create an export file in a program you will be importing from.
- Check whether data fields in the imported file match with the appropriate Salesforce fields.
- Make corrections and add new fields, if needed.
MDM stands for Master Data Management. To put it simply, it means creating a single source of truth, or one master “golden record” for all versions of a record across all systems. This could be a person, an object, or place, and a CRM like Salesforce can be one point of entry for this data.
- Select External Data > New Data Source > From Online Services > From Salesforce.
- Do one of the following: To import, select Import the source data into a new table in the current database. To link, select Link the data source by creating a linked table.
- Select OK.