The year is 1995 and the time is 1:00am. Nothing to watch on television but cheesy infomercials as you flick through channels. You happen to stumble upon a curious invention, one you actually might use in your everyday life. A rotisserie chicken cooker that takes no effort to cook at all. Simply amazing you think to yourself, you just “set it and forget it”, how much easier could it get. Fast forward to 2015 and it is 8am on a Monday and as you take your first sip of coffee, your Chief Data Officer calls you demanding to know why your team has taken so long in identifying and resolving data errors in your system. Your new global ERP rollout was completed several months ago which did not go as smooth as hoped. Bad data and irrelevant data were loaded into the new system causing major data quality issues, which are affecting business processes. One of those glaring business interruptions is causing the company to miss shipping deadlines to their customers due to the fact that incomplete customer data was loaded into the new ERP system.
Right about now you wish your data governance initiatives were just like that chicken rotisserie cooker, however data governance is not a “set it and forget it” discipline. An effective data governance program requires sound metrics, ongoing monitoring and adaptation to evolve your system. Best practices and lessons learned should be fundamental principles to guide your data governance, along with change management, an important element that gives you the flexibility to see changes in data values and how data is produced and consumed across multiple functional areas. Change management goes hand-in-hand with proactive impact analysis when revising data values, accommodating new business processes and adding or removing data sources.
In order to keep up with changes in your IT landscape, it’s important that your implementation and management include:
Focus on reusability and automation. As the data governance program evolves, it’s not necessary to start from scratch when extending the discipline to new functional areas. Look to reuse the rules and code developed at the outset across similar projects, and implement workflow-driven automation to reduce cycle times and accelerate value.
Empower data stewards. As a key role in data governance, it’s important that data stewards have a good mix of IT and business skills, and that they’re equipped with the tools and authority they need to excel. Web-based applications to identify and remediate data errors and supply real-time metrics are especially valuable.
Embed transparency and accountability. Your data governance processes should be highly transparent to stakeholders in terms of data itself, quality metrics and controls over data. Accountability for processes and decision-making should be clear and thoroughly defined.
It is also important to know that once a data governance program is in place, to not tail off your efforts to regularly monitor and report on key metrics. Systematic measurement enables you to continuously optimize your data, and should be a focus area to help you make the most of your data governance efforts. Remember that data governance is not a “set it and forget it” discipline, it takes great effort, but with the correct solutions to offer reusability, empowerment and transparency in place, you’ll get ongoing data quality cooking in no time.