Data management is the process of acquiring, keeping track of, organizing, and storing produced and gathered data by a company. Running corporate operations and providing information that supports corporate leaders, business managers, and other end users in making decisions depend on efficient data management in IT systems.
Various purposes of the data management process together seek to make data accurate, available, and accessible. Data management services teams and IT experts handle most of the necessary tasks. Usually, however, business users join the process to make sure data satisfies their requirements and to assist in the development of internal data standards and use guidelines under data governance initiatives.
Value of Data Handling
With the aim of raising income and profits, data is progressively recognized as a corporate asset that can be utilized to make better-informed business choices, boost marketing campaigns, and maximize company operations and lower expenses.
On the other hand, inadequate data management may force companies with incompatible data silos, inconsistent data sets and data quality issues. Those problems restrict their capacity to execute analytics and business intelligence (BI) applications, or worse provide erroneous results.
A rising number of regulatory compliance requirements, including data privacy and protection regulations such GDPR and the California Consumer Privacy Act (CCPA), has also made data management more important.
Furthermore, businesses are gathering increasingly more data and a greater range of data kinds, both traits of the big data platforms they have implemented. Such ecosystems may become messy and difficult to navigate without proper data management.
Which Main Components Comprise the Data Management Process?
From data processing and storage to governance of how data is structured and utilized, the many disciplines that comprise data management follow a sequence. The main purposes of the procedure are summed here.
Architecture of Data
Usually first, especially in big companies with loads of data to handle, is developing a data architecture. Data architecture documents data assets and maps data flows in systems, therefore offering a plan for data management. More generally, it also provides a structure for implementing databases and other data systems, including particular technologies to suit certain uses.
Management of Databases
Corporate data is most often housed on databases. Their data is arranged so that it may be accessed, changed, and controlled. They are found in data warehouses, which compile aggregated data sets from corporate systems for BI and analytics purposes, and in transaction processing systems generating operational data like customer information and sales orders.
Database management is therefore a fundamental aspect of data management. Database design, setup, installation and upgrades comprise core administration chores. Performance monitoring and tweaking needed to be done after databases have been configured to maintain reasonable response times on database searches users execute.
Data security; database backup and recovery; and implementation of software updates and security patches are additional duties for database managers (DBAs).
Covered in greater depth in the next section, other basic data management disciplines include the following:
- Data integration, for operational and analytical purposes, is the combining of data from several sources.
- Data extraction services charts data structures and the links among data items.
- Data governance provides standards and processes to guarantee data is consistent and utilized correctly all throughout a company.
- Management of data quality seeks to correct discrepancies and data mistakes.
- Master data management (MDM), which generates a shared reference set including consumers, goods, and other corporate entities.
Usually, data miners are data scientists and other qualified BI and analytics experts. Its basic components consist of statistical analysis and machine learning, and data management chores meant to ready data for analysis.
Using AI technologies and machine learning algorithms has automated more of the process and made mining vast data sets—including customer databases, transaction records, log files from web servers, mobile applications and sensors—easier.
Conclusion
Vendors are creating edge data extraction services capabilities as companies employ IoT devices and distant sensors to gather and analyze data in edge computing settings. Moving data management outside the cloud and on-site data centers lets real-time analytics apps on edge data run. But data management and governance need fresh procedures.
