Friday, February 23, 2024

10 Best Data Management Tools

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Data management tools enable organizations to prepare and deliver analysis-ready data for strategic decision-making, business intelligence (BI) initiatives, machine learning (ML) applications, and more. The software in this category consists of platforms, services, and tools that ensure accuracy, consistency, security, and compliance throughout the data lifecycle.

We evaluated the top data management solutions currently on the market to see how they compared on core and enterprise features, customer support, integrations, vendor profile, and price. Here are our picks for the top data management tools of 2023:

Featured Partners: Data Analysis Software

Top Data Management Software Comparison

The top data management tools of 2023 range from leading open source platforms to highly-specialized data management platforms for research and development and other related research applications.

Core Features Enterprise Features Support Integrations Pricing
Astera ★★★★½ ★★★★½ ★★★★★ ★★★★ ★★★★
Qlik Talend ★★★½ ★★½ ★★★½ ★★★ ★★★★
Azure Data Factory ★★★★½ ★★★★½ ★★★★½ ★★★★★ ★★★★½
Ataccama ONE ★★★★½ ★★★★½ ★★★★★ ★★★½ ★★★
DataHub ★★★★★ ★★★★★ ★★½ ★★★★★ ★★★★★
Collibra ★★★ ★★★★ ★★½ ★★★½ ★★★
Atlan ★★★★ ★★★ ★★½ ★★★★★ ★★★
Amundsen ★★½ ★★★ ★★★★½ ★★★★★
Alation ★★★ ★★★½ ★★ ★★★½ ★★★
Dotmatic ★★★★★ ★★★★ ★★★★ ★★★ ★★★½

Astera icon.

Astera

Best for Support

Overall Score: 4.38
Core Features: 4.5

Enterprise Features: 4.5
Support: 5
Integrations: 4
Pricing: 4

Astera develops an end-to-end data management platform that streamlines multiple stages of the data management lifecycle, from extraction and integration to data quality improvement and preparation for analysis and decision-making. The solution unifies various data automation and ML features in a single interface.

Astera 10.3 interface.
Astera 10.3 interface.

Pricing

  • $1,200 per license, per year
  • Free trial available

Features

  • Zero coding functionality enables non-technical users to manage complex data processes with minimal IT or specialist intervention
  • Various transformations and functions for manipulating data built-in
  • Comprehensive data quality features for ensuring data accuracy, reliability, and completeness

Pros

  • Strong AI/ML features, including AI-powered data extraction tool
  • Offers an integrated application programming interface (API) management tool

Cons

  • Large datasets can cause significant latency in workflows
  • Various reported performance issues and lack of debugging tools

Qlik icon.

Qlik Talend

Best for Low-Code/No-Code Management

Overall Score: 3.30
Core Features: 3.5

Enterprise Features: 2.5
Support: 3.5
Integrations: 3
Pricing: 4

Qlik’s recent acquisition of Talend brings two data management powerhouses together in a unified solution for continually ingesting enterprise data into popular data repositories in real-time. The Qlik Talend solution provides data integration, data quality, and data governance in a single, low-code platform.

The Talend interface.
The Talend interface.

Pricing

  • $200 per month for 10 full users
  • Free trial available

Features

  • Universal solution for continually ingesting enterprise data into popular data repositories in real-time
  • Comprehensive toolset for tracking, maintaining, and protecting data accuracy at all stages of the data lifecycle

Pros

  • Automates the design, creation, and ongoing updating of data warehouses and data lakes on any cloud platform
  • Allows for the creation of secure APIs and streamlined integration workflows

Cons

  • Significant learning curve
  • Real-time and batch processing can be slow and prone to failures

Microsoft icon.

Azure Data Factory

Best for Microsoft Ecosystem Integration

Overall Score: 4.65
Core Features: 4.5

Enterprise Features: 4.5
Support: 4.5
Integrations: 5
Pricing: 4.5

Microsoft’s Azure Data Factory is a cloud-based data management service that focuses primarily on data integrations. By connecting and collecting, transforming, and publishing data for BI applications, Azure Data Factory is an ideal data management solution for Azure BI and other Microsoft cloud-related services.

