Data management is a crucial part of any organization's operations. It involves a wide range of tasks, policies, procedures and practices that are necessary to ensure the effective use of data. Data management is the process of ingesting, storing, organizing, and maintaining data created and collected by an organization. It is essential for implementing IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning.
Data management requires a reliable data strategy and methods to access, integrate, clean, govern, store and prepare data for analysis. Data reaches organizations from many sources: operating and transactional systems, scanners, sensors, smart devices, social media, video and text. But the value of the data is not based on its source, quality or format; it depends on what you do with it. Data management involves collecting, storing, organizing, protecting, verifying and processing essential data and making it available to your organization.
While data processing, data warehousing, data governance and data security are all part of data management, the success of any of these components depends on the company's data architecture or technology stack. Data services and APIs bring together data from legacy systems, data lakes, data warehouses, SQL databases and applications to provide a holistic view of business performance. Once databases are configured, performance monitoring and tuning must be performed to maintain acceptable response times on database queries that users execute to obtain information from the data stored in them. These data requirements are often addressed and documented by business users in partnership with data engineers who will ultimately execute according to the defined data model. The main reasons for incorrect data and data loss are that there is no data management system or plan, or that the plan or system is of poor quality. Over the past decade, developments within hybrid cloud, artificial intelligence (AI), the Internet of Things (IoT) and edge computing have led to the exponential growth of big data, creating even more complexity for companies to manage. Data modelers create a series of conceptual, logical and physical data models that document data sets and workflows visually and map them to business requirements for transaction processing and analysis.
Data management cannot be done by chance; organizations must invest in data management solutions that can deliver all the results they need to successfully manage and use their data. More than ever, issues related to privacy, compliance and data digitization require banks to have a reliable database. Data entry errors, completion errors and processing inefficiencies are risks for companies that don't have a robust data management system and plan. DAMA International (the Organization of Data Governance Professionals) and other industry groups work to improve understanding of data management disciplines and provide guidance on best practices. Master Data Management (MDM) is also related to governance and data quality although MDM has not been adopted as widely as the other two data management functions. Developing a data architecture is often the first step especially in large organizations with a lot of data to manage. Data management teams can also perform real-time data integration using methods such as change data capture which applies changes to data from databases to a data warehouse or other repository; streaming data integration which integrates real-time data streams in a way continue; many organizations struggle to manage their massive collection of AWS accounts but Control Tower can help. Data security teams can better protect their data by leveraging encryption and data masking as part of their security strategy.
Organizations and businesses are using big data more than ever to inform business decisions and gain deep insight into customer behavior trends and opportunities to create extraordinary customer experiences. Data governance teams also help define roles and responsibilities to ensure that access to data is provided appropriately; this is particularly important for maintaining data privacy. .
