"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 20th century, data will be the catalyst for the economic growth of the 21st century. That data, including Big Data and Internet of Things (IoT) data, coupled with advanced analytics, such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), will be the guiding and differentiating force that drives an organization's business and operational success, and ultimately, their digital transformation.
DEAN OF BIG DATA TIP:
The contents of Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, have been covered pretty extensively in my previous two books—the "Big Data MBA" and "The Art of Thinking Like a Data Scientist." If you've read those books, you can probably skim this chapter as a refresher. If you haven't read those books, then grab a coffee and let's dive in.
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 21st 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 nature of insights. "Propensities" are an inclination or natural tendency for customers, products and operations to behave or act in a predictable way.
Consequently, if organizations are ready to embrace that "data is the new oil" and "data is the most valuable resource in the world," then the single most important question the organization must answer is:
"How effective is our organization at leveraging data and analytics to power our business and operational models?"
In this digitally-transforming world, the only sustainable and defensible differentiation is an organization's ability to exploit the economic value of its data and analytic assets to deliver analytics-infused customer, product, service and operational outcomes. It won't be the technology platform (whose differentiation is quickly eroded) and it won't be the user interface (which is easy to replicate in this digital-centric world). No, the source of sustainable, competitive differentiation will be the organization's ability to uncover superior customer, product, service, and operational insights, and interweave those insights into the organization's operational systems and value creation processes.
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I am going to introduce several concepts and ideas in this chapter that will be covered in more detail later on in the book.
Let's put the matter into context for your organization. How is your organization leveraging data and analytics to:
Most organizations have no idea how to answer these questions because they lack a "best-in-industry" benchmark against which to compare themselves. Organizations need this benchmark to:
The "Big Data Business Model Maturity Index" is a framework that I created to help organizations:
Figure 1.1: The Big Data Business Model Maturity Index
Figure 1.1 is the core of this book. If you don't understand what "good" looks like from a data and analytics perspective—if you don't know how far you can push your organization to exploit the business potential of data and analytics—then how is your organization ever going to master the economics of data, analytics and digital transformation? The mastery of the economics of data, analytics and digital transformation is what will distinguish the winners from the losers in the 21st century! Yes, mastering Figure 1.1 is a matter of survival.
Let's deep dive into each phase of the Big Data Business Model Maturity Index (BDBMMI).
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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) and data warehouses to generate management and operational reports and dashboards that communicate current operational status. The Business Monitoring phase leverages rudimentary analytics such as benchmarking (against previous periods, industry benchmarks, and plan) and indices (brand development, customer satisfaction, product performance, financials) to identify or flag under- and over-performing business areas that require more management or operational attention.
Unfortunately, running your business on retrospective reports and dashboards that tell the organization "What's Happened?" is like trying to drive your car using the rear-view mirror. It is easy for organizations to get lackadaisical and declare "mission accomplished" at the completion of this phase. And while the Business Monitoring phase is a great starting point, on its own the Business Monitoring phase is insufficient in helping organizations to become more effective at leveraging data and analytics to power their business models.
Organizations must push beyond this phase if they seek to become more predictive and prescriptive in optimizing the operations of the business. And that means running smack into the dreaded Analytics Chasm.
First off, the Analytics Chasm challenge is not a technology issue. Organizations have dumped tens if not hundreds of millions of technology dollars into that chasm. And that's what organizations have gotten wrong—chartering the Information Technology (IT) department to cross the Analytics Chasm.
If organizations want to cross the Analytics Chasm phase to become more predictive and prescriptive in their business operations, then they need to embrace an economics mindset, not a technology mindset. And that requires senior management to let go of outdated legacy data and analytic beliefs.
DEAN OF BIG DATA TIP:
Economics is a branch of knowledge concerned with the production, consumption, and distribution of wealth (or value).
An economics mindset can help organizations cross the Analytics Chasm by:
Figure 1.2: The Economics of Crossing the Analytics Chasm
Crossing the Analytics Chasm requires organizations to leverage the economics of data and analytics using a use case-by-use case approach to make the leap. We will discuss the use case-by-use case approach in more detail in Chapter 4, University of San Francisco Economic Value of Data Research Paper, an approach which is the key to determining and exploiting the economic value of the organization's data.
Phase 2: Business Insights: This phase seeks to uncover actionable customer, product, and operational insights buried within and across the organization's data. The Business Insights phase is where the organization seeks to predict "what is likely to happen next" with respect to its customers, products, and operations. The Business Insights phase explores a wide variety of internal and external data sources, using data engineering techniques (for example, data transformation, data enrichment, metadata enhancement, data blending) and a variety of advanced analytic techniques (predictive analytics, data mining) in an effort to uncover strategic, actionable, and material insights that might be useful in predicting performance.
