Defining Value for Data Strategy Initiatives

Posted in: Business Insights, Technical Track
In our previous conversation, we explored how to decompose our data strategy work into actionable workstreams. Each workstream enables the identification of an executive sponsor, our definition of a minimum viable product and dependencies with other workstreams. Now, we explore the different methods to identify and measure the value produced by investments in new capabilities.
The first question for many executives when presented with a comprehensive data strategy will be, “what does this do for my organization?”. This is shortly followed by, “what will this all cost?” As we develop and socialize our data strategy, we must articulate both the outcome for the business and the costs to realize. Many lean canvases and other documents will default to soft measures including “better products,” “more competitive,” “faster time to market” or “lower overhead.” While these are easy to grasp for the masses, they are difficult to quantify and nearly impossible to use as justification for corporate investments.
Effective communication of the value of our data strategy can come from one of three methods:
  • Increasing Revenue – Value measures for revenue are often measured in the growth of revenue over time specific to the individual or multiple product lines. Often, they can be focused on identifying new buyers or increasing upsell/cross-sell opportunities.
  • Decreasing Operational Costs – Responsible management of operational costs is a focus for many organizations. Data strategy projects that drive lower operational costs through decreased staff decreased outsourcing costs or decreased facility costs have a positive impact on EBITDA and investor relations by showing new efficiencies can be gained while maintaining existing revenue streams.
  • Cash Flow Optimization – Many organizations in the consumer goods, grocery or clothing industries still have sizable inventories that must be managed to ensure a strong selection for consumers while managing corporate cash flows. Stock evolution can be especially challenging for organizations that span brick-and-mortar and e-commerce worlds with an overlapping inventory. Value measures that show improved use of corporate cash reserves can improve the company’s bottom line while enabling cash to be invested in new growth areas.
When we speak about the value of a given data strategy investment or workstream, we can refer to the value measure in one of two ways:
  • Absolute – Absolute measures take the form of “a return of $2MM in additional revenue over 18 months with the investment of $1MM in capital.” These measures are often needed for business plans and executive overviews of a data strategy program.
  • Relative – Relative value is the measure of different possible investments, workstreams or use cases compared to one another. They often take the form of “use case A will return 2x that of use case B, but only take 1/2 the time to implement”. Relative measures are important for prioritizing where teams will focus exploration and planning time.
Relative measures can often simplify the process of prioritizing work effort. At the same time, absolutes can ensure that stakeholders have an alignment for ongoing measurement of the impact and success of the initiative. The time dilemma is often a complicating factor for many organizations. The time dilemma is how you balance lower return use cases that can begin showing value sooner in conjunction with larger impact investments that may take months or years to realize. Solving the time dilemma is about finding the right balance for an organization. The key is to show early returns and progress while supporting the business justification on longer-term, sustained investment data strategy initiatives.
Value measures, both absolute and relative help us answer the question of what value our data strategy investments have produced and what potential value those investments can provide for the business over varying time horizons. By assessing both value types while defining our data strategy, we create a compelling picture of the impact our data investments will have on business operations and their returns. All while identifying ongoing measures we can use to assess project successes.
In our next posting, we will explore the intersection of our data strategy and data governance programs. While our data strategy sets the direction of how we use data across the organization, our data governance programs will ensure we are compliant with legal and industry obligations for the use of our data assets. Our data strategy will inform many aspects of our data governance programs including risk tolerance, data literacy and technical controls implementation.
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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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