Not to date myself too terribly, when I first started working after college, our word processing was on a Wang terminal with a green screen. The letters and memos were printed out on a giant dot matrix printer on tri-color carbon paper.
The white copy would go to the recipient, the yellow copy into the official file, and the pink copy would go into a folder with other correspondence. Then, that folder would be circulated to all staff in the division so they would be informed on events.
Our economic forecast was done on a mainframe. We could afford to run only four scenarios per quarter. Soon after, the desktop personal computer was released and with it came the concept of distributed data. Fast forward a number of years, and there was an explosion of data on our hands coming from many sources; the internet, enterprise systems (HR, Financial, etc.), and our own staff creating data sets on their PCs.
Our governance of data did not keep up with the explosion. Many organizations are now playing catch up by implementing data governance programs, often looking for that single source of truth.
Data governance done well means better, cleaner data. This leads to the ability to do better analytics, leading to better business decisions, and, ultimately, better business results.
So how do leaders successfully implement a data governance model and ensure staff are on board for the journey? Oddly enough, the best practices for data governance are very similar to best practices for good management in general. Some may recognize similarities to our own Results Management System practices.
- Define sponsorship and ownership. Without high-level sponsorship and business ownership, a data governance framework cannot succeed.
- Identify related roles and responsibilities. Data governance is not an IT project. It involves all parts of the organization. All need to participate in the journey.
- Start small. Large endeavors like data governance often fail if they are not broken into bite-sized chunks. Strive for quick wins and build momentum as you go.
- Set clear, measurable, and specific goals. You cannot control what you cannot measure. Celebrate when goals are met and use this to go for the next win.
- Focus on the operating model. A data governance framework must integrate into the way of doing business in your enterprise.
- Leverage metrics. Focus on a limited set of data quality performance measures or KPIs that can be related to the general performance of the organization. Tying these measures to the overall mission of the organization as closely as reasonable helps to bring your team along.
- Remember, data governance becomes a core process for your organization. It is not a project. Incorporate into your business framework.
- Finally, employ change management best practices. We use Prosci’s ADKAR model. I can not stress enough the importance of an intentional change management communications plan.
Whether your organization is employing data governance to grow, become more efficient, or to meet regulatory guidelines, having a single source of truth is invaluable. Regardless of reason, the end result of not doing these things is the same.
With bad data (or conflicting data) we make poor decisions. Often, we will not realize these are poor decisions until much later
Scott Harra, EVP of Marketing and Government Relations
Links To Resources
Scott Taylor – MetaMeta Consulting
Gwen Thomas – International Finance Corporation
Founder of the Data Governance Institute
Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness
Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance
Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program