A person pointing to a board with charts, representing data management.
What Is Data Management? How Does It Work?
A person pointing to a board with charts, representing data management.

What Is Data Management? How Does It Work?

With businesses generating, collecting, and having access to more data than ever before, the need for organized and streamlined data management strategies and systems has become non-negotiable. Without efficient and secure data management practices, all of this information can result in more problems than solutions as it becomes overwhelming, disorganized, unusable, or even vulnerable to security and compliance threats. 

In this post, you’ll get a breakdown of what data management actually is and why it’s so important to your business, examples of data management, as well as key data management challenges and how to overcome them. 

What is data management?

While it might sound complicated, data management is simply the process of collecting, storing, securing, and managing information in a way that is most useful to a business, organization, or individual. When done properly, data management helps companies quickly analyze and understand the health of the organization (or parts of the organization) so that they can make informed, data-backed decisions. 

For example, when a business collects and organizes information about their email subscribers’ activity and behaviors in order to evaluate their content strategy or email send schedule, this is a form of basic data management. Collecting information on clients and adding it to a CRM platform is another classic example of data management. 

Why is data management so important?

In today’s data-driven world, effective data management is essential for any business that wants to boost efficiency, improve organizational processes, limit errors, and gain a competitive edge. Here are some reasons why data management is so crucial: 

  • Reduction of errors and risk: Proper data management includes making sure that data is accurate and up-to-date, standardizing processes across the organization, proactively identifying any risks that could occur, and data backup and recovery processes. 
  • Better decision-making: When data is up-to-date, organized, and easily accessible by anyone across an organization, businesses are better positioned to make effective decisions based on this information in the future.   
  • Improved efficiency: Sifting through disorganized data is not a great use of any employee’s time. With proper data management, team members are able to streamline the process and quickly uncover the data they need — without having to worry about whether it’s verified or reliable. 
  • Compliance: An important component of data management is making sure the organization is following any applicable regulations and compliance requirements or laws, something that helps further reduce the risk of any legal issues or fines. 
  • Happier customers: Organizations with properly-managed data can use this information to understand and serve their customers better — something that helps with retention and increased sales over time. 

Different types of data management

Just as there are many different ways to collect and use data, there are numerous different forms of data management. These include: 

  • Data Catalogs: A data catalog is a centralized index or inventory within an organization that provides information about where a data asset is located, its format, history, ownership, and the quality of the data. This helps team members quickly and easily find exactly what they’re looking for, plus helps with accountability and governance. 
  • Data Lifecycle Management: Data lifecycle management (or DLM) refers to the different stages data goes through from creation to deletion. These stages are creation or collection, access, usage, storage, transfer, and deletion. 
  • Data Preparation: A key step in the data analysis process and a significant part of data management, data preparation involves cleaning data to make sure it’s cohesive, formatted properly, easy to understand, and doesn’t contain duplicates, missing values, or other unusable components.
  • Data Warehousing: Data warehousing is a type of data management that includes gathering, storing, and managing data in a central database. An organization’s data warehouse is like a library where the cleaned data is stored and organized in order to make it as easy as possible to find, analyze, and report on.   
  • ETLs (Extract, Transform, Load): As the name implies, ETL involves retrieving and extracting data from various sources, transforming it (cleaning, filtering, processing) into a standard format ready for analysis, and loading data into the system (ie. data warehouse). 
  • Data Integration: As a previous blog post defined, “data integration is the process of creating a unified system where data can be consulted, by importing business information from disparate sources.” As an essential component of data management, data integration helps provide an organization with valuable insights and business intelligence in a central location that helps inform future decisions. 
  • Master data management: In this kind of data management, every bit of data your organization relies on has a single master record which is then streamed, synced, or otherwise pushed to other platforms. This helps prevent data from becoming outdated when it’s pulled from multiple tools.

