Excessive Bureaucracy Is Killing Data Governance

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Click to learn more about author Steve Zagoudis.

Despite the best-in-class Data Governance policies and programs, failure rates are still too high. Current efforts often fall short of delivering trusted data, despite huge investments of time and money. People are looking for a better way, judging by the high popularity of “Data Governance 101” conference sessions. At the same time, seasoned veterans of DG show frustration with the way their programs have become stifled with bureaucracy.

There is a better way to approach Data Governance. It starts with changing how we think about data and systems. Earlier, we introduced the concept of Lean Governance based on the success of the manufacturing world. This is not to be confused with “Agile Governance.” The goal of Lean Governance is to achieve the results you desire in a relatively short time frame, without adding more burden to the organization.  

The first step to going lean is to let go of the ways you were taught to solve the problems with data. We start with the premise that Enterprise Data Management takes place in a data factory. Think of your raw data as inventory.  Data is processed (manufactured) into business information (finished goods). The goal of Lean Governance is to go from inventory to finished goods with as little waste and risk as possible. 

Just as the goal of your corporation is to create value for its stakeholders, the goal of your data factory is to provide timely, accurate data. Focus on the constraints of data availability and quality. Anything that does not add value is considered waste. Where there is waste there is risk.  

Lean production in the 1980s utilized the concept of minimum-quality requirements to strike the balance between sufficiency and perfection. This was critical to achieving the required factory throughput. Today in manufacturing, the quality bar is constantly being raised by increased competition, increased consumer demands for quality, faster design and development cycles, and regulatory pressures. 

For the data factory, a host of forces combine to take the bar for Data Quality ever higher, including:

  • Demand for analytics
  • Regulatory expectations
  • Corporate risk appetite
  • Process automation
  • Need for fraud monitoring

In addition to quality requirements, Lean Governances focuses on minimum compliance requirements and minimum audit requirements. What is the leanest governance implementation that will satisfy the organizations’ overall minimum quality, compliance, and audit requirements?  Answering this question unleashes the power of Lean Governance.  

Future blog posts will continue to explore how to define and implement these minimum requirements with the least amount of waste and risk possible. Any approach that can dial back excessive bureaucracy to increase stakeholder value should be most welcome.

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