Whether implementing self-service dashboards, developing reporting processes to meet regulatory compliance, or defining BI strategy, one common challenge seems to arise: governance. When evaluating both business and technical challenges that exist, the following questions come up consistently:
- How can we ensure that source data can be trusted?
- How do we develop strong data quality parameters that are consistent and repeatable?
- Can our current data support better customer experience initiatives?
- How can we leverage analytics to provide a comprehensive view of our business?
All of these touch on the need for consistent business rules and processes associated with an organization’s information assets. Additionally, when organizations begin to understand the relevance of these questions to their overall information management strategy, they are ready to start developing a strong data governance initiative that combines governance requirements with analytics.
For most organizations, a single data warehouse is not a reality. Big data sources, increasing complexities, operational intelligence, and information diversity creates an environment that requires a consistent and thorough data management strategy. Added complexities mean more moving parts and a requirement to understand the intricacies of each data process flow.
This highlights the importance of data governance. Defined by The Data Governance Institute as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” Stated more simply, data governance includes the processes and framework involved in managing data assets. For instance, organizations need to know how to define their data by understanding what “customer” means within different departments and how it should be managed overall. Because customers can have multiple touch points within the organization, managing those interactions consistently becomes important.
Customer insights are just the beginning. These concepts exist for all types of data, whether operational, transactional or analytical. The way in which information is managed will have an effect on how it can be used. If information cannot be trusted, it becomes almost impossible to gain full value from its use.
Many business intelligence and analytics projects focus on a limited data set within the organization. And many project stakeholders believe that governance within the framework of analytics is enough. The reality, however, is that data governance should encompass all data assets across the organization to create a cohesive view of information and provide a way to manage inconsistencies and potential data quality issues as they arise.
Although very technological and data focused, strong governance affects business. Better customer lists and insight into demographics, products, suppliers, partners, etc. create more visibility. This visibility, if used right, helps organizations manage their data more effectively and helps identify potential opportunities and manage performance. Practically speaking, some business benefits include:
- The ability to manage data quality, leading to better insight and quicker time to insight
- Better collaboration across departments
- More efficient data management, supporting automated analytics and broader business insights
The main advantage for organizations is that governance done right provides more efficient information access and visibility leading to better analytics.
This article was written by Lyndsay Wise from CIO and was licensed by Bloomberg.