There are many definitions of Data Governance, starting with a simple, straightforward base we’ll call it: “Data governance is a group of practices and principles that ensure top quality during your data’s whole life cycle.”

Data governance goes beyond this simple definition to encompass data management, support for business processes, and a broad collection of data functions and strategies, Such as:

  • Master Data Management (MDM): The complete collection of procedures, policies, standards, and tools for specifying, regulating, and handling data.
  • Data lineage: Handling the origin of data, where it moves, and what happens to it.
  • Data Loss Prevention (DLP): Ensuring that sensitive information isn’t sent out the corporate network and controlling what data can be transferred.
  • Data Security: Protecting data from unauthorized access to or corruption of information.
  • Data integrity: Assessing the veracity, accuracy, and quality of information.
  • Data synchronization: Putting consistency among data types.

Employees now expect access to enterprise data everywhere, at any time, on any device; industry leaders require data that is searchable, viable, and adaptable enough to provide actionable insights.

A smart data governance approach and technology solution enables an organization to manage its information, meet regulatory requirements, and encourage its electronic transformation journey.

By collecting, analyzing, and gleaning advice from organizational information, company leaders can easily detect and react to inquiries from regulators, perform early assessments, and research new business opportunities based on data with the most significant referential value and quality. With the information under management and centralized, it is possible to defensibly delete information once its worth to the company is no longer measurable, with a standardized and repeatable approach.

A robust data governance strategy doesn’t ask that you construct a new silo dedicated exclusively to compliance. Instead, it unites governance requirements with data analytics to get a more dynamic data governance process. Conventional data management techniques make it almost impossible to access and analyze information for actionable insights quickly. However, organizations need to quickly integrate and picture information to fulfill compliance requirements while also forcing better business decisions.
Satisfying these expectations may contribute to significant security and compliance risks. IT leaders have to balance driving business value with stringent regulations, all without disrupting workforce productivity or endangering business assurance.

Using software to move data into a central data hub to handle accessibility, security, retention, and expiry of every item creates a win-win scenario and provides increased value from information, and helps drive a broader strategic and analytical plan at the executive level.

In the next post, we will explore an essential concept for Data Governance, which is the Single Source of Truth for your data.