Azure Data Factory UI.
Azure Data Factory UI.

Pricing

  • Based on pipeline task or type (for example, orchestrations cost $1 per 1,000 runs
  • Free trial available

Features

  • Integrates with myriad data and processing sources, including SaaS services, databases, file shares, and FTP web services
  • Supports external activities for executing transformations on compute services like HDInsight Hadoop, Spark, Data Lake Analytics, and ML

Pros

  • Streamlined to work with broader Azure ecosystem
  • Mapping data flows gives users a no-code, visual method for designing data transformations
  • Unified monitoring dashboard provides a holistic view of all data pipeline activities

Cons

  • Configuring incremental data loads is a complicated manual effort
  • Does not offer data exploration functionality

Ataccama icon.

Ataccama ONE

Best for Core Features

Overall Score: 4.13
Core Features: 4.5

Enterprise Features: 4.5
Support: 5
Integrations: 3.5
Pricing: 3

The Ataccama ONE platform unifies data governance, data quality, and master data management (MDM) under a single interface. The solution offers a range of AI features as well as support for both hybrid and cloud environments.

Ataccama ONE UI.
Ataccama ONE UI.

Pricing

  • $90,000 for one year
  • Free trial available

Features

  • Master data management (MDM) features can easily check dataset contents before using it
  • Comprehensive data profiling, cleansing, enrichment, and lineage tracking capabilities
  • Fully automated data catalog

Pros

  • Strong integration with other solutions allow for complete end-to-end data lineage
  • Easy-to-use, intuitive dashboard/console

Cons

  • Limited connectivity to downstream systems
  • Large datasets can result in data reporting and movement issues

DataHub icon.

DataHub

Best for Open Source

Overall Score: 4.15
Core Features: 5

Enterprise Features: 5
Support: 2.5
Integrations: 5
Pricing: 5

Co-developed and maintained by Acryl Data and LinkedIn, DataHub’s open-source metadata management platform is a powerful solution for ensuring that information can be integrated, accessed, shared, linked, analyzed, and maintained across an organization’s data estate.

The DataHub UI.
The DataHub UI.

Pricing

  • Free (open source)

Features

  • Unified search across databases, data lakes, BI platforms, ML feature stores, orchestration tools, and more
  • End-to-end lineage tracing across platforms, datasets, ETL/ELT pipelines, charts, dashboards, and beyond
  • 360-degree view of data entities

Pros

  • Open-source solution allows for granular customizations
  • Large community of contributors and supporters

Cons

  • Early release solution—may need more battle-testing
  • Steep learning curve

Collibra icon.

Collibra

Best for Analytics Tools

Overall Score: 3.48
Core Features: 3

Enterprise Features: 4
Support: 2.5
Integrations: 3.5
Pricing: 3

Collibra offers a data intelligence cloud that enables organizations to extract more value from their data assets. The data management platform incorporates AI governance and other powerful data analytics features—many of which came from its recent acquisition of Owl Analytics.

The Collibra UI.
The Collibra UI.

Pricing

  • Around $100,000 or $0.898 per hour (for a large AWS EC2 instance)
  • Free trial available

Features

  • Stewardship management allows for the assigning of data roles and responsibilities
  • Centralized policies consolidate the creation, reviewing, and updating of data policies

Pros

  • Data catalog provides a comprehensive view of enterprise data assets, including definitions, owners, lineage and relationships
  • Strong automated data governance features

Cons

  • User interface can be challenging for new users
  • Cost-prohibitive for smaller organizations

Atlan icon.

Atlan

Best for Leading Data Warehouse Integrations

Overall Score: 3.53
Core Features: 4

Enterprise Features: 3
Support: 2.5
Integrations: 5
Pricing: 3

Atlan’s data catalog solution gives organizations a centralized repository for managing their data assets and provides a systematic, organized way for data professionals and business users to find, access, and manage the organization’s data.

