Data Management Tools: Top 13 Tools

Data management tools have become increasingly crucial since huge amounts of data are being generated every day. A study carried out by Invesp based on a data-driven marketing survey showed that 87% of marketers think that data is an underutilized asset in their company.¹ This is understandable because, with so much data coming in on a daily basis, it can be difficult to keep up with records and data, especially without the right knowledge of efficient management techniques.

It is not impossible to manage large amounts of data, and having a solid understanding of data management technologies is essential for growth, particularly in this data-driven era. Fortunately, there are several solutions available, ranging from straightforward, user-friendly technologies to more complex ones depending on the capabilities of each firm.

Keep reading this article for more insight into data management and data management tools. This article will also analyze some data management tools. We will get you acquainted with the different types, and discuss their pros and cons.

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What Is Data Management? 

Data management is simply the act of managing data. It includes all processes involved in the lifecycle of a piece of data from collection to storage to manipulation of this data for different purposes and finally retirement of the data.

Large data sets are an unending source of irreplaceable value, and with the right data management procedures, organizations can access and extract crucial information from them as needed to advance any business.

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What Are Data Management Tools? 

 Every day, organizations are bombarded with enormous volumes of data from several sources. Data management tools are used to manage the data and maximize the benefits that may be obtained from it.

Data management tools can be utilized for many purposes. 

• To store data in a secure manner
• To organize large volumes of data 
• To eliminate redundant data 
• To provide employees with access to the right kind of data when needed etc.

A good knowledge of the right kind of data management tools to serve a company’s needs will go a long way toward the functional efficiency of a company.

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Features Of Data Management Tools

There are certain features that data management tools must have to function effectively.  

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Must Be Flexible 

As stated earlier, there is a large volume of data made available and this amount will only keep getting increasingly larger. To make good use of all this data, it must remain flexible.

Data can be stored in data warehouses or lakehouses. Data warehouses are storage platforms that have some kind of structure to how data can be stored meanwhile lakehouses store data in a more fluid nature. The flexibility that data lakehouses offer means there is infinitely more opportunities for relevant information to be drawn from this data. 

Data that is not flexible is limited in that there is a minimal amount of ways the data can be used for our benefit.

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Data Access Optimization 

Another important feature of data management tools is data access optimization. With this feature, organized data can be accessed and retrieved from a central location in a format that can be used by various teams in the company.

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Must Be Secure 

Data management tools must offer security. Some data collected contain private information and effective data management tools must be able to protect these data  from data losses and breaches by using effective authentication and encryption.

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Global Work Facilitation

Global work facilitation is an important cloud-based feature for data management. It allows users in different parts of the world to gain access to data when permitted. As a result, remote workers can share and receive information in international locations to manipulate data according to their needs.

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Types of Tools Essential To Effective Data Management 

In light of the fact that data is so dynamic and may be altered in a number of different ways to serve the desired purpose, there are many different types of data management solutions accessible.

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Data Transformation Tools 

Data transformation tools are tools that can assist you in the process of transforming data from one format to another to be used for different purposes through an automated process.

It is important however to know that picking the right type of data transformation tool is crucial because if done wrongly, can lead to loss of data during the process.

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Cloud Data Management Tools

Cloud data management tools are tools that give companies the opportunity of storing huge amounts of data entirely in the cloud. Through these tools, organizations can perform data management functions like backup data, search for data, data recovery, and data analysis.

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Reference Data Management Tools 

Reference data management is a subclass of master data management. They help to assist in organizing and bringing uniformity to your data. They make it possible for business users to quickly combine data and comprehend the overall condition of the company.

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ETL And Data Integration Tools

Extract Transform and Load (ETL ) and data integration tools help move data from different sources to where it is needed (a prescribed database or datawarehouse). 

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Master Data Management Tools

Master data management tools are used to organize master data (product, supplier, and customer information for a business)  from business applications in a uniform manner to make it available for other business uses. 

Master data management tools help to solve the problem of managing business data as improper management of this data can negatively impact a company.

