Data management is the practice of managing data as a valuable resource to unlock its potential for an organization. It involves collecting, storing, organizing, protecting, verifying and processing essential data and making it available to your organization. The purpose of data management is to help people, organizations and connected things optimize the use of data within the limits of policies and regulations so that they can make decisions and take action that maximize the benefit to the organization. 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. Its value depends on what you do with it. A reliable data strategy and methods to access, integrate, clean, govern, store and prepare data for analysis are essential for effective data management. Data services and APIs bring together data from legacy systems, data lakes, data warehouses, sql databases, and applications, providing a holistic view of business performance.
Data management enables more efficient access to data analytics that provide the information needed to improve business operations and identify opportunities for improvement. By establishing a better framework for accessing the wide swaths of data generated by each company, companies can make more informed decisions and improve their ability to deliver valuable products and services to their customers. Organizations with well-managed data can also become more agile, allowing them to detect market trends and move forward to take advantage of new business opportunities more quickly. Data lineage tracks the path of data from its sources to its current location as it tracks key details: technical, business, and metadata (data about data). Once you have established a complete list, ask yourself what is the best way to organize and protect this data for later recovery.
For example, migrating to cloud databases and big data platforms can be difficult for organizations that need to move data and process workloads from existing on-premises systems. Relational databases organize data into tables with rows and columns that contain database records; related records in different tables can be connected using primary and foreign keys, avoiding the need to create duplicate data entries. Data warehousing requires a defined schema to meet specific data analysis requirements for data outputs such as dashboards, data visualizations, and other business intelligence tasks. Artificial intelligence (AI), machine learning, Industry 4.0, advanced analytics, the Internet of Things and intelligent automation require a lot of timely, accurate and secure data to do what they do. Each of these components in the data management space is undergoing a tremendous amount of change right now. If you need to create a data management plan, here's a great free resource from the University of California. In addition, data models need to be updated when new data sources are added or when an organization's information needs changes.
These can include database management systems, data warehouses and lakes, data integration tools, analysis tools and more. Master Data Management techniques collect data from a variety of sources and display it as a fixed and reliable source. Data management is essential for organizations looking to maximize their potential by unlocking the power of their data. It helps reduce risks related to data processing while providing accurate information to people and organizations. It also helps optimize data which in turn helps make better business-related decisions taking into account company rules regulations and policies.
