What Is Data Management? (With Benefits and Best Practices)

By Indeed Editorial Team

Published June 26, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data management professionals are essential in helping companies deploy IT systems to run their business applications and provide analytical information. Many companies invest in data management solutions to improve their scalability, security, and reliability. Learning the importance of data management systems in a company can help you find and access trusted data to help a company make effective decisions. In this article, we answer, "What is data management?", explain what it does, discuss its importance, list the best practices for implementing it, and answer frequently asked questions.

What is data management?

Knowing the answer to, "What is data management?" can enable you to help a company manage and organize information, making it readily available to its customers and employees. Data management refers to storing, organizing, collecting, protecting, and processing data to support a business's efficiency, productivity, and decision-making. It helps organizations and individuals optimize the use of data within policy regulations to make decisions that benefit them.

The data management process usually includes various functions that collectively ensure that a company's data is accessible and accurate. It typically employs multiple techniques and tools, such as data architecture, data integration, data warehousing, and data analysis, to transform complex data into actionable or meaningful information. As companies create data, data management becomes essential for strategic planning. Leading data management systems often enable companies to leverage big data from various sources.

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What is the function of data management systems?

Depending on a company and the type of data it manages, organizing data often covers a wide range of procedures, tasks, processes, and policies. The function of data management include:

  • Ensuring adequate data security and data privacy

  • Collecting, validating, and processing data

  • Storing data across various clouds and on-premise locations

  • Governing how companies and individuals can use data through applications

  • Using data in an increasing number of applications, algorithms, and analysis

  • Integrating multiple types of data from multiple sources, including unstructured and structured data

  • Destroying and archiving data in compliance with retention schedules and guidelines

  • Providing methods for disaster discovery and high availability

Why is data management important?

Companies with a good data management process often gain the necessary insights to deliver a better customer experience. The following are the benefits of effective data management:

Reduces data silos

Various data management frameworks and tools help companies eliminate data silos or dependencies. A data silo refers to a collection of information that's not easily accessible to an organization's employees or customers. For instance, an organization can use data management systems to reveal the most common renewal window when analyzing accounting data. By accessing this information, their marketing or sales team can easily upsell customers.

Related: What Is Structured Data? (Benefits and Other Data Forms)

Increases data visibility

Data management can increase an organization's data visibility, making it easier for individuals to find the appropriate information for analysis. Through data visibility, an organization can become more productive and organized. It improves its employee's workflow by enabling them to spend less time searching for data they require to complete their tasks.

Improves scalability

Organizations with effective data management processes can scale data with repeatable processes to keep them current. For instance, many cloud platforms allow for more flexibility, enabling an organization to increase or reduce its data capacity to meet users' needs. When data processes are easy to modify, organizations can reduce the cost they may spend on conducting the same research to scale data.

Enhances data security

Data management typically protects a company and its employees from data theft, breach, and loss using encryption and authentication tools. It limits unauthorized individuals' access to specific data, such as customer payment information. Having strong data security ensures that a company's information is easily retrievable in the event of risks. It also protects a business from paying fines if it doesn't carefully manage data in compliance with government policies and regulations.

Reduces data error

Data management is essential in reducing the errors that may occur in storing and collecting data. It establishes policies and processes for companies to use when making decisions. With reliable data management, businesses can quickly respond to customer needs and market changes. For instance, accurate product data can help marketers build better campaigns using the features customers mostly prefer.

Related: How to Learn Data Science (A Complete Guide)

Data management best practices

Preparing a reliable data management framework can help companies implement practical solutions and strategies to address the challenges of managing data. The following are best practices you can implement for effective data management:

Identify the business's goals

Identify the data management objectives the company wants to achieve. A company's goals can help you determine the process for managing, storing, collecting, analyzing, and cleaning its data. It can also enable you to determine its relevant data, prioritize objectives, and evaluate the management tool that may work best. Consider creating a discovery layer on the organization's data tier to browse and search for useful sets that can help you identify its data management goals.

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Develop a reliable data science environment

Implementing a reliable data science environment can enable companies to automate multiple activities. This means they can quickly streamline the evaluation and creation of data models. These are visual representations of a company's data elements that help it decide how to store, share, access, and update information. By removing the need for manual data transformation, a company can conduct tests efficiently on new models. A data science environment also has management tools that can help a company reduce costs.

Implement autonomous technology

Autonomous technology, such as machine learning and artificial intelligence (AI) devices, can enable an organization to monitor database queries continuously. They allow a company's database to reduce the performance of time-consuming manual tasks and maintain a quick response to users' needs. With autonomous technology, businesses can efficiently optimize their big data without errors.

Related: How Much Does a Data Scientist Earn?

Prioritize data security

To protect a company's data from outsiders, consider training its employees on ways to handle data correctly and ensure its processes meet compliance requirements. You can use new data discovery tools to review data and identify the compliance requirements the company can monitor and detect. As compliance demands increase, this can help the company reduce security risks and handle potential breaches.

Frequently asked questions

The following questions and helpful answers can help you gain more insights into managing data sets:

What are the types of data management systems?

The types of data management systems include:

  • Data warehouse systems: These systems often benefit marketers by helping them discover their customer's purchasing patterns and identify problems a product solves. They function as a customer data source that collects information for purchase systems, financial applications, and marketing platforms.

  • Marketing technology systems: They may include marketing automation platforms, advertising technologies, and e-mail service providers that help marketers perform campaigns. These systems also help marketers cross-reference their customer data and audience with comprehensive information.

  • Customer relationship management systems (CRM): CRM refers to the database that often stores all customer data sources, including sales opportunities, revenue data, personal data, subscription renewals, and conversion data. A sales team may use these systems to keep contacts and leads.

  • Analytical tools: These tools serve several purposes in data management. For instance, companies can implement them in marketing, advertising, and sales to gain performance insights.

Related: Primary vs. Secondary Data (With Definitions and Benefits)

Who can use data management?

Data management usually enables multiple organizations to achieve their goals and become successful. The following are industries that use data management:

  • Banking: Data management processes are essential in the banking industry because they enable banks to protect customer data and comply with government regulations. With data management, banks can gain customers' trust.

  • Retail: Understanding customers' needs and responding to their expectations requires retail stores to have reliable data management. It can enable them to improve customer satisfaction and streamline data.

  • Government: Solid data management systems typically support the government in preventing fraud. It can also help federal or provincial authorities ensure citizens' safety.

  • Manufacturing: In the manufacturing industry, data management often enables producers to manage product inventories and enhance better performance. It can also help producers integrate data from multiple sources to make well-informed business decisions.

Related: What Is Background Information? (With Types and Sources)

What are the specialties in data management?

Here are the various specialties data management professionals may focus on:

  • Data warehousing: It oversees and provides a cloud-based, or physical infrastructure companies can use to analyze raw data.

  • Data security: It protects data from unauthorized corruption and access.

  • Data governance: This outlines the policies, standards, and procedures an organization may follow to maintain data integrity and security.

  • Data architecture: It provides a formal approach for companies to create and manage data flow.

  • Data preparation: Companies use it to clean and transform raw data into the right format for analysis.


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