What is Data Management and Why is it Important?

Daniel Attoe
Daniel Attoe

Updated · Sep 21, 2022


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Data is an essential business tool. In fact, it is the most essential tool, according to The Economist. With the access companies have to it in 2022, terms like big data are no longer buzz words. 

All kinds of businesses – from corner coffee shops to giant multinationals – now understand the importance of data for insights and efficient business practices. 

They also know that it takes some processing to turn all that information into something usable. 

With that in mind, what is data management? Why is it so essential for organizations? And what are the tools that companies can invest in to manage their data properly?

I will give you all the answers and more.

But first, let’s begin with the basics. 

What Is Data Management

Data management is the practice of ingesting, storing, organizing, protecting, and maintaining an organization’s data. Effectively, it sets the stage for companies to analyze the data they collect.

Nowadays, organizations have access to a staggering amount of data

It helps companies make better decisions and solve issues faster. It provides a snapshot of customer behaviour and trends, which is essential for understanding your clients and making accurate business decisions.

But there’s an issue.

Unstructured data is of little use. Organizations must make sense of the massive amounts of data somehow. It’s not the easiest task, with 95% of companies admitting that data management presents a problem.

However, we cannot overemphasize the importance of carrying out this practice. Strategic and effective data management helps companies become more organized and productive. On top of that, it can be a huge money saver. 

The data management process involves several aspects. 

Properly combining and implementing them ensures that data is well-organized and readily available. It also guarantees its accuracy.

Functional data management helps organizations make full use of this valuable asset.

Types of Data Management 

As I mentioned earlier, there are several disciplines involved in data management. 

Let’s take a closer look.

Master Data Management

It’s not uncommon for businesses to deal with fragmented, duplicated, and outdated data. Corrupted data becomes redundant or conflicting and is of little use.

Master data management is what solves the issue.

It’s a core discipline under data management that ensures uniformity, accuracy, and consistency in an organization’s data assets.

This aspect involves creating a single “master record” for identifiers and other essential data elements across various sources.

Properly done, master data management consolidates data from multiple sources. It deduplicates and reconciles an enterprise’s data, ensuring it’s consistent across the board. 

This helps verify the trustworthiness of information.

Data Modeling

Data modeling is the process of visualizing an information system. It shows the types of data in it, the connections between them, and their flow within the system.

In essence, data modeling creates a simple illustration of a complex application system. It displays data objects, their groupings, relationships, and the rules that guide them. It also uses text and symbols to present the way data flows.

Data models are essential for database management. They provide a holistic representation of the data stored within a database.

Data modeling is a critical step in designing the data warehouse.

Data Security

Security breaches come with severe consequences for any organization. To avoid corruption, data theft, and other malicious activities, a company must protect its data from unauthorized access. 

Data security comprises a range of processes. They all come down to ensuring that a company’s databases are safe from cyber threats, human error, insider threats, and other causes of data breaches. 

Data security involves evaluating various threats and reducing the risks of the data management process.

To achieve this, organizations deploy cybersecurity tools and practices. They use encryption, redaction of sensitive data, masking, and access restrictions.

Data Architecture

Data architecture is the system responsible for how companies collect, store, and integrate data. It’s a structure that establishes efficiency in the handling and manipulation of data, showing the data assets and their flow within the system.

Don’t mix up data architecture and data modeling

The latter applies to the rules and relationships connecting the elements in a database. On the other hand, data architecture is a more encompassing view of how an organization handles data sorting, integration, and storage.

Data architecture is key for other aspects of data management. It provides a structure for developing and executing data governance. And it’s just as important for ensuring data privacy and supporting data warehousing.

Data Governance

Data governance is the system of authority and control over the principles of data management in an organization. 

It defines who controls a data asset and its use, plus the extent of that control. It also describes the situations where specific actions involving data can occur.

Data governance encompasses roles, responsibilities, policies, standards, and processes that rule the availability and usability of data.

Efficient governance is crucial for strategic data management. Professionals who work with big data, for instance, would benefit from having clearly defined roles. It significantly reduces data misuse and inconsistency.

Moreover, data governance plays a significant role in keeping organizations within the boundaries of regulatory compliance. 

Data Warehousing

Data warehousing is the process of collecting data from multiple sources and storing it electronically to help provide business insights.

