Data management is a total lifecycle information system that tracks data from the time it is created until it ceases to be useful. It includes all the processes to proactively collect, organize, protect, store and share data. Data governance involves planning, creating, implementing, and enforcing policies that describe how an organization manages its data. Its ultimate goal is to ensure the widespread availability of high-quality data information that is standardized, secure, compatible and up to date.
Data integration practices ensure that raw data is organized and maintained in a structured manner on a foundation. Data Management Platforms are used to identify, alert, diagnose, and resolve faults in the database system or underlying infrastructure, allocate database storage and memory resources, make database design changes, optimize database query responses for a faster application performance. Data management systems and processes should be stored in a central knowledge management system, where the entire organization can easily access them with a tool like Confluence or Notion. Procedures should be defined to keep information up to date, as people and their knowledge may leave the organization, and data management needs will certainly evolve.
People should be taught and trained to manage data following their data governance policies and processes. Tableau can help eliminate data silos, streamline processes, and make self-service analytics accessible across the organization. Major retailers like Tape à l'oeil rely on data management to design customer experiences that measure omnichannel buying and buying behaviors, meeting customer demand in near real time. Updates should be made to data management policies as part of recurring meetings, such as town halls or quarterly updates from the organization's management team.
If business objectives don't inform the data management strategy, valuable time and resources could be wasted collecting, storing and analyzing the wrong types of data. Data management is essential for decision-making in any company. It is important to act strategically and proactively when it comes to data rather than acting ad hoc and reactive. An enterprise data management strategy can help organizations use their data more effectively and efficiently.
It can also help them make better business decisions and extract more value from data in less time because it's clean and standardized.