What is data management? Definitions, examples and best practices

In many ways, data is central to the operations of the modern world. From the ones and zeros that make up a tweet to the digital records of a medical patient, data is constantly stored, secured and shared.

It’s no wonder that data management — the collection of processes that ensure data is stored, secured, processed and analyzed — has become so crucial to modern businesses.

In the following, we’ll explain why data management is important, what its main objectives are, and the place management occupies in the current business landscape. 

Why is data management important?

Product development

Trust

Businesses of all stripes often work with sensitive user data. Credit card information, home addresses and health records are just some of the data that proper management is designed to protect within their database. Knowing that their data is secure, customers will trust a business and want to return.

Conversely, let’s say customers do not trust that their credit card information is secure within a given company's database. Chances are they won’t go through with a purchase. So, in effect, quality data management can help a business’s bottom line as well as its reputation.  

Analytics

Data management isn’t just about processing and moving bytes around. It can be used to develop powerful insights that guide businesses on future decisions, too, especially in today's world of big data. This is known as data analysis, and it’s supported by data management.

Processing market data, for instance, can help an investment bank predict the future of a stock. This, in turn, can give the bank an early start on a positive investment, or the insight to sell a stock before it loses value. Data analysis, in other words, is a crucial aspect of why data management is so important for keeping businesses profitable.

According to a survey conducted by Experian, which canvassed 500 executives and managers at U.S. organizations in a variety of industries, eight out of 10 said investing in data quality has resulted in a high return on investment for business initiatives. It’s no wonder why data management has become such a popular and exciting field. 

What are the objectives of data management?

Some major objectives of data management are:

• Secure storage of data across both hardware (physical servers) and software (cloud-based data centers)
• Working within compliance and regulatory standards to ensure data governance, including security and privacy
• The creation and accessibility of data from a diverse number of sources
• Making data available despite high network strain or a catastrophic server failure
• Seamless incorporation of data into software, such as applications and analytics programs

Of course, these objectives will not apply to the same degree to every business. A company managing patient records, for instance, may have no interest in incorporating its data into a phone application. Nonetheless, these objectives can help guide and inform the management processes of many businesses. 

Types of data management systems

There are four main types of data management systems:

Hierarchical DBMS

In this model, data is organized in a hierarchy from top to bottom. At the top, “parent” data is split into smaller “child” data. For instance, a “parent” piece of data may include a user’s entire social media profile on an application. This user’s particular name, past activity and other derivative pieces of data would all constitute “children” data.  

Network model

In contrast with a hierarchical model, the network model allows for each “child” piece of data to have multiple “parents.” This does not have a top-down structure, and data can instead be accessed through different pathways. 

Relational model

Popular for its simplicity, the relational model of data management places data within rows and columns in a table. Through structured query language (SQL), this data can be accessed and manipulated for a wide number of applications. 

Object-oriented model

In this model, data is stored in “classes,” which are made up of smaller units of data called “objects.” Within objects, units of data are stored along with their operations (e.g., the functions that that data will go on to perform). In effect, this model combines both a database and an application. 

Best practices for data management

Here are some practical tips that can help optimize the performance of data managers:

Long-term and short-term storage

Some data may require constant access and manipulation, while other data sees less use. Understanding the distinction can allow data managers to place data in the right location. Long-term preservation systems can keep less-used data safe but accessible when needed. In turn, this can free up resources needed to facilitate more active data transfers.

Always back up

UC San Diego suggests a “Rule of 3”: Keep two copies of everything on-site (local servers) and one copy off-site (in the cloud). This will ensure that, even in the event of catastrophic on-site failure, there will always be a copy of your data in a safe, remote location.

Use file naming to get more organized

Every business will require a different convention but settling on a file-naming system can help keep everyone in an organization on the same page. When looking at a long list of documents, for instance, it’s much easier for employees to find a specific document if it includes its creation data, author name and other important info in the file name. 

Keep good notes

Creating a document that includes any and all comments, thoughts and suggestions can give context to data. This document can help introduce a bulk of otherwise raw data to someone with less context and provide important information for understanding what they’re looking at.  

Jobs in data management

Positions within the field of data management include: 

Computer systems analysts

Overview: By analyzing businesses’ established computer systems, these analysts propose changes that can make the systems more efficient, secure and cost-effective. They can work under a computer and information systems manager to implement these new solutions for a business.

Salary range: As of May 2023, computer systems analysts earned between $63,230 and $165,700, with a median wage of $103,800, according to the U.S. Bureau of Labor Statistics (BLS).

Educational requirement: A bachelor’s degree in information technology is a common requirement for computer systems analysts.

Job outlook: Employment of computer systems analysts is projected to grow 10% from 2022 to 2032, according to BLS.

Salary ranges are not specific to students or graduates of University of Phoenix. Actual outcomes vary based on multiple factors, including prior work experience, geographic location and other factors specific to the individual. University of Phoenix does not guarantee employment, salary level or career advancement. BLS data is geographically based. Information for a specific state/city can be researched on the BLS website.

BLS Occupational Employment Projections, 2022-2032 is published by the U.S. Bureau of Labor Statistics. This data reflects BLS’ projections of national (not local) conditions. These data points are not specific to University of Phoenix students or graduates.

Information security analysts

Overview: These analysts are tasked with implementing protocols to keep a business’s data, network and computer systems safe from security threats. They are responsible for keeping data systems up to date with the latest regulatory standards, as well as keeping a watchful eye in case of security breaches.

Salary range: In May 2023, information security analysts earned between $69,210 and $182,370, with a median wage of $120,360, according to BLS.

Educational requirement: A bachelor’s degree in computer and information technology or a related field is typical. Given the importance of keeping business data safe, a master’s in cybersecurity can help a candidate stand out as especially qualified.

Job outlook: This role is projected to grow 32% from 2022 to 2032, according to BLS.

Common challenges in data management

Performance hiccups

Whether due to large amounts of traffic or faulty hardware, the efficient transfer and management of data can present a big challenge in data governance. Investing in up-to-date hardware, as well as running stress tests and diagnostics, can help prepare a data network for strain. 

Keeping up with regulations

Ever-changing compliance and regulatory standards, especially related to data security, are enough to make one’s head spin. Using automated tools and maintaining robust security can help data managers keep everything in line with regulations. 

Maintenance

There are always things to fix and change when it comes to managing data. Taking down an entire network to implement these changes, however, can cause unnecessary disruptions for users. Incorporating mirrored databases and failover systems can allow data managers to perform the work they need without interrupting the flow of data.  

Privacy laws and regulations

Both domestically and internationally, the laws surrounding data privacy and protection play a central role in how data management operates. On the one hand, data presents a powerful opportunity for businesses to better understand potential customers and provide more quality products. On the other hand, individual privacy is broadly recognized as a human right that needs to be protected.

Understanding the network of laws that govern the collection, processing, management and use of data is a prerequisite to proper data governance. These laws include:

• Payment Card Industry Data Security Standard (PCI DSS)
• Health Insurance Portability and Accountability Act (HIPAA)
• Children's Online Privacy Protection Rule (COPPA)
• EU General Data Protection Regulation (GDPR)

Much of what we take for granted today, whether it be booking a rideshare on your phone or making a purchase online, depends on data management. Those specializing in data management, therefore, are a necessary part of how this modern world works, both today and tomorrow.