The CEO Mandate: Become Value-driven, Not Data-driven

"Data is the new oil."

For the first time in my long tenure in the data and analytics business, the world has started to associate "value" to data. In fact, The Economist on their May 6, 2017 magazine cover declared, "The world's most valuable resource is no longer oil, but data," validating the digital future and putting an end to the way most organizations have previously regarded data in its collection, storage and associated reporting—as a necessary cost of doing business and one to be minimized, at that.

But what does "data is the new oil" really mean and how will it impact organizations?

In the same way that oil fueled the economic growth of the 20^th century, data will be the catalyst for the economic growth of the 21^st century. That data, including Big Data and Internet of Things (IoT) data, coupled with advanced analytics, such as Artificial Intelligence (AI...

The Data- and Value-Driven Mindsets, Defined

I often hear Senior Executives state that they want to become data-driven as if somehow having data is valuable in itself. The value of data isn't in just having it (data-driven). The value of data is determined by how you use it to create new sources of value (value-driven). To exploit the economic potential of data, Senior Executives must transition from a data-driven mindset (focused on amassing data) to a value-driven mindset (focused on exploiting the data to derive and drive new sources of customer, product and operational value).

Data may be the new oil or the most valuable resource in the world, but it is the customer, product and operational analytic insights (propensities) buried in the data that will determine the winners and losers in the 21^st century.

DEAN OF BIG DATA TIP:

Whenever I use the term "insights" in the book, I will also add the term "propensities" to reflect the predictive...

Understanding the Big Data Business Model Maturity Index Phases

Let's deep dive into each phase of the Big Data Business Model Maturity Index (BDBMMI).

DEAN OF BIG DATA TIP:

From an advanced analytics perspective, Phase 1 of the BDBMMI leverages Descriptive and Exploration analytics to tell you what happened and why. Phases 2 and 3 of the BDBMMI leverage Predictive Analytics (to predict what is likely to happen) and Prescriptive Analytics (to prescribe preventative or recommended actions based upon the predictive analytics). Phase 5 leverages Automation and Autonomous analytics to create a business and operating model that is continuously learning and adapting to environmental and industry changes.

Phase 1: Business Monitoring: The Business Monitoring phase seeks to monitor and report on "What's Happened?" with respect to the operations of the business. The Business Monitoring phase is where companies leverage Business Intelligence (BI...

Navigating the Big Data Business Model Maturity Index

The problem with the BDBMMI is that the journey up the index is not a continuous process. There are different challenges that must be addressed at each phase as organizations seek to become more effective at leveraging data and analytics to power their business models. Let's review the steps required to transition between the BDBMMI phases.

Transitioning from Business Monitoring to Business Insights

Here are the actions to transition from Phase 1: Business Monitoring to Phase 2: Business Insights:

• Identify an organizational Strategic Business Initiative; that is, what is the organization trying to accomplish over the next 12 to 18 months from a business perspective, and what are the financial, customer, and operational impacts of that initiative.
• Identify, validate, value, and prioritize the organization's key business and operational Decisions that the key stakeholders need to make in support of the targeted strategic business initiative. Cluster the decisions into common subject areas or Use Cases.
• Capture, cleanse, normalize, transform, enrich, and make available the relevant data sources in a Data Lake (a data lake is a centralized data repository that allows organizations to store both structured and unstructured data at the lowest level of granularity)—at the lowest or most detailed...

Transitioning from Business Insights to Business Optimization

Here are the actions to transition from Phase 2: Business Insights to Phase 3: Business Optimization:

• Evaluate the customer, product, and operational Analytic Insights uncovered in the Business Insights phase for business and operational relevance based upon the Strategic, Actionable, and Material value of those insights with respect to the business and operational objectives of the top-priority use cases.
• Develop Prescriptive and Preventative Analytics (preventative analytics are analytic outcomes that provide the analytic insights necessary to prevent an action or event from happening) in order to deliver actionable recommendations and propensity scores in support of the business and operational stakeholders' key Decisions with respect the top-priority business and operational Use Cases.
• Deploy a Data Lake with full data management capabilities (indexing, cataloging, metadata enrichment, governance...

Transitioning from Business Optimization to Insights Monetization

Here are the actions to transition from Phase 3: Business Optimization to Phase 4: Insights Monetization:

• Aggregate, cluster, and classify the customer, product, and operational insights, captured in the Analytic Profiles, into new revenue or Monetization opportunities. Create a rough order estimate of market size and viability of new monetization opportunities and assess how the new opportunities leverage and/or extend existing data and analytic digital assets.
• Create customer and operational Journey Maps to identify sources of customer and market value creation and then map those sources of Value Creation against the organization's internal data and analytic capabilities for Value Capture.
• Explore new customer and market "as a service" consumption models that not only support the new Monetization Opportunities but yield new sources of customer, product, and operational insights...

Transitioning from Insights Monetization to Digital Transformation

And finally, here are the actions to transition from Phase 4: Insights Monetization to Phase 5: Digital Transformations:

Digital Transformation is the creation of a continuously learning and adapting business model (AI-driven and human-empowered) that continuously seeks to identify, codify, and operationalize new, actionable customer, product, and operational insights (propensities) in order to optimize (reinvent) operational efficiency, enhance customer value creation, mitigate risk, and create new revenue opportunities.

• Drive business decisions by leveraging the Economic Value of Data. Create an operational environment that continuously seeks to capture new sources of customer, product and operational data.
• Leverage Design Thinking techniques to create a Collaborative Value Creation Culture that supports and fuels ideation and exploits innovative conflict. Force cross-organizational collaboration...

Testing the Big Data Business Model Maturity Index

Let's say that your business initiative is to "reduce unplanned operational downtime." That's a business objective that can apply to many industries including manufacturing, entertainment, transportation, oil and gas, power, financial services, telecommunications, and healthcare. And with the bevy of IoT devices and sensors exploding on the marketplace, now would be the perfect time to address this wide-ranging, value-destroying operational problem.

Reducing unplanned operational downtime, however, is more than just an IoT challenge, because the source of much of your unplanned operational downtime may have nothing to do with machinery and device problems. Instead, it may have lots to do with those pesky human customers and their unreliable behavioral patterns. So be sure to contemplate both human and device behavioral patterns. You can use Table 1.1 and Figure 1.5 to help with your homework assignment.

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Summary

Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, sets the stage for the rest of the book. If organizations are ready to embrace that "data is the new oil" and the catalyst for the economic growth of the 21^st century—then addressing this question becomes paramount to the organization's digital transformation success:

How effective is our organization at leveraging data and analytics to power our business models?

The BDBMMI provides a benchmark against which organizations can compare themselves. But equally important, the Big Data Business Maturity Model provides a roadmap or a guide. It guides organizations in transitioning from retrospective reports that tell them what happened, towards predictions as to what is likely to happen, and prescriptive, and preventative actions based upon those predictions. It guides organizations in helping to monetize their customer, product and operational insights, and finally towards digital transformation...

Further Reading

1. "The world's most valuable resource is no longer oil, but data," Regulating the internet giants, The Economist, May 6, 2017: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data

Homework

1. Where on the BDBMMI does your organization sit vis-à-vis best-in-industry usage of data and analytics?

   

2. How committed is business leadership to leveraging data and analytics to power the business?

   

3. How well is the culture of your organization prepared to navigate the BDBMMI?

   

4. What business initiative do you think could benefit the most from a tighter integration of data and analytics? What specifically could analytics do to power that initiative?