One of the biggest stumbling blocks of a business is the expansion of its Database. A few problems one might have to deal with while trying to expand their Database are storage problems, complicated management issues, and difficulty in the location, sharing, and checking of isolated data. To steer growth, a company needs to have the right data management strategy and the right data management tools.

So, what are data management tools? The main objective of data management tools is to provide software that makes it easier to organize, store, retrieve, and manipulate data in different formats. These technologies guarantee its effectiveness, accuracy, and security throughout the data’s lifetime.

This article introduces you to the concept of data management and the importance and key features of data management tools for your business strategy. The article wraps up with a comprehensive list of the best tools for data management to look out for this year. 

Top 14 Data Management Tools

Now that you have seen the different categories of data management tools, let’s take a gander at a few of the best tools from each category. When selecting the data management software for your company, it is crucial to take into account all available data management tools examples. These tools will serve as a great addition to your enterprise workflow pipeline.

1. ETL and Data Integration Tools

In computing, Extract, Transform, and Load is the procedure of copying data from multiple sources into a destination system that represents the data. Data integration, on the other hand, refers to the process of combining data from multiple sources into a single destination. The next section will discuss the top ETL and data integration tools.

A) Hevo Data

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Hevo allows you to replicate data in near real-time from 150+ sources(40+ free sources) to the destination of your choice including Snowflake, BigQuery, Redshift, and Databricks. Without writing a single line of code. Finding patterns and opportunities is easier when you don’t have to worry about maintaining the pipelines. So, with Hevo as your data pipeline platform, maintenance is one less thing to worry about.

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For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.

– Juan Ramos, Analytics Engineer, Ebury

Check out how Hevo empowered Ebury to build reliable data products here.

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B) Stitch Data

Stitch Data is a Cloud-based ETL (Extract, Transform, Load) data management tool. It allows you to move data from numerous sources into a data warehouse to provide you with analysis-ready data. The salient features of Stitch Data are as follows:

  • Pre-Integrated with multiple data sources and can move data into Amazon Redshift, Amazon S3 (Simple Storage Service), Google BigQuery, and PostgreSQL to name a few.
  • It provides easy scheduling for data replication.
  • Error handling and alerting with automated resolution are provided.
  • It is a managed cloud service with automatic scaling and enterprise-grade service-level agreements.
  • An API and JSON framework that lets you push data into a data warehouse.

C) Fivetran

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Fivetran is a fully managed data pipeline with pre-built connectors that deliver analysis-ready schemas while adapting to source changes automatically. You can also check the best data pipeline tools list. Here are a few salient features of Fivetran:

  • No data limit is imposed by Fivetran.
  • The data can be moved from Point A to Point B by the Caching Layer without having to worry about storing a copy on the application layer.
  • A company’s data can be centralized by leveraging Fivetran as a part of the workflow. It can also integrate all the sources to determine all the KPIs (Key Performance Indicators) relevant to the company.
  • The caching layer can also be used to send data over a direct secure connection and provide direct integration at the same time.

D) Talend

Talend Logo

Talend is one of the best data management software that provides enterprises with data integration, integrity, preparation, and quality tools. Talend was bought by Qlik. Companies may share, clean, and alter data. Here are some features of Talend:

  • Data transformation features offered by Talend include filtering, flattening/normalizing, aggregating, replicating, looking up, joining, and temporal windowing.
  • To Live preview of sample data
  • The ability to use the same connections and graphical elements to construct batch and streaming pipelines inside the same interface
  • Capacity to enrich, clean, and standardize data in the pipeline

2. Cloud Data Management Tools

Since storage and bandwidth have become cheaper, the number of off-premise data warehousing and management solutions has increased considerably. Companies with a large amount of data to store, analyze and sift through have adopted loud-based tools for better efficiency. This has been made possible through the emergence of robust cloud data management tools in the past 5-10 years. Giants like Google and Amazon primarily dominate these fields, but with the advent of technology, many smaller companies now offer tools for customers with data needs of all sizes. Here are three prominent enterprise data management tools:

A) Amazon Web Services

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Amazon Web Services offers many tools that can be combined into a cloud data management track. It is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs on a pay-as-you-go basis. A few salient services offered by Amazon Web Services are as follows:

  • Amazon Redshift for data warehousing.
  • Amazon Athena for SQL-based analytics. 
  • Amazon Quicksight for dashboard construction and data visualization.
  • Amazon Glacier for long-term backup and storage. 
  • Amazon S3 (Simple Storage Service) for temporary and/or intermediate storage.

Amazon Web Services Pricing

The price for AWS is variable and dependent on the implementation.

