Data Governance and Stewardship in Enterprise Applications
By Vikram Chalana on Aug 2, 2017
In the last article, I addressed why data is increasingly being recognized as a strategic asset in your digital journey. In that context, I also identified the importance of data governance and stewardship programs in your organization. In this article, I’d like to talk more about governance and stewardship.
Datum, LLC provides a good definition of data governance and stewardship in this white paper. Data governance is the process of defining what data matters to the enterprise. The deliverable of the data governance process is the metadata attributes that are necessary to maintain order, efficiency, and control of data in your enterprise applications. Data stewardship, on the other hand, is the process of leveraging these attributes to maintain order, efficiency, and control.
Data governance is derived from three basic components:
- An organization that can accommodate data-related events
- A set of processes to articulate data-centric policies
- A system of business execution knowledge that is essential to providing context for data management
Data governance allows you to establish policies to govern data, and these policies need to reflect the value or contributions data make to the organization. The process of setting up these policies can vary fundamentally from one organization to another because it reflects how each organization makes decisions and sets priorities. There are 3 levels of these policies as shown in the picture to the right: the glossary of common business terms, data standards, and business rules.
Data stewardship is the process of taking a data governance policy and moving it out to people who play a role in data creation, change, or preparation. At its core, stewardship involves responding to business activities based on the policies that are set up during the data governance program. You can manually enforce the rules or choose from three methods to implement data stewardship for your key enterprise applications. An application data management (ADM) framework will often provide tools for each of these stewardship methods. Most companies will employ a combination of these three methods, in addition to manual governance.
Active governance codifies the exact routines and expectations concerning data quality and integrity. These methods can help remove individual judgment about policy from the picture over time. In some scenarios, this method can automatically generate or default data so the users don’t introduce errors. In other scenarios, active governance provides options to give a user the opportunity to validate enterprise application data before it goes into the system.
Reactive governance involves using reports and dashboards to identify risks associated with your data that can disrupt your business processes or create compliance concerns. At the simplest, these dashboards can identify data that don’t follow the business rules defined during the governance process.
Proactive governance completes the cycle of identifying data with errors, cleansing data, and updating the enterprise application with the cleansed data. It also combines elements of active governance to prevent erroneous data from entering the system in the first place.
For most companies, managing information with a level of discipline requires a cultural shift. Therefore, processes like data governance don’t move through an organization like a tidal wave of activity. Instead, data governance and stewardship is an ongoing work stream of data management that is built over time with both tactical and long-term initiatives. Identifying tools and techniques early-on will enable analysts to quickly validate the data governance cycle.
Once a data governance program is accepted, your data management organization can consider implementing enterprise-wide capabilities in areas that meet your requirements. Improving data governance and stewardship practices across the enterprise can lead to significant gains in business performance and agility and ADM tools can play a very important part of your data governance journey.
About the author
As Winshuttle’s Chief Technology Officer, and Co-Founder, Vikram has been focused on empowering people to transform their ERP-based businesses since Winshuttle’s humble beginnings. He is passionate about technology that allows people to improve their lives and the way they run their businesses. Outside of work Vikram likes to spend time outdoors running, hiking, kayaking, and skiing.
Questions or comments about this article?
Tweet @Winshuttle to continue the conversation!