Enterprise Data Management (EDM) seems to be at the top of many organizations’ strategies for 2018, as the importance of data to organizations continues to grow exponentially. EDM plans may include modernizing an existing data warehouse to enable near real-time data, building a big data environment to support deeper analytics, focusing on and increasing digital capabilities and associated analytics, moving existing data and analytics to the cloud, increasing analytics capabilities in the organization, or most likely a combination of these.
Data governance is a key component of EDM, and is also taking on a higher level of importance. Some of the key trends that are causing a greater need for data governance include:
- increasing data volumes from more and more sources, causing data inconsistencies that need to be identified and addressed, before decisions are made using incorrect information
- more self-service reporting and analytics (data democratization), creating the need for a common understanding of data across the organization
- the continuing impact of regulatory requirements such as GDPR, making it even more important to have a strong handle on what data is where, and how it’s being used
- an increasing need for a common business language to enable cross-departmental analysis and decisions
Regardless of the type of data an organization is managing – data warehouse, data lakes, big data, etc., a strong data governance capability is important. It will enable proactive management of data.
For example, a financial institution I worked with had very poor, inconsistent customer data. All of the customers with first, middle and last names had multiple differences, and addresses were inconsistent. This type of situation makes it very difficult to do any type of customer analytics, from identifying cross-sell opportunities to tracking and understanding customer experience. Data Governance can be a first step in identifying the issues, defining standards, and implementing changes in the business to align with these standards.
My prior article provided ideas on the initial phase of a data governance initiative, which is Discover. With the right assistance, an organization can begin data governance activities during the discover phase, which is typically a handful of weeks, then move right into implementation. The discover phase should have resulted in a set of recommendations and a roadmap to follow, both for immediate next steps as well as longer term considerations. The discover phase should have also provided some clarity on the initial areas of focus, such as:
- identifying key sources of truth and ensuring they are consistently used
- improving data quality across these key data sources
- developing standard business language and a business glossary for key data sources
- enhancing organizational metadata
- developing standards and policies for data access
- identifying business priorities for EDM initiatives
- developing and providing data and report certification
- developing and sharing an Enterprise Data Framework to provide data visibility and alignment
- and so on…
The actual implementation of data governance can take weeks or even months, depending upon the level of attention and engagement there is across the organization. One important thing to recognize is that most successful data governance programs become part of the day to day processes over time. So, as you embark on implementation, ensure that what you put in place is scalable and sustainable.
- start with a mission and objectives that includes the initial focus area
- get the structure and the right people in place, from executives to stewards
- establish the Data Governance charter and policies from the start
- ensure there’s visibility of decisions and progress across the organization
- implement with the goal of developing processes that can be embedded in the day to day business operations
- keep it as simple as possible, with the goal of less bureaucracy and more progress
Data governance used to be a nice to have, but due to the increasing focus and importance of data and analytics, it’s becoming a necessity that helps to drive data management across the enterprise.