The Atlan UI.
The Atlan UI.

Pricing

  • Starts at $2,500 per month
  • Free trial available

Features

  • Comprehensive data governance features and data lineage capabilities (for example, column-level)
  • Data collaboration features that support various data stakeholders across the organization
  • Extensive integrations with Snowflake, Databricks, Amazon S3, Tableau , Power BI, and Redshift, to name a few

Pros

  • Built on an open-source, customizable codebase
  • Intuitive interface simplifies data management tasks

Cons

  • Support and documentation are lacking
  • Commercial version pricing falls on the high-end

Amundsen icon.

Amundsen

Best for Data Search and Retrieval

Overall Score: 2.83
Core Features: 2.5

Enterprise Features: 3
Support: 1
Integrations: 4.5
Pricing: 5

Created by Lyft, Amundsen offers a data discovery and metadata management platform geared for data analysts, data scientists and engineers. The solution features a page-rank style search based on usage patterns and data resource indexing of tables, dashboards, streams, and more.

The Amundsen UI.
The Amundsen UI.

Pricing

  • Free (open-source)

Features

  • “Google-like” search interface for data
  • Open-source data discovery and metadata engine

Pros

  • Widely-used platform for data discovery and metadata management
  • Offers several modern deployment options (e.g., Docker, EC2, Kubernetes)

Cons

  • Lacks fine-grained access control
  • Requires technical expertise to configure and deploy, with support lacking

Alation icon.

Alation

Best for Data Source Connectors

Overall Score: 2.8
Core Features: 3

Enterprise Features: 3.5
Support: 2
Integrations: 3.5
Pricing: 3

Alation’s data intelligence platform has a large footprint across all industries, from banking and finance to retail and government. The solution allows organizations to centralize their data’s context, rules, policies, definitions, and relationships via a single source of reference.

The Alation UI.
The Alation UI.

Pricing

Features

  • Comes with a powerful behavioral analysis engine
  • Strong search feature with filtering

Pros

  • Over 70 native data source connectors, including Microsoft Azure data and analytics services
  • Easy-to-use interface

Cons

  • Does not provide end-to-end data lineage
  • Setting up connectors can be difficult

Dotmatics icon.

Dotmatics

Best for Specialized Data Management

Overall Score: 3.90
Core Features: 5

Enterprise Features: 4
Support: 4
Integrations: 3
Pricing: 3.5

Dotmatics is a data management platform focused specifically on science, research, and development use cases and environments. The solution simplifies collecting and processing of instrument data and enables non-technical users to easily glean critical insights directly from their data.

The Dotmatics UI.
The Dotmatics UI.

Pricing

  • Around $20,000 per year
  • Free trial available

Features

  • Data tools uniquely designed to power many different R&D workflows
  • Production-quality scientific visualization tools

Pros

  • Digital science platform provides the first true end-to-end solution for managing scientific R&D data
  • Strong data management and collaboration capabilities

Cons

  • Difficult to configure and implement
  • Geared specifically for sciences and research (for example, drug discovery) data management use cases

Key Features of Data Management Software

While your data management feature priorities may vary depending on your unique requirements and environments, you should generally keep the following features top-of-mind, with a focus on the tools that align with your organization’s current needs and future growth.

Data Security and Compliance

Be sure to select a data management tool that offers robust security features, such as encryption, access controls, and audit trails. Additionally, ensure that the software complies with relevant data protection regulations and industry standards applicable to your business.

Data Quality and Governance

Maintaining high-quality data is essential for accurate analysis and decision-making. The software should include features for data cleansing, validation, and enrichment. Additionally, strong governance tools help ensure data accuracy, consistency, and adherence to organizational policies.

Scalability

Your organization’s data needs are likely to grow over time. Make sure the software can scale seamlessly to handle increasing amounts of data without sacrificing performance. This includes both storage capacity and processing capabilities.