The data warehouse is essentially a library or repository of historical data. It’s an integral part of data management and analytics and supplies real-time information for business intelligence activities.

This data can come from various sources, both internal and external. The process consists of pulling it in, transforming it, and sorting it properly.

For quality assurance purposes, the sources are also added to the warehouse. 

Don’t confuse a data warehouse with a database, even though both concepts appear similar. A warehouse can store assets from different databases, easing the analytic process. 

Data Catalogs

With the massive amount of data available today, professionals may have to wander through a maze before reaching the correct data.

Fortunately, there’s a way out.

A data catalog is a detailed, organized inventory of data assets within an organization. It helps users quickly access what they need. 

Data catalogs use metadata — data describing other data — together with data management tools to create a searchable inventory for an organization’s data. 

Catalogs usually come with tools for data curation to collect more metadata, aiding the identification and usage of data further.


ETL is short for extract, transform, and load.

It’s a data integration system that involves all three steps in blending data from different sources to a single location. It also helps in data migration across different sources and destinations. 

ETL is a central concept to data warehousing.

Here’s how it works:

  • Extract: Here, the data is obtained from its source. This can be a database, application software, data warehouse, analytical tool, or other sources.
  • Transform: The data undergoes several transformative processes. These could include data cleansing, standardization, deduplication, verification, combination, sorting, and others. This is the most important step.
  • Load: The final stage involves loading the now-transformed data into its new destination. This can be a database or a data warehouse.

While each is a separate process, a single data management tool can handle every step simultaneously.

Data Pipelines

Organizations generate vast amounts of data from various sources. It may not always be possible to analyze that data at the collection point. In such situations, it’s important to move the data between different systems. 

This process involves several steps, and the data pipeline is their aggregate.

Fundamentally, a pipeline is a data management technology that defines series of necessary actions. It covers ingesting raw data, moving it to target storage or analysis destination, and the sub-processes in between. 

These sub-processes depend on the data’s use case and destination. 

A well-designed data pipeline accelerates the whole process by collecting, transforming, moving, and integrating data.

Data Preparation

Often, initially gathered data is too raw to provide any valuable insights. That’s because it regularly comes with errors, missing values, and inaccuracies.

And so, it must undergo data prep. 

Data preparation involves gathering, combining, and manipulating raw data into a form that can undergo analysis. The procedure involves cleansing, profiling, validation, formatting, and transforming the data before analysis.

This stage can be pretty tedious. Still, we cannot downplay its importance if an organization wants clear and error-free insights. 

There are enterprise data management tools that ease the process by automating and accelerating data preparation.

Data Stewardship

Data stewardship is the practice of managing and overseeing an organization’s data assets. It involves ensuring the accessibility, usability, and trustworthiness of the available data.

The data steward is responsible for managing data quality. It ensures the data is usable and trusted and handles core metadata, amongst other tasks. 

All of these must be carried out under data governance guidelines. Data stewardship is a vital part of the governance process. The latter is responsible for establishing high-level policies around controlling and using data, while the former ensures their application.

Why Is Data Management Important? 

At this point, we’ve discussed the various ways to manage a company’s data.

Now, let’s touch on the importance of data management and how it benefits the organizations that implement it well.

Removes Data Redundancy

Data redundancy is a common issue.

It happens when the same piece of data is stored in multiple places. This can be a major challenge, as it causes a pile-up of data sets, which leads to the waste of storage space. 

Data management helps eliminate this problem by integrating data in a single database or warehouse. This way, duplicate values are deleted, saving essential space and ensuring accuracy.

Some management tools also keep redundancy down by spotting and dealing with duplicate entries before any pile-ups occur.

Reduced Errors

The employment of different data management techniques can drastically minimize the potential errors in data assets. 

It does so by establishing policies and following processes set around the control and use of data. 

Activities like copy and paste, drag and drop, and others can lead to human errors. Effective data management reduces these errors, ensuring that the available data is reliable.

Improved Accessibility 

Recently, there’s been tremendous growth in the amount of data generated by organizations. 

With that in mind, it would be close to impossible to easily access the correct data without efficient management. A data management analyst or another professional can reach the data they need only when it’s properly organized and managed.

Data management removes barriers to visibility, helping to streamline the process of deriving insights. Search results also become exponentially better and faster.