B) Microsoft Azure

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Microsoft Azure’s platform provides several ways to set up a cloud-based data management system. It also provides handy analytic tools that can be used on Microsoft Azure-stored data. Microsoft Azure allows for multiple database/ data warehouse styles with a good set of tools to manage them. Here’s a list of salient services provided:

  • Standard SQL databases and virtual machine-based SQL servers.
  • Blob Storage.
  • Private Cloud Deployments.
  • NoSQL-style table storage options.
  • Azure Data Explorer (ADX) enables a user to perform real-time analysis of very large streaming data without the need for preprocessing.

Microsoft Azure Pricing

The pricing for Microsoft Azure similar to Amazon Web Services is also variable and depends on the user implementation.       

C) Google Cloud Platform

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The Google Cloud Platform offers a large set of tools for Cloud-based Data Management. It is handy as it comes along with a workflow manager that ties the different components together. Here are a few key services offered by the Google Cloud Platform:

  • Google Data Studio for GUI (Graphical User Interface)-based analysis and dashboard construction.
  • Cloud Datalab for code-based data science.
  • 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 intake of data.
  • Cloud BigTable for NoSQL database-style storage.
  • Connections to BI tools like Tableau, Looker, and Power BI.

Google Cloud Platform Pricing

Google can also connect to a large number of other data sources as well. The pricing for Google Cloud Platform depends on the implementation opted for by the user and hence is flexible.

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3. Master Data Management Tools

Master Data Management Tools allow you to integrate all the business applications of the entire enterprise from different departments into a single file. Here are a couple of Master Data Management Tools that can help you create a central point of reference for your business.

A) Dell Boomi

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With over 12,000 customers and deemed pioneers of delivering Pervasive Intelligence, Dell Boomi’s Master Data Hub leverages the power of the Cloud, providing flexibility, scalability, high agility, and automatic upgrades among many other things. A few salient features of Dell Boomi are as follows:

  • It defines models via low-code, visual experience.
  • Deploys data models and identifies the source systems that can interact with these models.
  • Similar records are automatically merged.
  • Dell Boomi governs the data with real-time bidirectional process flows across silos.
  • Dell Boomi enables Data Stewarding. This means that teams are alerted to resolve data entry issues and duplicates. 

Dell Boomi Pricing

You can get the pricing for Dell Boomi’s Master Data Hub on request.

B) Profisee

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Profisee is a Master Dataset Management Tool that builds and delivers trusted and relevant information across the business. The salient features of Profisee’s Master Data Management are as follows:

  • It cleans, matches, and standardizes source data without a single line of code.
  • Data Stewardship is enabled within the organization to manage Master Data with reliable feedback from analytics.
  • Events are distributed to subscribing systems by Profisee.
  • It acts as an integrator, as it federates Master Data for global enterprises.
  • It also provides real-time bi-directional integration.
  • Integration of custom applications is enabled.

Profisee Pricing

Profisee pricing is fixed per hour for a user.

C) Ataccama ONE

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Ataccama offers ONE, their automated database management software that can be run in the Cloud, on-premise, or in a hybrid setup. It is a collaborative curation platform that provides solutions not only for Master Data Management but also data governance. Here are a few salient features of Ataccama ONE:

  • ONE is a tool that is GDPR (General Data Protection Regulation) ready.
  • It provides connectors for a wide range of data types and sources.
  • ONE provides automated project configuration and metadata discovery.
  • The data curation, cleaning, and classification carried out are based on Machine Learning.
  • ONE is typically domain-agnostic but it does come with a set of pre-configured rules for MDM for domains like Customer, Contact, and Product.

Ataccama Pricing

The pricing for Ataccama is also available on request.   

D) Informatica

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Informatica is among the popular enterprise data tools. In addition, it offers excellent features and data integration. Because each application has a notably distinct user interface, business users find it challenging to switch between them thanks to Informatica’s increasingly diversified data management toolkit. This increases the steepness of the already lengthy learning curve and encourages users to hunt for Informatica substitutes.

Informatica Pricing

The pricing for Informatica is also available on request.  

4. Data Visualization and Data Analytics Tools

Data visualization tools allow you to view your data in a pictorial format (like graphs and charts), which makes it easier to draw coherent insights from it thus simplifying the analytical process. Here are a few handy data visualization and data analytics tools to integrate into your business model.

A) Tableau

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Tableau is a BI platform that helps people see and understand data with the belief that data analysis should focus on asking the right questions to extract meaningful insights with built-in visual practices. Salient features of Tableau are as follows:

  • It can easily connect to different data sources.
  • It allows easy access to visualizations for partners, teams, and clients.
  • It allows you to create interactive maps automatically.
  • Tableau provides unlimited data exploration with interactive and intuitive dashboards.
  • Dashboard setup hardly takes a few minutes with data from popular web applications.