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Data Transformation Tools Examples 

Dataform 

Dataform is a data management platform that can transform your data in different data warehouses (BiqQuery, Snowflake, Redshift, etc) from its raw form to different tables and views to be analyzed for different purposes.

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Pros

It can run arbitrary SQL operations.

It can define tables and views to be created in the data warehouse.

It can be scheduled to ensure updated data. 

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Cons

Dataform is a tool that will require the help of qualified data analysts and engineers and cannot be used by individuals without proper knowledge of the tool.

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Price

The price is dependent on the features used. However, a free version is available.

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AirFlow

Airflow is a platform that utilizes python code manage workflows. This platform offers the ability to develop, schedule, and monitor workflows and can connect to many technologies.

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Pros

Airflow is a dynamic tool. It is configured in python code and can allow dynamic pipeline generation. 

Airflow is Extensible that is =, it can connect to numerous technologies and can be adjusted to your preferable environment.

Highly scalable.

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Cons

Airflow has no native support for Windows. 

Python coders are required before Airflow can be used. 

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Price

It is a free and open-source tool.

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Cloud Data Management Tools Examples

Panoply

Panoply is a cloud-based tool that simplifies the data management process. Panoply is based on ETL that is, data from its source can be loaded into a data warehouse through in-built data source integrations. 

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Pros

This tool does not require knowledge of complicated code to use. 

Panoply allows scheduled updates to your data hence, making your data ready to be used for immediate analysis when required. 

It has connections to major analytical tools.  

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Cons

There are not many cons to this tool. Users, however, have complained that the interface is not very user-friendly. 

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Price

The price of this tool ranges from about  $400-$1700 per month depending on the package required. 

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Google Cloud 

Google cloud offers a wide range of data management tools. For example,  Google Big Query for tabular data storage, Cloud BigTable for NoSQL database storage, Data Studio for GUI-based analysis, etc.

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Pros

Google cloud has plenty of scaling options. 

It offers a wide range of tools. 

Uses machine language for advanced analyses. 

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Cons

Requires a lot of effort to become accustomed to these tools.

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Price

The pricing for this tool varies.

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Amazon Web Services

Amazon web service is a cloud-based service by Amazon that offers a vast array of services. This tool is best utilized by larger businesses with vast amounts of data and access to technical know-how for efficient management.

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Pros

Amazon web services are cost-effective. 

It is highly scalable. 

It has a variety of databases and tools. 

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Cons

AWS tools require high-level technical skills. 

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Price

Amazon employs a pay-as-you-go service. This implies that you pay for the service you require for however long you require it. Hence, payment is subject to each user.

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Reference Data Management Tools Examples 

Collibra 

Collibra is a tool that utilizes a single integrated platform to automatically manage workflow and analyze data for trusted insights. This tool makes data more accessible and businesses can draw useful information to be implemented for business growth.

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Pros

Collibra is a user-friendly tool. 

This program enables effective data management and organization across a wide range of organizations.

This program offers reliable technologies that may be utilized to acquire continuous data solutions and accomplish data governance objectives for enterprises.

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Cons

Implementation costs of this tool can be pricey.

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Price

Price is received upon request. 

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Reltio Cloud 

Reltio Cloud is a Software as a service (Saas) data management platform. Reltio cloud is able to store large volumes of data from different sources, refine them and establish relationships between them using graph technology and machine learning. 

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Pros

Reltio is user-friendly. 

Supports the use of both structured and unstructured data. 

This tool performs tasks with great speed and agility. 

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Cons

This tool will require a steep learning curve to master its functionality.

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Price

Retlio offers a free trial. Pricing is also available when requested.

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ETL And Data Integration Tools Examples 

Stitch Data 

Stitch is an ETL and data integration tool that effectively loads data from multiple sources into a warehouse to be used for different purposes. 

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Pros

This tool does not require coding knowledge.

Stitch uses API and JSON framework allowing users to push data into a warehouse. 

Data can be replicated from any source to any destination. 

Stitch can build and commercially support custom integration for your business.

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Cons

Some users have complained that its UI is not very user friendly.

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Price

Stitch prices range from $100- $500 per month depending on the package and services required.

