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Implementing Data Management Processes

The massive amount of data collected by businesses requires a clear strategy for organizing, securing and storing information to make it more usable. The ones who manage their data as investments are able to leverage it to gain insight into customers as well as market trends and operational efficiency.

The achievement of goals in data management requires a team of people who have different skills working together to sort, categorize and organize data into a usable form. This includes ETL processing which transforms raw data from operational applications such as point-of-sale (POS) into a model that is optimized to be used for analytical processing. It also includes data cleansing to eliminate duplicates and maintain data integrity. Data catalogs are also developed which provide the locations, security levels and content of your organization’s various data tiers. There’s also discovery, which allows you to search and browse these data tiers to find specific information and data sets.

Other issues related to managing data include determining access and ownership controls and ensuring compliance with standards like GDPR, CCPA and others. The ever-changing compliance landscape creates confusion about how to handle data, and prevents companies from investing in a strategy for managing data until they’re sure they can meet the new requirements.

The key to overcoming barriers to data management is to clearly identify your business goals. Concentrate on the most important KPIs in deciding which data to collect and how to manage it. Implement the processes to support those goals.

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