Designing Data Governance Programs that Deliver Business Value

Posted in: Business Insights, Technical Track
data governance

As organizations embark on their Data Governance journey, many struggle with justifying the costs with program outcomes that have traditionally been viewed as required but not delivering significant business value. This often pits data governance programs against product initiatives in the quest for yearly budget increases. But this tension can be managed, and data governance programs can deliver strong business returns when structured properly and maintain a focus on high impact areas of the business and prioritized with strong customer experiences.

 

Beginning with defensive data governance programs, these are initiatives focused on lowering a company’s risk profile and ensuring compliance with regulatory and contractual requirements. The ultimate goal of defensive programs is to lower the company risk to as close to zero as feasible from fines, regulatory enforcement, or threat of legal action.  Common measures of defensive programs will be the cost of implementation of new processes and technology, compared to the potential costs of enforcement or legal actions against the organization.

Defensive data governance programs will often be spurred by outside forces, the most common recently being CCPA, CPRA and GDPR.  Each requires specific business processes to be defined and varying levels of automation implemented. 

CCPA has legally defined fines for violations of law relating to consumer data handling. In the most egregious cases, those defined as intention have a fine of $7500 per incident. For the unintentional, the fine goes down to $2500. Organizations will look at this delta to determine their level of investment in CCPA systems for managing consumer requests. For a hypothetical organization with 1-million in-scope consumer records, a company could have an intentional exposure of $7.5MM. By investing $1MM-$2MM, that exposure can be lowered by $5MM.

Offensive data governance projects will often have significantly higher rates of return for a business, enabling improved customer experiences and lower rework costs for business processes. Offensive projects often take the form of data quality, integration of disparate systems or annotating data to enable easier discovery and analysis. When structured deliberately to improve processes and experiences in measurable ways, offensive projects will become key enablers for larger corporate initiatives.

Key metrics for offensive projects fall into two categories, projects that provide additional revenue through new sales or better attach rates. Data governance can fuel this by ensuring high quality data is well integrated and available, providing a friction-free experience for consumers and consumers of data. Projects will often also take the form of lowering costs, this can be done through eliminating rework and error-prone manual processes.

Offensive projects often look at returns on investment (ROI), and will take the form of evaluating the potential boost to lifetime customer value, the ability to increase collective lifetime customer value through better integrated data, more seamless customer experiences and more rapid engagement with customer support requests can often provide an ROI of 3-4x.

Business value has two dimensions, it must be measurable so that the effort can be prioritized against other business tasks, and it must be work that creates bragging rights and excitement within the organization. Excitement from sponsors of data governance projects are a valuable asset, they build teams of supporters that evangelize the win, the approach and the value to the organization. Data governance programs should prioritize ongoing financial wins over one-time improvements. Programs that increase consumption of services and decrease operational costs have sustainable, ongoing savings, and improved financials.

In my next post, I’ll outline the five phases of a data governance program and how to structure your programs to move at high velocity with positive business impacts, while continually improving. Make sure to sign up so you don’t miss it.

 

 

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About the Author

VP Analytics
Joey Jablonski is VP of Analytics at Pythian, he leads strategic engagements assisting customers in developing their data strategy, defining and executing on data governance programs and building analytical models to power the modern data-driven organization. Prior to Pythian, Joey was VP of Product at Manifold, where he brought a product mind-set is part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. Joey led engagements across industries including high tech, pharmaceuticals and for the federal government. Before Manifold, Joey held executive leadership positions at Northwestern Mutual, iHeartMedia and Cloud Technology Partners. He brings 20+ years of experience in software engineering, high performance computing, cyber security, data governance and data engineering.

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