Why is Data Quality still an issue after all these years? To get an answer to the prevalent question, Wayne Eckerson and Jason Beard engage in a dynamic exchange of questions which lead us to the root cause of data quality and data governance problems. Using examples from his past projects, Jason shows the value of business process mapping and how it exposes the hidden problems which go undetected under the standard IT lens. 

In his most recent role as Vice President of Process & Data Management at Wiley, a book publisher, he was responsible for master data setup and governance, process optimization, business continuity planning, and change management for new and emerging business models. Jason has led business intelligence, data governance, master data management, Process Improvement, Business Transformation, and ERP projects in a variety of industries, including Scientific and Trade publishing, Educational Technology, Consumer Goods, Banking, Investments, and Insurance.  

Key takeaways:

• One reason why data quality problems still exist is because of the exponential growth of data and processes that create data in the past few years. Data quality and data governance practices can have a hard time keeping up with the speed of data creation.
• The promise of MDM has sometimes failed because the projects lack focus on the upstream business process. Trying to solve on the front end of the business process is where the real magic happens in data quality and data governance.
• Process tools like SIPOC, Statistical Process Controls, and Failure Mode Effects Analysis can be used to assess and diagnose data quality issues
• Mapping out the entire information pipeline through value stream mapping can help you identify the source of the problem.

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• Automation, better tools, new skills, and re-organized teams can help to solve data quality issues.
• The mantra should be: ‘If you get the process right, the data will follow’
• Processes and data often suffer from the same problem: lack of ownership and accountability. This is where process governance and data governance can help.
• Process improvement projects don’t happen in a vacuum. Executives are often already aware of the problems and are intuitive of the possible outcomes, and issue process improvement project charters to identify root cause. 
• In an ERP scenario, the software is designed to work in a certain way so you’ll have to mold your process so that it works with the software. One should be ready for the exceptions it will create.
• You can’t always see all the problems that are going to occur in an integrated data set just by looking at the data. You’ll have to see who’s involved in the process, rules and policies they are attempting to follow, and what kind of training they possess.
• For holistic data governance, you have to govern not just the input but the outputs as well.
• To keep the business interested in data governance, you have to show them how you are solving their problems, and sustain these through coordination with an ongoing Change Management effort.

Below is one question and answer from the podcast

Wayne Eckerson: How do you do data governance and process improvement so that business stays engaged and keep the momentum going?

Jason Beard: You’ll have to help them with the problems they are facing. Nobody wants to join data governance initiatives if they thought their data was perfect already or they weren’t experiencing any problems. When we stood up data governance here at Wiley a couple of years ago, we began with a formalized process around data issue management, identifying critical elements and data owners. We start with the data issues log, identify owners for those issues and conduct root cause analysis around what’s causing those problems. Some of that results in a process improvement initiatives and some of that results in a technology enhancement requests which are forwarded to IT.