Top 14 Data Management Tools for 2022

Virtually every aspect of our lives is data-driven. Today, data is the engine of everything and has become a valuable resource to any organization that receives vast amounts of data from many different sources.

But why is the data valuable? It comes from the knowledge that we can discover by manipulating it to extract its potential. In order to drive growth, a company has to have the right strategy and the right tools. And that’s where data management comes in.

This post presents you the concept of data management, as well as the importance and key features of data management tools for your business strategy. It ends with a comprehensive list of top data management tools for 2022.


What is Data Management

Data management (DM) essentially describes the process of data collection, storage and use in an efficient, secure and cost-effective manner. Data management tools offer features like archiving, backup, disaster recovery, archiving, research, analytics and more. The main focus is on connecting and transferring data from different sources and making critical business decisions.

Data Management covers the following operations:

  • Build, access and update data at different levels.
  • Store data across clouds and on-premise.
  • Use data across apps, analytics, and algorithms
  • Provide high-availability and disaster recovery.
  • Secure data and provide privacy.
  • Archive and destroy data in accordance with compliance requirements.

Effective data management is a combination of best practices, concepts, processes, procedures and a broad set of tools that help businesses effectively control and manage their data assets. In other words, it is a cross-platform process that uses a variety of tools and objectives to achieve centralized data consistency. It is monitored throughout the lifecycle of a data asset.


Data management is closely linked to the following principles and concepts:

  • Data Access: The ability to operate and gather information wherever it is stored.
  • Data Quality: An end-to-end practice to ensure data is accurate and usable for their intended purpose. It provides better, cleaner data for any volume or type.
  • Data preparation: Practice of preparing data for analytics and reporting.
  • Data integration: Steps taken to combine different types of data.
  • Data Federation: Virtual data integration which allows you to view combined data from multiple sources without having to move and store the combined view to a new location.
  • Data Governance: Rules and decisions that assist in data management to ensure alignment between data strategy and business strategy. It effectively manages all critical data assets, no matter their size, type or location.
  • Data streaming: Practice of analyzing data while it’s in motion. It enables users to filter, clean and correct fast moving data before it is stored for instant, tangible results in real time and from a single interface.

Basic management approaches

We will outline below some of the basic approaches that illustrate the role of information systems in data management.

Cloud data management

The process of storing and integrating data in the cloud. This entails delivering data management services such as backup, disaster recovery, archiving, research, and analysis in a single unified cloud platform that can be used in real time.

Master data management

To provide a single reference point and avoid redundancy, an approach was utilized to identify and handle key data in a unified manner. Reference data and analytical data that support decision-making are examples of master data.

Reference data management

Reference data management, which is derived from master data management, sets allowed values that can be used by other fields. Internally or externally mandated reference data is possible.

ETL and data integration

Extract, Transform, Load (ETL) is the process of copying data from one or more sources to a destination system (usually, a data warehouse), which is ideal for high performance analysis.

Data analytics and visualization

A practice of conducting state-of-the-art data analyses to process selected data from big data sources in order to present it in a visual context. Aims to help convey the significance of such data.

Best Data Management Tools

We’ve pulled together 30 of our favorite data management tools from across these categories, which we’ve shared below.

Keep in mind that this is not our rankings, but rather a collection of tools to help you get closer to choosing the best data management tool for your needs.

1. Sage Data Business Intelligence platform

SageData Business Intelligence platform gives you the tools to holistically understand your business through your data. It turns the complicated process of collecting and analyzing Marketing Data into a seamless, intuitive experience and combines everything a business needs to be data driven.

Key features include:

  • Data collection tools used to collect data you need from external systems for analytics and processing.
  • Data Store to help you process and aggregate your data and gain insights for your business and make smart decisions.
  • Advanced analytics to develop understanding what channels bring you the best customers through better Marketing Attribution.
  • A reporting module to help visualize your data and impress your investors.

Sage Data price: free trial is available; see all pricing options.

time and data are your biggest assets

2. Amazon Web Services

AWS provides a complete suite of analytics services, enabling companies of all sizes and industries to reinvent themselves with data. It provides many of data analysis options without a cloud server, including data storage, big data analysis, real-time data, data integration, and more.

Few services offered by Amazon Web Services are as follows:

  • Amazon Athena for quering data and analyzing big data in Amazon S3 using standard SQL.
  • Amazon S3 (Simple Storage Service) for temporary data storage.
  • Amazon Glacier for data archiving and backup.
  • Amazon QuickSight for Dashboard Construction and Data Visualization.

Pricing options: vary depending on your implementation.

3. Microsoft Master Data Services

This product includes a range of services that enable users to manage a master dataset for an organization. Data can be arranged in models and updated by creating rules, including access controls to allow who updates the data.

Key services include:

  • Microsoft Azure Data Factory – a cloud ETL service for scale-out serverless data integration and data transformation. It offers a code-free interface for intuitive data creation, monitoring and management.
  • Microsoft SQL Server Integration Services (SSIS) – a platform for building enterprise-level data integration and data transformations solutions.
  • Microsoft Power BI – a service that delivers insights to enable fast, informed decisions. It helps transform data into visuals that can be shared on any device and enables collaboration through customized dashboards and interactive reports

Pricing options: vary depending on your implementation.

4. Google Cloud

Google Cloud Platform provides a broad range of tools for cloud data management. It is convenient since it comes with a workflow manager that links the different components together.

Key Google Cloud components include:

  • Analytics based on Google Data Studio for GUI (Graphical User Interface) and dashboard building.
  • Cloud Datalab for exploratory data analysis, data visualization, and building machine learning models.
  • ML (Machine Learning) Engine for advanced analysis through Machine Learning and Artificial Intelligence.
  • Google BigQuery for tabular data storage.
  • Cloud Pub and Cloud Data Transfer for the date intake and support.

