The Economics of Data, Analytics, and Digital Transformation

By Bill Schmarzo
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  1. The CEO Mandate: Become Value‑driven,Not Data-driven

About this book

In today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization’s data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.

The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company’s operations through AI and machine learning.

By the end of the book, you will have the tools and techniques to drive your organization’s digital transformation.

Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book:

"Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."

Publication date:
November 2020
Publisher
Packt
Pages
252
ISBN
9781800561410

 

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 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.

 

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 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.

DEAN OF BIG DATA TIP:

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:

  • Optimize key operational and business processes?
  • Mitigate security, compliance and management risks?
  • Uncover new sources of customer, product, operational and market revenue?
  • Create a more differentiated, compelling customer experience?

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:

  • Compare and assess their performance against the best-in-industry from a data and analytics usage perspective.
  • Provide a simple and actionable roadmap to become more effective at leveraging data and analytics.
  • Envision what "good" might look like for their organization.

The "Big Data Business Model Maturity Index" is a framework that I created to help organizations:

  1. Map or benchmark where they sit today in comparison to data and analytics "best-in-industry" practices, and
  2. Provide a roadmap for becoming more effective at leveraging data and analytics to power the organization's business models (see Figure 1.1).

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.

 

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) 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:

  • Transforming from an IT mentality of using data and analytics to monitor the business, to a business mindset that seeks to predict what's likely to happen in order to prescribe actions to prevent or monetize that prediction.
  • Moving beyond aggregating data to reduce storage and data management costs, to analyzing and mining all the detailed transactions and engagement data at the level of the individual (human or device).
  • Expanding data access from a restrictive tabular data model to providing access to all data sources—internal and external, structured and unstructured—that might yield useful customer, product, and operational insights at the level of the individual (human or device).
  • Transitioning from a batch data processing environment to an operational model that can process and analyze the data in real-time in order to catch customers or operations "in the act" to create new value creation and monetization opportunities (see Figure 1.2).

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.

DEAN OF BIG DATA TIP:

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).

DEAN OF BIG DATA TIP:

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.

DEAN OF BIG DATA TIP:

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.

 

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 level of Granularity—including historical operational and transactional data, internal unstructured data (consumer comments, technician notes, engineering specs), and external or third-party data (weather, traffic, local events, economic growth metrics, financials).
  • Create an analytics (data science) Sandbox (data lake) that allows the data science team to rapidly ingest data (as-is; no schema required), quickly provision the necessary data engineering and data science tools. This allows the data science team to explore the data, discover patterns, trends, and relationships buried in the data, and test different combinations of data, data enrichment techniques, and analytic algorithms for their Predictive Capabilities in a fail fast/learn faster environment.
  • Deploy and use Predictive Analytics to uncover potentially actionable and predictive customer, product, and operational insights (example, propensities, preferences, patterns, trends, interests, passions, affiliations, associations, and sentiments) buried in the data.
  • Train business users to "Think Like A Data Scientist" to unleash the organization's tribal knowledge and brainstorm variables and metrics that might be better predictors of performance (see Figure 1.4).
  • Create a "Right time" analytics capabilities that monitors individual and device "behaviors" to flag anomalies or behavioral changes that might be worthy of further analysis and action.
  • Master Data Science capabilities including Data enrichment, Data visualization, Statistics, Diagnostic analytics, Data mining, and Predictive analytics.
  • Master Design Thinking capabilities such as Personas (key personality and/or operating traits of a key stakeholder who is relevant to the problem at hand), Stakeholder Maps (a relationship map between stakeholders who either impact or are impacted by the problem at hand), Envisioning (a facilitated brainstorming exercise across a diverse set of stakeholders), Facilitation (a discovery and exploration process guided by a trained facilitator), Hypothesis Development (a design template that defines the criteria against which a successful data science engagement will be measured), and Illustrative Analytics (mocked up analytics designed to nurture and validate stakeholder analytic requirements).
  • Develop Business Case with financial and business justification and supporting Return on Investment (ROI) analysis.

Figure 1.4: The "Think Like a Data Scientist" Methodology

 

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, security) that supports Collaborative Value Creation between business stakeholders, IT, and the data science team. The data lake must support the data science team's need for rapid data ingestion, feature engineering, data exploration and discovery, data enrichment, and analytic model development and testing.
  • Capture or store the customer, product, and operational insights in Asset Models (Analytic Profiles for humans and Digital Twins for devices and machines) that aggregate the analytic insights about the organization's key business entities.
  • Leverage DevOps disciplines to operationalize the customer, product, and operational insights into the organization's key operational systems. Operationalize the prescriptive recommendations with modern DevOps techniques to embed the analytic insights (propensities) into operational systems in an easy-to-understand, easy-to-consume format.
  • Measure Decision Effectiveness. Instrument the analytic recommendations in order to determine the effectiveness of the recommendations. Use the results of the effectiveness measurements to refine and finetune the analytic models.
  • Master Data Science capabilities such as feature engineering, ML, DL, reinforcement learning and AI.
  • Master Design Thinking capabilities such as customer journey maps, mockups, prototyping, and storyboards.
  • Master Value Engineering to quantify the economic value of data and analytics on the organization's top priority business and operational use cases (a topic that we will discuss in detail in Chapter 2, Value Engineering: The Secret Sauce for Data Science Success).
 