Tableau Pricing

There are different plans in place for the users opting for Tableau:

  • Individuals
  • Teams and organizations
  • Customized plan

B) Looker 

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Looker is another cloud-based visualization and analytics platform. It is mainly targeted at business intelligence, data applications, and embedded analytics. This allows you to share actionable insights in real time. Here are a few salient features of Looker that set it apart:

  • You can connect directly to the databases without having to download any software in particular.
  • Easy-to-read and use dashboards.
  • Access to the dashboards is provided to everyone, not just the data scientists and analysts.
  • You can define metrics using LookML which is Looker’s data modeling language.

Looker Pricing

For Looker, the pricing depends on the scale of deployment and the number of users. For more details on the pricing, you would have to get a quote from Looker.

C) Microsoft Power BI

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Microsoft Power BI provides interactive visualizations and business intelligence capabilities with a simple interface, designed to be used by analysts and data scientists alike. Here are a few salient features of Microsoft Power BI:

  • Microsoft Power BI is a no-code data platform, that services both desktop and web clients.
  • Microsoft Power BI offers a library of pre-built connectors.
  • A simple drag-and-drop interface is provided by this tool.

Microsoft Power BI pricing

There are different plans offered by Power BI based on dedicated cloud computing and storage resources with subscriptions.   

Data management and the role of information systems

Big Data, real-time analytics, data modeling, and the general wise use of information are among the difficulties that information systems and data management together assist to address. The issue of applying the best practices that result in the efficient use of information systems is brought about by the rise of massive, diversified, and publicly available data. This is why it is crucial to establish an information-centric perspective.

The two main components that comprise its foundation are data and information, which are governed by information systems and data management. 

Types of Data Management Products

  • Management of Product Information (PIM): PIM tools automate and centralize the administration of product information across many supply chains, sales channels, and other channels, making them perfect for manufacturers and merchants.
  • Management of Master Data (MDM): MDM tools are made to manage vital corporate data, including personnel, customer, and operational information. By centralizing, synchronizing, and cleaning data, they guarantee consistency and quality.
  • Data Modeling: By establishing conceptual models and standards for data consistency and quality, tools in this category aid in the adaptation of data for storage. 
  • Data Warehouse (DW): While DW products offer data storage options, they do not come with specialized data management procedures. They are frequently connected to already-existing hardware and are just used for data storage. You might require separate tools for other procedures, such as data quality analysis, metadata management, and data security.

Challenges of Data Management

This article so far has given an outlook of some of the best data management tools out there, their features, and pricing. But data management is not without its challenges either. The data management challenges usually stem from the ever-increasing volume of data. Here’s a list of things organizations might face while trying to leverage data management tools as a part of their workflow:

  • Unclear goals and objectives: A significant challenge with data management is the lack of clarity around what it is that an enterprise is looking for from the processed data. In the absence of a clear goal for acquiring the right data, and analyzing it to facilitate data-driven business decisions the true potential of the data management tools cannot be exploited.
  • Meeting compliance requirements: Organizations must review their data and practices regularly to keep up with the constantly changing compliance regulations to ensure that everything is aligned with the new or updated requirements.
  • Multiple data storage options: Data is stored across multiple platforms making it difficult to analyze since it isn’t available in a single format or repository. Data therefore must be transformed into a unified format to allow for easy analysis.
  • Sparse utilization of data management: With massive amounts of data to account for, companies find it challenging to fully understand the whereabouts of the enterprise’s data, the amount of data, how to use it, and where to put it to use.  
  • Extracting value that solves a specific problem: The main challenge lies in making sense of data collected from numerous sources. This involves understanding that data management and data analytics go hand-in-hand, to extract maximum value from data in the form of actionable insights.  


This article helps you master data management tools and why is it important for enterprises to have a solid data management strategy in place. It then shows you the different types of data management tools available in the market, along with a few key features that set them apart from the rest. In the end, the decision to leverage one or more of these tools lies with you depending on your requirements. You need to test data management tools and pick the best for your use case.

Extracting complex data from a diverse set of data sources can be a challenging task, especially for a beginner. Building an in-house ETL pipeline will consume a significant amount of time and resources.

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Share your understanding of Data Management Tools in the comments below!

Content Marketing Manager, Hevo Data

Amit is a Content Marketing Manager at Hevo Data. He enjoys writing about SaaS products and modern data platforms, having authored over 200 articles on these subjects.

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