An Outcome-Driven Enterprise Data Strategy: Data Lifecycle Processes

An Outcome-Driven Enterprise Data Strategy: Data Lifecycle Processes

According to Gartner, “a digital business cannot exist without data and analytics.” I agree and would add “processes for managing that data” to this list. Why? We don’t create data just to create data. We produce it to help the business run, and to be successful, an outcome-driven data strategy must include processes detailing how to create, update, and delete (CRUD) business-critical data.

These CRUD processes are often part of a larger business process, such as the personal data used in lead management, the product data in research and development, and the vendor data in supply chain management. How we handle them varies based on the company, business goal, standards, data in question, and a host of other factors.

Some processes can be automated via business rules, while others require manual input. They can also be done en masse or at the individual record level. Whatever the approach, though, it’s important that the processes are as simple, automated, and friendly as possible and designed according to the data quality standards set by your organization.

In this third installment of a five-part series with my colleague Maria Villar, I’m going to dive into data lifecycle processes and their role in an outcome-driven enterprise data strategy.

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