The journey from data and AI to value does not start with data or AI.
Data and analytics are the new sources of value creation and competitive advantage in today’s economy. Some organizations have “cracked the” code in applying AI/ML to their data to uncover the customer, product, service, and operational predictive propensities (propensity to buy, churn, renew, default, fail, break, return an item, get sick, suffer a head injury, experience a heart attack) that drive new sources of customer, product, service, and operational value.
Unfortunately, most organizations are still struggling to create value from their data.
The organization’s ability to uncover, codify, and effectively apply these predictive customer and product propensities leads to a massive market valuation shift. The most valuable companies in the world have transitioned from purveyors of products and services to purveyors of knowledge embedded in their products and services. These organizations are mastering the economics of data and analytics to create more compelling and differentiated customer experiences, optimize and automate business processes, and create innovative business and operational models (Figure 1).
Figure 1: Market Shift to Organizations that are Purveyors of Knowledge
Data and analytics are modern-day economic assets that can create unbounded sources of customer, product, service, operational, and market value. However, many organizations struggle to leverage these assets effectively and efficiently.
- According to an MIT Technology Review Insights survey, only 13% of organizations deliver on their data strategy despite increasing investment in big data and AI initiatives.
- McKinsey found that organizations are investing trillions to become more data-driven, but only 8% successfully scale analytics to get value from their data.
- Bain & Company revealed that only 4% of companies said they have the right resources to draw meaningful insights from data and to act on them.
Disturbing results, to say the least. The answer to this challenge is right before us, but the first step requires organizations to adopt a new mindset.
Adopting an Economics Mindset
The starting point for addressing the challenge of creating value from the organization’s data is to adopt an economics mindset.
Economics is a “value in use” asset valuation methodology where the value of the asset is tied directly to the value that the use or application of that asset derives and drives.
Unfortunately, there is no value in possessing data. In fact, costs and potential liabilities are associated with storing, protecting, backing, and managing the organization’s data.
With an economics mindset, the value of the organization’s data is unleashed by uncovering and codifying the customer, product, and operational predictive behavioral and performance insights buried in the data and then applying those insights to optimize the organization’s business initiatives and top priority use cases (Figure 2).
Figure 2: Stages of Data Monetization
The four stages of Data Monetization are:
- Stage 1: Data is a Cost. This stage reflects the increasing costs associated with data storage, management, and governance and potential regulatory and compliance risks/costs associated with not properly managing or protecting your data.
- Stage 2: Data Monetization Exploration. This is the Proof of Values (POV) stage, where pilots around well-vetted use cases build organizational awareness and hands-on experience around the potential of an organization-wide data monetization effort.
- Stage 3: Data Monetization Value Realization. The CDO spearheads the rapid operationalization and subsequent governance of the organization’s data monetization efforts by driving data and analytics re-use and refinement.
- Stage 4: Data Monetization Value Acceleration. By creating data products (versus data sources) and AI apps (versus AI models), data and analytic asset enhancements ripple across the organization, causing a rapid acceleration in value realization.
The key to getting value from the organization’s data doesn’t start with data or AI / GenAI. If you want to get value from your organization’s data, start by understanding how your organization creates “value” and measures its value creation effectiveness.
Let Jason and the Argonauts show us how that’s done.
Schmarzo and the Value·Nauts
In the 1963 movie “Jason and the Argonauts,” Jason must prove himself by retrieving the Fleece from the land of Colchis. Along the journey, Jason and his crew (including the ever-cool Hercules) face a multitude of dangers, including harpies, a giant bronze statue, a hydra, and an army of skeletons, which are friggin’ scary when you’re eight years old. Many lives are lost in the perilous journey (Figure 3).
Figure 3: Jason and the Argonauts’ Journey to Value without a Map
The CDO journey from data to creating value resembles Jason’s journey, filled with danger and challenges. These challenges include outdated data architecture, a lack of widely available analytic capabilities, and data management and governance deficiencies. These issues significantly impact the CDO’s ability to create quantifiable value and gain business leadership support.
However, what if we reframed our journey and started by understanding how the organization creates and measures its value creation effectiveness? The journey now looks like Figure 4.
Figure 4: AI / Data Journey Starts with Value
Key steppingstones on this journey map are:
- (1) Business initiatives or goals: The objectives that the business wants to achieve over the next 12 to 18 months
- (2) Desired outcomes: The expected results of the business goals from the perspective of the key stakeholders
- (3) Decisions and KPIs: The decisions that the stakeholders need to make and the KPI and metrics against which they will use to measure the effectiveness of the outcomes and decisions
- (4) Use cases: Packaging the key stakeholders’ critical decisions, desired outcomes, and supporting KPIs and metrics that support the business initiative. Note: this step includes the cross-stakeholder collaboration to identify, validate, value, and prioritize the use cases
- (5) Predictive insights: Individual entities predicted behavioral and performance propensities, tendencies, and inclinations
- (6) Tools: The data transformation and analytic tools that mine the data for predictive insights
- (7) Data: The raw material that creates value through data and analytics
Note: this journey should look familiar to any of my “Thinking Like a Data Scientist” students. It’s just missing the design canvases that help guide, validate, and simplify the journey process.
Schmarzo and the Value·Nauts Summary
Starting your AI and Data Journey with value has many advantages, such as:
- Getting business buy-in by involving the key stakeholders early and throughout the journey
- Establishing credibility by quantifying the value upfront rather than after the fact
- Clarifying the desired outcomes, decisions, and metrics for each use case and avoiding unnecessary data, analytics, and architecture complexities
- Saving time, money, and resources by avoiding Big Bang AI/GenAI projects that take too long and have high risks
- Building the data and analytics capabilities and infrastructure incrementally, based on the ROI of each use case
- Accelerating the time-to-value and reducing the implementation risks by leveraging the economies of learning
With these advantages, you’ll be ready to embark on your own Value·nauts journey that starts with value and uses data and AI to create new sources of value for customers, products, services, and operations.
You can check out my “The journey from data and AI to value does not start with data nor AI” YouTube video if you want to learn more about the Value to Data journey.
 MIT Research Study: “Only 13% of Organizations are Delivering on their Data Strategy.”
 Bain & Company: “The value of Big Data: How analytics differentiates winners”