What is data management and what does it matter?

Data management is the process of digesting, storing, organizing, and maintaining data created and collected by an organization. Effective data management is an essential part of the deployment of IT systems that execute business plans and provide analytical information to assist operational decision-making and strategic planning by corporate executives, business executives, and other end users.

The data management process involves a combination of different functions; Overall, the goal is to make the data available in precise corporate systems. Most of the work required is done by IT and data management teams, but business users usually participate in some process steps to ensure that the data meets their needs.

This comprehensive guide explains what data management is and provides insights into its individual disciplines, best practices for data management, the challenges organizations face, and the benefits of a successful data management strategy.

The importance of data management

Data is increasingly seen as a corporate asset that can be used to make informed business decisions, improve marketing activities, optimize business operations and reduce costs, with the aim of increasing revenue and profits. But a lack of proper data management can leave organizations with inconsistent data sets and data quality problems that limit their ability to implement business intelligence (BI) and analytics programs, or worse, flawed findings.

It is becoming more important today as businesses come under increasing pressure to comply with regulatory compliance requirements, including privacy and information protection laws such as the GDPR and the Consumer Privacy Act. In addition, companies are recording more and more data volumes and a wider range of data types, both of which are characteristics of Big Data systems, many of which have been used. Without good data management, navigation in such environments becomes difficult.

Types of data management functions

The separate disciplines that are part of the overall process include a series of steps, from data processing and storage to how data is formatted and used in operating and analytical systems. Developing a data architecture is often the first step, especially in large organizations that have a lot of data to manage. Provides a map architecture for the database and other data operating systems to be deployed, including specific technologies tailored to specific applications.

Databases are the most common platform used to store corporate data. They contain a collection of data that is organized so that it can be accessed, updated, and managed. Databases are used in transaction processing systems that generate operational data such as customer records and sales orders and data warehouses, as well as in warehouses that store integrated data sets from business systems for business intelligence and analytics.

Database management is a key function. After launching the databases, monitoring and performance adjustment should be done to maintain an acceptable response time to the database requests that users run to receive information from the data stored in them. Other administrative tasks include designing, configuring, installing, and updating the database, data security; Backup and restore databases and use software upgrades and security patches.

More About Types:

The main technology used to deploy and manage databases is a database management system (DBMS): software that acts as an interface between controlled databases and database administrators, end-users, and the applications that access them. Alternative database operating systems for the database include file systems and cloud object storage services. They store data in less structured ways than mainstream databases, which provide more flexibility in the types of data that can be stored and how they are formatted. As a result, they are not suitable for trading programs.

Other basic disciplines include data modeling, which charts the relationships between data elements and data flows in the system. Data integration, which combines data from different sources for operational and analytical use. Data governance, which sets policies and procedures to ensure that data is consistent across an organization. Finally, data quality management, which aims to fix data errors and inconsistencies. The other is core data management (MDM), which creates a common set of reference data about customers and products.

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