Increased Productivity

Organizations without good data management systems are bound to deal with inefficiency.

On the other hand, proper management invariably leads to higher productivity. With the right structure in place, employees can easily access the data they need to carry out their duties. 

Enhanced Security 

Data is a valuable asset and must be kept safe from influence, compromise, breaches, and theft. 

When an organization observes the best data management practices, this resource is secure and only available to authorized users. Moreover, databases and warehouses remain free from interference.

Security tools handle this process through encryption and authentication practices. This way, an organization can secure confidential data while ensuring compliance.

Time and Cost Efficiency

Another benefit of effective data management is that it boosts time and cost-efficiency. 

Organizations derive this benefit primarily by avoiding duplication. 

Say data is secure, accessible, properly stored, and complies with the best data management practices. If that’s the case, the organization doesn’t need to waste time, effort, and costs to generate data all over again. 

Also, professionals won’t unwittingly carry out tasks already completed by others.

Backup and Recovery

Data loss is a big concern for most organizations. It’s not uncommon either. Humans make errors, computers crash, and files get corrupted. 

In such an event, the company should be able to move on without losing important data. 

That’s possible because of backup and recovery.

It involves two stages: storing copies of data assets and then retrieving them in case of a loss.

Ultimately, with a backup tool taking periodic snapshots, companies don’t have to suffer lost or corrupt data.

What Is Data Management Software

So far, we’ve looked at data management and its various aspects. 

Obviously, managing an organization’s data manually would be a struggle. In fact, it’s next to impossible when we’re talking big data.

So how do analysts and other professionals handle and maintain data assets? 

Well, with the use of data management software.

It extracts, cleanses, integrates, and manages data from various sources. Essentially, it simplifies the management of large amounts of data by providing the tools to properly store and organize it.

It offers a wide range of critical features for the data lifecycle – from capturing, processing, and storing, all the way to analysis and interpretation.

This type of software includes but is not limited to database software.

Building an Effective Data Management Strategy

Understanding the benefits proper data management provides is the easy part. 

However, there’s still the tougher job of setting up and implementing those systems. 

An effective data management strategy guides all the components surrounding data management. It ensures that organizations can better overcome the challenges of data management.

It’s not a walk in the park, but these three steps will help any business hack data management.

Center your objectives

There’s a wide array of organizational goals. The data generated by each organization fits into the objectives it sets out to achieve. Likewise, the management strategy must also center around the company’s objectives.

Put data governance in place

Data governance is a core concept in building a data management plan. It defines processes, roles, and policies surrounding data.

An effective strategy involves deciding who controls what data, their actions, the applicable situations, and the data management techniques involved.

Use the right software and tools

There are tons of different software and tools out there. Their features and applications are just as varied –  while some can handle multiple data management tasks, others are much more specialized. 

For a data strategy to work, an organization must identify the appropriate systems and tools for generating and using data. 

Wrap Up 

Data management is an essential precursor to analysis. Before an organization can glean valuable insights from data, it must go through various stages involving manipulation, processing, and storage. 

This is what data management entails.

Overall, it involves various disciplines that aim to ensure that data is accurate, safe, available, and accessible to users within an organization.


What is data management, and why is it important?

According to DAMA International, data management is the development and execution of architectures, policies, practices, and procedures to effectively manage the information lifecycle needs of an enterprise.

Data management tools play an important role in getting the vast amounts of data that organizations generate ready for analysis and use.

What is data management with examples?

Examples of data management disciplines include:

  • Data Modeling
  • Data Governance
  • Data Security
  • Data Stewardship
  • Master Data Management
  • Data Preparation
  • Data Pipelines
  • ETL
  • Data Catalogs

What's the purpose of data management?

Data management is vital to businesses for several reasons. It helps to cut down on data errors and redundancy, ensuring that data is consistent. 

In addition, proper data management keeps data safe from theft, interference, and loss. When properly implemented, the organization’s data is easily accessible for authorized usage, regardless of its size. 

For more information on what data management is, check out our article.


Daniel Attoe

Daniel Attoe

Daniel is an Economics grad who fell in love with tech. His love for books and reading pushed him into picking up the pen - and keyboard. Also a data analyst, he's taking that leap into data science and machine learning. When not writing or studying, chances are that you'll catch him watching football or face-deep in an epic fantasy novel.

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