Common challenges of data management — and how to solve them

With the sheer volume of data available to businesses today, data management can be a complex and overwhelming task, even for those with a strong analytical skillset. From trying to transform data into tangible business strategies to ensuring security and compliance, there are several common challenges of data management any organization may face. These issues include:  

Protecting your data 

With security breaches and cyber-attacks becoming more frequent, organizations must prioritize the protection and security of their data throughout the data management process. Although there are never any 100-percent security guarantees, there are a number of ways businesses can protect their data from unauthorized access or threats. 

Organizations can proactively implement ironclad security measures such as firewalls, anti-virus protection, encryption, security audits, and regular employee security training and education. As even the most secure and careful organizations can fall victim to security threats or even accidental data loss, it’s also important for businesses to regularly back up their data to minimize the impact of events like these. 

A lack of insight

While organizations collect and store more and more data, simply possessing this information isn’t enough to be of much help to the business. To actually get any value or benefits from their data, companies need to know exactly what data they have, where it’s stored, how to access it, and how to use it effectively in order to make a positive impact on the business. 

To do this, it’s important to develop a data management strategy that establishes a clear understanding of the business’ data goals, processes, standards, and security procedures, introduce data management tools, implement data governance policies, and run comprehensive data training to employees that’s tailored to each of their roles. 

Seemingly endless data 

The more data that’s collected, the more difficult the data management process becomes. Organizations can address this increasingly daunting issue by first prioritizing and streamlining the collection, processing, and storing of data that’s most relevant to their business goals and targets. By having clear goals, it’s easier to filter out irrelevant data and make sure the data being organized and stored is actually beneficial — something that helps prevent the accumulation of unnecessary or redundant information (and saves time). 

An example of how businesses are implementing these practices and automating their processes is through the use of AI tools and machine learning. As explained by Deloitte, “Some [organizations] are beginning to experiment with automated data classification, including deployment of homegrown and commercial AI and machine learning tools.” As these tools become increasingly refined and precise, they have the potential to significantly help alleviate the burden of managing large volumes of data.

Siloed data

When data is collected from and stored in different systems across various departments within a business, it becomes difficult to analyze the information and make informed decisions from a holistic point of view. Data that is siloed across multiple locations is difficult to manage, can result in inconsistencies and redundancies, and can contribute to security and privacy breaches.  

In order to fix this common data management issue, companies need to go through the process of data integration in order to consolidate and centralize their data from all of their sources in one accessible location. This allows for seamless, transparent, and accessible data sharing across the business — something that allows for more accurate, data-informed decisions. 

Compliance, privacy, and legal regulations are serious matters when it comes to data management. As regulations are constantly changing and evolving, staying up-to-date can be a major challenge for organizations of any size. For example, compliance and legal regulations may not be consistent across different regions or countries, making compliance tricky for global organizations. A business operating in Germany could have a totally different set of laws compared to a business operating out of Canada. 

That said, different regions even within the same country can have different requirements, deadlines, processes, and penalties, which makes it even harder for businesses to establish an effective data management strategy regardless of where they’re located. 

To overcome and help alleviate some of the stresses of evolving compliance and legal regulations, businesses (at a minimum) need to have a vigilant understanding of how compliance and data privacy regulations are integrated throughout all levels of their processes, software systems, tools, and employee conduct. In addition to a dedicated legal team, individuals throughout the organization can stay informed of changing laws and regulations by paying close attention to international news, subscribing to blogs on the subject, or consulting external experts such as cybersecurity specialists or data protection specialists. By taking a proactive approach, organizations can ensure that they’re following compliance and data privacy regulations that protect their data from unauthorized access, misuse, and loss as much as possible.

Data management is a crucial component of any successful business. While businesses have access to an abundance of data, this data can quickly become overwhelming without the right tools, techniques, and data management strategies in place. With proper data management, organizations can improve efficiency and productivity, reduce risk, enhance the decision-making process, and even increase customer satisfaction.