Data Integrations

Your data management software should be able to integrate with various data sources and formats—this includes compatibility with different databases, file types, and data protocols. The ability to connect and consolidate data from diverse sources is crucial for gaining comprehensive insights.

UI Design and Usability

A user-friendly interface is essential for ensuring that all members of your data team can effectively use the software. Look for a solution that provides an intuitive dashboard, easy navigation, and clear reporting tools. Accessibility is also critical; the software should be accessible from various devices and locations to accommodate remote teams.

How to Choose the Best Data Management Software for Your Organization

The data management solutions in this list range from leading open-source platforms developed or released by other large organizations (Linkedin’s DataHub and Lyft’s Amundsen, for example) to highly-specialized data platforms to support research and science (Dotmatics).

In many cases, choosing the best data management software for your organization hinges on the composition of your current data estate—for example, if your data infrastructure is hosted in Microsoft Azure’s cloud, then Azure Data Factory will likely be your go-to data management tooling option.

Finally, pay close attention to the criteria we used to evaluate this software, detailed in the next section. These are the key requirements common to most enterprise users for data management software and will help you narrow down the list to those solutions that best meet your particular needs.

How We Evaluated Data Management Software

To evaluate the leading data management tools, we analyzed each solution across the following key criteria.

Core Features | 25 percent

For core features, we analyzed AI/ML options, the quality of dashboards and visualizations, data workflow management and collaboration features, and analytics tools.

Enterprise Features | 20 percent

Critical enterprise features include multi-language/multi-region availability, hybrid data options, data privacy and compliance controls, data estate management tools, and the cadence and regularity of feature enhancements.

Support | 15 percent

We evaluated live chat options, phone support, email support, premium support, and the quality of self-help items (for example, documentation and knowledge base) in gauging the offering’s support resources.

Integrations | 10 percent

Some evaluation measures related to integrations include API availability and quality, depth of ecosystem, developer resources, and available plugins and libraries.

Vendor Profile | 20 percent

In evaluating vendors, we looked at the breadth of their software suites, business type, customer base, length of time in business, and such reputational factors as data breaches, privacy incidents, product roadmaps, and acquisitions.

Pricing | 10 percent

In terms of pricing, we looked at the availability of a free trial, overall cost, pricing tiers, and discounted add-on/option prices and upgrades, if any.

Frequently Asked Questions (FAQs)

What is data management software?

Data management software is a set of tools and applications designed to efficiently store, organize, and retrieve data.

Why is data management software essential for organizations?

Data management software is crucial for organizations, as it helps maintain data accuracy, ensures compliance with regulations, and facilitates informed decision-making.

Does data management software help improve data privacy and security?

Data management software employs security features such as encryption, access controls, and regular backups to safeguard sensitive information. These measures help prevent unauthorized access, data breaches, and ensure the integrity of the stored data.

What role does data management software play in ensuring data quality?

Data management software includes features for data cleansing, validation, and normalization. These processes enhance data quality by identifying and rectifying errors, inconsistencies, and redundancies, ensuring that organizations work with reliable and accurate information.

Can data management software assist in regulatory compliance?

Yes, data management software often includes features to help businesses comply with various data protection regulations. It allows organizations to track and manage data usage, implement privacy controls, and generate audit trails, aiding in adherence to legal requirements.

How does data management software support scale with my organization’s data usage/needs?

Scalability is a key feature of data management software, enabling businesses to handle increasing volumes of data as they grow. These tools can adapt to the evolving needs of an organization by providing flexible storage options, efficient data processing, and seamless integration with other business systems.

Bottom Line

In short, a competent data management software platform can provide your organization with agile and resilient mechanisms for managing vast amounts of information. By streamlining data processes, mitigating risks, and enhancing data quality, data management tools empower firms to make informed decisions, comply with regulations, and ultimately thrive in today and tomorrow’s data-driven landscapes.

Read 10 Best Data Mining Tools for 2023 to learn more about the software enterprise organizations use to find patterns in massive volumes of data to fuel decision-making.

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