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In today’s digital economy, companies have access to more data than ever before. This data creates a foundation of intelligence for important business decisions. To ensure employees have the right data for decision-making, companies must invest in data management solutions that improve visibility, reliability, security, and scalability.

What is data management?

Data management is the practice of collecting, organizing, protecting, and storing an organization’s data so it can be analyzed for business decisions. As organizations create and consume data at unprecedented rates, data management solutions become essential for making sense of the vast quantities of data. Today’s leading data management software ensures that reliable, up-to-date data is always used to drive decisions. The software helps with everything from data preparation to cataloging, search, and governance, allowing people to quickly find the information they need for analysis.

Types of Data Management

Data management plays several roles in an organization’s data environment, making essential functions easier and less time-intensive. These data management techniques include the following:

  • Data preparation is used to clean and transform raw data into the right shape and format for analysis, including making corrections and combining data sets.
  • Data pipelines enable the automated transfer of data from one system to another.
  • ETLs (Extract, Transform, Load) are built to take the data from one system, transform it, and load it into the organization’s data warehouse.
  • Data catalogs help manage metadata to create a complete picture of the data, providing a summary of its changes, locations, and quality while also making the data easy to find.
  • Data warehouses are places to consolidate various data sources, contend with the many data types businesses store, and provide a clear route for data analysis.
  • Data governance defines standards, processes, and policies to maintain data security and integrity.
  • Data architecture provides a formal approach for creating and managing data flow.
  • Data security protects data from unauthorized access and corruption.
  • Data modeling documents the flow of data through an application or organization.

Why data management is important

Data management is a crucial first step to employing effective data analysis at scale, which leads to important insights that add value to your customers and improve your bottom line. With effective data management, people across an organization can find and access trusted data for their queries. Some benefits of an effective data management solution include:

Visibility

Data management can increase the visibility of your organization’s data assets, making it easier for people to quickly and confidently find the right data for their analysis. Data visibility allows your company to be more organized and productive, allowing employees to find the data they need to better do their jobs.

Reliability

Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.

Security

Data management protects your organization and its employees from data losses, thefts, and breaches with authentication and encryption tools. Strong data security ensures that vital company information is backed up and retrievable should the primary source become unavailable. Additionally, security becomes more and more important if your data contains any personally identifiable information that needs to be carefully managed to comply with consumer protection laws.

Scalability

Data management allows organizations to effectively scale data and usage occasions with repeatable processes to keep data and metadata up to date. When processes are easy to repeat, your organization can avoid the unnecessary costs of duplication, such as employees conducting the same research over and over again or re-running costly queries unnecessarily.

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