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Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed

Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed
I think, therefore I can.

I’m sure we all remember the story of “The Little Engine That Could.”

A little railroad engine was built for pulling a few cars on and off the switches. When more powerful engines are asked to pull a load over a steep hill, they respond “I can’t; that is too much a pull for me”. So, the little engine asked to do the job. As the little engine bravely starts pulling the load, the engine starts puffing faster and faster chanting “I think I can, I think I can, I think I can.”  And as the little engine gets near the top and the completion of the task, its chant shifts from “I think I can” to “I know I can.”


Okay folks, our industry is in need of a “I know I can” moment!

Thomas Davenport and Randy Bean recently released a marvelous report titled “Data and AI Leadership Executive Survey 2022.”  This is an absolutely great “must read” survey for every business executive who is seeking words of guidance to make more effective use of data and AI to transform their businesses. Some of the words of guidance from the report include:

  • Investment in Data and AI initiatives continue to grow as efforts deliver measurable results
  • The Chief Data and Analytics Officer (CDAO) role continues to grow and evolve, while turnover and stability remain challenges in what is still a new role
  • The CDAO role is focusing on business growth and analytic outcomes
  • Organizations are targeting data investments in key areas of need

However, all is not well in Data Value Creation Candy Land…


Figure 1: Data Value Creation Candy Land

For many of the organizations interviewed in the study, achieving data-driven leadership remains an elusive aspiration.  And nowhere is this challenge more evident than the stall that has occurred in organizations’ ability to create a data-driven culture, as highlighted by the table on Page 10 of the research report (and in Figure 2).


Figure 2: Challenges in Creating a Data-driven Culture

The progress made between 2019 and 2022 to become more data-driven has not only stalled, but has actually decreased in many respects including:

  • Just 19.3% have established a data culture, down from 28.3% in 2019
  • Just 26.5% have created a data-driven organization, down from 31.0% in 2019
  • Just 39.7% are managing data as a business asset, down from 46.9% in 2019
  • Just 47.4% are competing on data and analytics, flat from 47.6% in 2019
  • Just 56.5% are driving business innovation with data, down from 59.5% in 2019

Compare this trend to Gartner’s AI “Business Value Forecast” trend in Figure 3.


Figure 3: Business Value Forecast by AI Type

Munging these two charts together, yields a very troubling picture in Figure 4.


Figure 4: Disconnect between Data-centric Culture and Economic Value of AI

While the projected Business Value realized from the application of AI continues to accelerate, organizations are failing to instill a data-driven culture.  Given the outsized potential of data and analytics to transform every use case, every industry, and every institution, which should organizations do?

Stop Focusing on Becoming Data-driven!

Being data-driven ain’t crap.  Total waste of time and breathe.  And this is where data is very different than oil.  While possession of oil has value, the possession of data provides no value, and in fact, there are significant (and increasing) costs, risks, and potential liabilities associated with the storage, management, and governance of the data (corresponding to Stage 1 of the Data Monetization Roadmap in Figure 5).


Figure 5: “4 Stages of Data Monetization

The value of data is only realized when you apply the customer, product, and operational insights buried in the data to optimize the organization’s key business initiatives and supporting use cases.

Instead of focus on becoming value-driven, focus your organization on mastering how to uncover the customer, product, service, and operational insights buried in your data sets to optimize key business processes, mitigate compliance and regulatory risk, optimize operational excellent, uncover new revenue streams, and create a more compelling differentiated customer experience! Focus on becoming value-obsessed!

Becoming Value-obsessed

So, what can you organization do to overcome the data-driven cultural stall highlighted in Figure 2?  How can organizations get over the hump in transitioning from data-driven to value-obsessed?

Here are my 6 steps in transitioning to becoming value-obsessed.

  1. Step 1: Understand How Your Organization Creates and Measures Value.  One’s value- obsessed journey must start by understanding the business initiatives around which the organization creates “value” (e.g., improve customer retention, reduce unplanned operational downtime, reduce hospital acquired infections, improve 4-year graduation rates).  This also includes identifying the KPIs and metrics against which the organization measures the value creation effectiveness of those business initiatives.  By the way, understanding how your organization creates and measures value is not a “show up and throw up” exercise that’s done to appease the business executives.  Identifying the KPIs and metrics against which value creation effectiveness is measured is the very heart of the organization, and your data, data science, and data management strategies.  And today’s organizations are going beyond traditional financial metrics to measure value creation by including operational excellence, customer satisfaction, employee satisfaction, partner/ecosystem health, risk reduction, diversity, environment, and ethics metrics.
  2. Step 2:  Monetize Business and Operational Use Cases.  After identifying how your organization creates and measures value, the next step is to identify the business and operational use cases (I define use cases as clusters of decisions and KPIs around a common subject area) that support and power the organization’s value creation processes. This requires close collaboration with the business stakeholders to identify, validate, value, and prioritize the business and operational decisions that these stakeholders need to make in support of the value creation processes and the KPIs against which decision effectiveness these stakeholders will measure decision effectiveness.
  3. Step 3:  Focus on Business Outcomes, Not Technology Outputs.  The value-obsessed journey focuses on delivering meaningful and relevant business and operational outcomes to the key internal and external stakeholders.  Delivering meaningful and relevant outcomes means developing an intimate understanding of what these stakeholders are trying to accomplish.  Design Thinking tools like Personas and Customer Journey Maps will be your BFFs in this effort.
  4. Step 4: Empower the Frontlines.  The execution of most of the organization’s value creation processes occur at the frontlines of the organization – at the point of customer engagement and operational execution.  So, if you want to become value-obsessed, then empower those front line “value creation engines” with the customer, product, service, and operational insights (individualized predicted behavioral and performance propensities) necessary to optimize the execution of the business and operational use cases.
  5. Step 5: Embrace Economics, not Technology.  Economics is the branch of knowledge concerned with the production, consumption, and transfer of wealth or value.  Economics is the most powerful concept in not only business, but in society.  And while technology is very important, technology only exists to support economics and the creation and distribution of value and wealth.  Also, while technologies are constantly changing and evolving, the ability to create value using economics is not.
  6. Step 6: Master the Economies of Learning.  In knowledge-based industries, the ability to learn and adapt more quickly than your competition is the key to sustainable success.  Yes, the economies of learning are more powerful than the economies of scale in knowledge-based industries. And soon, every industry will be a knowledge-based industry. But note, economies of learning apply to both machine / model learning (driven by AI / ML) as well as human learning (driven by curiosity, creativity, and innovation. Integrating the economies of learning across both machines / models and humans is the real game changer!

Figure 6: Six (6) Steps to Becoming Value-obsessed

Summary: Fallacy of Becoming Data-driven – Part 1: Becoming value-obsessed

Okay, the 6 Steps to becoming value-obsessed is a major step forward in overcoming the data-driven stall that we saw in Figure 2.  But these 6 steps are not sufficient.  Follow only these 6 steps, and you probably won’t see any major uptick in the organization’s data-driven journey.  We must go one step further.

In part 2 of this series, I will focus on cultural transformation, employee empowerment, and how to create a culture of data-driven, value-obsessed business and operational innovation!

Yea, baby!!