Data management is the practice of collecting, organizing, and using data in a secure, efficient, and cost-effective manner. The purpose of data management is to help organizations and connected things to optimize the use of data within the boundaries of policies and regulation so that they can make decisions and take measures that maximize the benefit to the organization. A robust data management strategy is increasingly important than ever, as organizations increasingly rely on intangible assets to create value. Data management is the process of ingesting, storing, organizing and maintaining data created and collected by an organization. Effective data management is a crucial part of implementing IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other users.
The use of data management allows more efficient access to data analysis that provides the information needed to improve business operations and identify opportunities for improvement. In our digital world, data reaches organizations from many sources: operating and transactional systems, scanners, sensors, smart devices, social networks, video and text. However, the value of data is not based on its source, quality, or format. Its value depends on what you do with it. Data management is the process by which companies collect, store, access and protect data from various business software solutions. Effective data management requires a data strategy and reliable methods to access, integrate, clean, govern, store and prepare data for analysis.
Data management encompasses all disciplines related to data management as a valuable resource. It includes activities such as data governance, data integration, master data management (MDM), metadata management, data quality management (DQM), security and privacy. Data governance specifies how responsible people use processes and technologies to manage and protect data. MDM creates a central master data registry for selected data domains, which is often referred to as the gold register. Data quality techniques include data profiling, which scans data sets to identify outliers that could be errors; data cleansing, also known as data debugging, which corrects data errors by modifying or eliminating incorrect data; and data validation, which compares data sets to identify outliers with pre-established quality rules. Relational databases organize data in tables with rows and columns that contain database records; related records from different tables can be connected using primary and external keys, avoiding the need to create duplicate data entries.
Increasingly popular cloud database platforms allow businesses to scale or shrink rapidly. The main use cases for data warehousing are BI queries and business reports, which allow analysts and business executives to analyze sales, inventory management, and other key performance indicators. An experienced and qualified team with advanced data management skills is crucial to developing and refining a global approach to data management. Increasingly popular cloud data platforms allow businesses to grow or shrink quickly and cost-effectively. All of these components work together as a “data utility” to provide the data management capabilities that an organization needs for its applications, as well as the analyses and algorithms that use the data originated by those applications. However, the lack of adequate data management can burden organizations with incompatible data silos, inconsistent datasets, and data quality problems that limit their ability to run business intelligence (BI) and analysis applications or worse cause erroneous findings.
Effective data management has been critical to business success for more than 50 years from helping companies improve information accuracy report detect trends and make better decisions to driving digital transformation driving new technologies and business models today. This comprehensive guide to data management explains in more detail what it is and provides information on the individual disciplines it includes best practices for data management the challenges faced by organizations and the business benefits of a successful data management strategy. These requirements provide standardized data protection laws that give individuals control over their personal data and how it is used. Given the critical role that data plays in business today a robust data management strategy and a modern data management system are essential for every company regardless of size or industry. However reliable data starts with a robust data management strategy backed by proven data management technology.