Pricing options: vary depending on your implementation.

5. Informatica PowerCenter

Informatica PowerCenter is an on-premise ETL tool.

Informatica PowerCenter offers the following features:

  • Advanced connectivity and integration with all types of data sources
  • Automated data validation via script-free automated audit
  • Transformations of data including relational, XML, JSON, PDF, Microsoft Office and IoT data.
  • Metadata-based management that provides graphical views of data streams, impacts, and lineages.

Informatica PowerCenter price: available upon request.

6. Tableau

Tableau is a BI platform that can be used both in the cloud and as downloadable software. It streamlines raw data in an easy-to-understand format for data analysis.

Key features include:

  • Easily connects to data sources
  • Enables teams, partners and customers to easily access visualizations.
  • Enables unlimited data exploration with interactive dashboards
  • Creates “dashboard starters,” actionable dashboards setup in minutes with data from popular web applications
  • Creates interactive maps automatically

Tableau price: starts at $70 per user per month.

7. Blendo

Another cloud-based ETL and data integration service that connects to numerous data sources and automates the collection, detection, and preparation of data using an optimal relational schema.

Key features include:

  • Connects to numerous data sources with a few clicks, moves data to Amazon Redshift, PostgreSQL, MS SQL Server, and more
  • Loads and syncs legacy data from cloud services.
  • Automated collection, detection and preparation of data using optimal relational schema

Blendo price: starts at $150/month, depends on number and types of integrations.

8. Talend

Talend data integration software provides software to integrate, clean, hide and profile data.

Key features of Talend offerings include:

  • Graphical interface to manage multiple source systems using standard connectors.
  • Master Data Management (MDM) functionality
  • Single, consistent and accurate view of key enterprise data

Talend price: $1,170/user monthly or $12,000 annually.

9. Dataform

Dataform enables collaboration on SQL pipelines in BigQuery.

This fully managed data transformation platform helps businesses efficiently manage a variety of cloud-based data warehouse processes.

Key features include:

  • Assistance with writing SQL workflows into a collaborative IDE.
  • Offering built-in version control and integration with Github
  • Enable data teams to write data quality tests and unit tests and set alerts each time they fail to guarantee the reliability of your data.
  • Publish tables and automate complex SQL workflows in a timely fashion.
  • Running schedules to ensure your data is always up to date

Dataform price: varies and depends on the number of users and features used. A free version is available.

10. Dell Boomi

Dell Boomi Master Data Hub is the integrated platform that smartly links your applications and automates your workflows.

Key features offered:

  • Help businesses reach interoperability between external partners and internal systems.
  • Define models through visual and low-code experiences and synchronise and enrich data using the centralized hub.
  • Deployment of data models and identification of source systems to interface with.
  • Integration of system records into a consolidated repository and automatic fusion of similar records.
  • Ability to manage data and alert teams to resolve duplicate and data capture issues.
  • Manage data with real-time two-way process streams across silos.

Dell Boomi Master Data Hub price: follows a customized pricing model, and a 30-day free trial is available.

11. Profisee

Profisee Master Data Management assists companies in managing master data by cleaning, standardizing and matching source data.

Key features include:

  • Record management to standardize, clean, and match source data without any coding
  • Event management to identify data changes and distribute events to subscriber systems.
  • Integrator to federate data for international businesses, with real-time bi-directional integration.
  • Enterprise workflow to apply enterprise-wide business processes and allow administrators to manage the performance of data stewards.
  • SDK to enable the integration of custom applications

Pricing: upon request.

12. Magnitude Kalido MDM

Magnitude Reference Data Management is equipped to define and model critical business information from any domain – customer, product, financial and other.

Key features include:

  • Multidomain modeling to support operational structures, from codelists to multi-use and self-referential hierarchies.
  • Automation of governance and control of master data objects and loading processes.
  • Invalid data is systematically sent to data managers for improvement.
  • Mapping from global to local, external to internal, and specific to general mapping with no disturbance to existing elements.
  • Governance to customize workflow to control MDR business processes.

Pricing: upon request.

13. Stitch Data

Stitch Data is an ETL platform that integrates data from sources such as databases and applications into a centralized data warehouse so that users can analyze it. It has integrations for enterprise software data sources, and can receive data via WebHooks and an API as well.

Features include:

  • Pre-built with several data sources and can move data in Amazon Redshift, Amazon S3 (Simple Storage Service), Google BigQuery, PostgreSQL and other.
  • Error management and automatic resolution reporting are provided.
  • This is a managed cloud service with auto scalability and enterprise-level service level agreements.
  • An API and JSON frame that lets you transfer data to a data warehouse.

Pricing: 14-day free trial, then month/annual plan are available.

14. is a cloud-based platform for data analytics and visualization.

Key features list:

  • Easy drag and drop interface to create dashboards.
  • Secure integration with your SaaS platform, web application or online portal.
  • Login to databases or data warehouses with no server installation required.
  • A flexible API for automating dashboard creation, customizing dashboards, or configuring interactivity between your app and embedded dashboards.

Pricing: $995 – $2,700 per month for full embedded capability or white-labeling, with plans ranging from 100-1000 monthly active viewers.

Achieving integrated data management

Extracting complex data from a diverse set of data sources can be a difficult task even for experienced analysts, let alone beginners. Data management platforms are useful here to handle business processes around structured data in large organizations.

Nobody has the bandwidth to manage alone, so for those who try – this may be a sign to find a software solution that can help!

Read our article for Predictive Analytics Tools for 2023

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