The Business Insights phase is where the collaboration between the business stakeholders and the data science team becomes indispensable in identifying those variables and metrics that might be better predictors of performance.
The data science team seeks to identify and codify the customer, product and operational insights (trends, patterns, and relationships) buried in the data. These insights form the basis for transitioning to Phase 3: Business Optimization.
Phase 3: Business Optimization: The Business Optimization phase seeks to embed prescriptive analytics (recommendations and propensity scores) into the operational systems in order to automate the optimization of the organization's key operational processes. These systems seek to constantly optimize their operations based upon each customer engagement or operational interaction. In this phase, organizations seek to automate parts of their business operations with advanced analytic modules that automatically optimize operational performance. This phase leverages predictive analytics, prescriptive analytics, and supervised and unsupervised ML to create specific, operational recommendations.
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Supervised Machine Learning uncovers relationships between variables buried in datasets given a known outcome or label (unknown knowns); it learns and codifies the relationships between multiple dependent variables and a known outcome variable.
Unsupervised Machine Learning uncovers relationships between variables in datasets in which there is no known outcome or label (unknown unknowns): it discovers and codifies previously unknown relationships between independent variables.
Phase 4: Insights Monetization: Organizations are realizing that the best way to monetize their data isn't to sell it, but instead to leverage the customer, product, and operational insights (propensities) that have been gathered throughout the Business Insights and Business Optimization phases to create new revenue or monetization opportunities. During the Business Insights and Business Optimization phases, organizations should be gathering insights—propensities, tendencies, patterns, trends, associations, relationships—about their key business and operational entities (for example, customers, doctors, teachers, technicians, stores, compressors, chillers, turbines, and motors).
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The Business Insights and Business Optimization phases are internally focused; that is, they focus on leveraging data and analytics to predict, prescribe, and optimize the organization's internal use cases. However, the customer, product, and operational insights gathered during the Business Insights and Business Optimization phases will be instrumental in the externally focused Phase 4: Insights Monetization.
Insights about the organization's key business and operational entities can be aggregated, mined, classified, and clustered to identify unmet or under-served customer, product, operational, and market needs. These aggregated insights form the basis for identifying and creating net-new monetization or revenue opportunities such as new products, new services, new channels, new audiences, new partners, new audiences, new markets, and even new consumption models.
Phase 5: Digital Transformation: The final phase of the BDBMMI has as much to do with culture as it does with data and analytics. The key to Digital Transformation success is to create a culture that encourages the continuous exploration, creation, sharing, reuse, and refinement of an organization's digital and human assets. As we will explore in Chapter 8, The 8 Laws of Digital Transformation, Digital Transformation is about creating an environment where advanced analytics such as RL and AI (which I will cover in Chapter 6, The Economics of Artificial Intelligence) are augmenting the capabilities of the front-line employees to explore, learn, and adapt at the point of customer engagement and operational execution.
Digital Transformation must also address the organization's compensation and rewards structure to incentivize the business functions to share, reuse, and refine the organization's data and analytic assets. This likely means transforming how you hire, train, promote, and manage the organization to create this sharing and continuous learning and adapting culture.
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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.
Figure 1.3 summarizes the characteristics of each of the different phases of the BDBMMI:
Figure 1.3: Big Data Business Model Maturity Index Phase Characteristics
Now let's understand the action plan that organizations can follow to advance up the BDBMMI.
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.
Here are the actions to transition from Phase 1: Business Monitoring to Phase 2: Business Insights:
Figure 1.4: The "Think Like a Data Scientist" Methodology
Here are the actions to transition from Phase 2: Business Insights to Phase 3: Business Optimization:
Here are the actions to transition from Phase 3: Business Optimization to Phase 4: Insights Monetization:
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.
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False Positives and False Negatives are situations where the predictive models are wrong with their conclusions. Understanding and managing for the costs of False Positives and False Negatives are critical to making informed policy and operational decisions. For example with a disease:
Now we want to test what we have learned about the BDBMMI with a little homework assignment.
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.
Table 1.1 provides a checklist of the steps to navigate the Big Data Business Model Maturity Index.
Transition Phases | Transition Phase Characteristics |
Crossing the Analytics Chasm from Business Monitoring to Business Insights |
|
From Business Insights to Business Optimization |
|
From Business Optimization to Insights Monetization |
|
From Insights Monetization to Digital Transformation |
|
Table 1.1: Checklist of BDBMMI Transition Steps
Figure 1.5 summarizes the Big Data Business Model Maturity Roadmap.
Figure 1.5: Big Data Business Model Maturity Index Roadmap
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 21st 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.
Crossing this Analytics Chasm is not a technology challenge; it's an economic challenge for how organizations leverage the economic value of data to derive and drive new sources of customer, product, and operational value.
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