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Talend 

Talend is a data management tool that integrates, governs, and preserves data integrity in a single platform. 

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Pros

Talend offers good speed and agility. 

Uniform view of key enterprise data. 

Talend has Master Data Management functionality. 

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Cons

This tool requires a good amount of skill to use it well.

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Price

Talend prices are received upon request. 

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Informatica Power Center 

Informatica power center is a great on-premise ETL tool. Data from different sources and in different formats can be integrated and centralized to be accessed and used by businesses to inform business projects.

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Pros

Does not require code to use this tool. 

Utilizes connectors to integrate data from different sources.

This tool is highly scalable. 

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Cons

This tool requires a steep learning curve.

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Price

The price is given upon request.

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Master Data Management Tools

Dell Boomi

Dell Boomi is a platform that allows the movement and arrangement of data on applications or in the cloud. It mitigates the flow of data over systems, people, and applications by building cloud-based integration processes known as Atoms. 

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Pros

Boomi is easy to use and understand. 

It allows users to build integrations through the drag and drop feature quickly. 

Access to already built standard integration for download by users. 

Boomi has a scalable and secure cloud-native architecture. 

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Cons

Despite its easy-to-use nature, Dell Boomi is a pricey tool.

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Price

Available upon request. 

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Profisee 

Profisee is a data management tool that simplifies the process of managing and governing data across multiple data silos. This is important because data drawn from multiple sources is often inconsistent, some data may be lost in the process hence, making it unreliable to draw conclusions from it. 

Profisee utilizes Microsoft intelligent data platform integration to create more reliable data analysis for effective decision-making.

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Pros

Profisee is easy to use and easy to configure.

Efficient data cleaning without any coding.

Profisee allows scalability of your data.  

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Cons

Despite its user-friendly nature, users will still experience a learning curve. 

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Price

The price is given upon request. 

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SAP Data Management 

SAP data management tool helps in the management of enterprise data by storing data in a centralized and accessible format for all departments of a business so that businesses are equipped to make strategic data-driven decisions. SAP helps to expedite workflow, and increase productivity and efficiency in the workplace.

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Pros

SAP offers the effective movement of master data to target destinations.

SAP is a flexible tool that enables business owners to create their own guidelines within the SAP structure.

SAP has multiple analytical features such as performance evaluation and decision making.

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Cons

This tool requires skill to be able to maximize its potential as using it can be complex.

SAP is also an expensive tool. 

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Price

The price of this tool is available when requested. 

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Choosing The Best Data Management Tool For You 

It's crucial to select the right data management tool for you. There are a few things to think about in order to make this procedure simpler.

• Ensure your data management tool can integrate with other software applications such as your CRM and email marketing system
• Make sure it is scalable that is, your data management tools have the capacity to expand as your business grows and more data is accumulated 
• You should purchase a tool that is affordable for you. It would not be useful to purchase an expensive tool with features that you do not require
• Ensure that your data management tool of choice promises the security of your data as data may house private information and security breaches of such information could be costly to your company 

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Final Thoughts 

The notion that the future will be data-driven is not unreasonable. Huge amounts of data are becoming more readily available every day, and people, small enterprises, and large corporations have discovered that careful analysis of this data is a valuable asset.

Different data management tools are available to be harnessed for proper data management. practices. Using these tools effectively is the next step you need in your business strategy to make lucrative business decisions.

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FAQ 

What are some data management best practices?

• Have adequate data storage that works for your business 
• Catalogue and file your data efficiently 
• Implement metadata for different data sets 
• Ensure you have different levels of documentation for your data
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What’s the difference between Data Management and Data Governance?  

 Data management involves the process of creating and executing policies, structures and procedures to manage the data lifecycle needs of a business. 

An organization's data management is governed by a data governance body, which also includes enforcement, monitoring, and decision-making powers.

Data Management focuses on making data valuable while data governance focuses on the safety, reliability, and trustworthiness of data. 

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Sources

1. Shukairy, A. (2022, April 25). The importance of data driven marketing – statistics and trends. Invesp. Retrieved from https://www.invespcro.com/blog/data-driven-marketing/ 

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