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 that can be further mined to derive and drive new sources of value.
  • Apply Data Science concepts such as Asset Models, Analytic Profiles, and Digital Twins (a digital representation of a physical asset such as a wind turbine or a compressor) to capture and fuel new sources of customer, product, and operational value.
  • Apply Design Thinking concepts such as Personas, Prototypes, Customer Journey Maps, and Storyboarding to validate these new sources of customer, product, and operational value.
  • Test, validate, and Operationalize the data management and data science development and production processes.
  • Create a Business Plan that articulates and quantifies how the organization can operationalize these new monetization opportunities.
 

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 around purposefully constructed operational conflicts (that is, increase X while decreasing Y) to fuel envisioning, brainstorming, and organizational innovation.
  • Create composable, reusable, continuously learning Analytic Modules that appreciate in value the more that they are used through the use of Deep Reinforcement Learning and AI (something that we will cover in Chapter 6, The Economics of Artificial Intelligence).
  • Update the Key Performance Indicators (KPIs) and Metrics against which business progress and success will be measured. Understand any potential second-order ramifications from those KPIs including the costs associated with False Positives and False Negatives when making strategic business and operational decisions. Ensure everyone in the organization has a "clear line of sight" from their day-to-day operations and those KPIs and metrics that measure business success.

DEAN OF BIG DATA TIP:

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:

  • A False Positive is incorrectly classifying a healthy person as being infected.
  • A False Negative is incorrectly classifying an infected person as being healthy.
  • Create an analytics-enabled 3rd-party Co-creation Ecosystem that enables the organization to accelerate the capture, commercialization, and monetization of the organization's intellectual property.
  • Create Intelligent Apps, Smart Places, and Smart Things that are continuously learning and adapting based upon every customer engagement and operational interaction.
  • Create a culture that leverages Deep Reinforcement Learning and AI that empowers front-line employees so the organization never misses an opportunity to learn.

Now we want to test what we have learned about the BDBMMI with a little homework assignment.

 

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.

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

  • Identify, validate, value, and prioritize the use cases that comprise the "unplanned operational downtime" business initiative including use cases dependencies. Supporting use cases could include demand forecasting, maintenance scheduling, inventory availability, consumables management, appropriate staffing, tools and equipment availability, and events impact.
  • Gather potentially interesting data sources that support your top priority use cases into the data lake at the lowest level of granularity. Data sources could include sensor readings, maintenance notes, engineering specs, technician certifications and experience, consumer comments, inventory and consumables, local events, weather, traffic, economic variables, and so on.
  • Explore a wide range of illustrative analytics using a sub-set of operational data to identify and validate best analytics approaches.
  • Use statistics and predictive analytics to measure cause-and-effect and codify patterns, trends, relationships, and associations buried within and across the datasets.
  • Start exploring simple analytic models to predict product, device, and human behaviors that are indicative of potential operational problems.

From Business Insights to Business Optimization

  • Continue to test, learn, and refine your predictive models until you reach the required model accuracy level and "Goodness of Model Fit" as defined by the costs of False Positives and False Negatives.
  • Repeat until model accuracy and goodness of fit meet the use case requirements. Note: you can avoid overfitting of your models by ensuring that you have defined a robust set of metrics against which to measure model performance.
  • Create prescriptive and preventative analytics that deliver maintenance, inventory, staffing, and customer recommendations that prevent operational downtime problems.
  • Capture customer, product, and operational insights (propensities) within the Asset Models (Digital Twins for devices, Analytic Profiles for humans) that reside in the Data Lake.
  • Operationalize the prescriptive and preventative analytics by coming full circle to integrate the analytic outputs and results into the operational systems.
  • Instrument the analytic results to capture and measure the effectiveness of the prescriptive recommendations delivered by the analytic models.

From Business Optimization to Insights Monetization

  • Enhance and enrich Asset Models (Digital Twins for devices, Analytic Profiles for technicians, engineers, and customers) with propensity scores, and strength and direction of relationships and associations (using graph analytics).
  • Leverage Asset Models to mine, discover, aggregate, and validate unmet or underserved customer, product, operational, or market needs.
  • Operationalize unmet or underserved customer, product, operational, or market needs to create new monetization opportunities via new products, services, markets, channels, audiences, partners, and consumption models.

From Insights Monetization to Digital Transformation

  • Leverage Digital Assets (data and customer, product, and operational insights) to reinvent the organization's business models to continuously capture and exploit new sources of customer and market value creation.
  • Create composable, reusable, continuously learning Analytic Modules that appreciate in value the more that they are used through the use of Deep Reinforcement Learning and AI (something that we will cover in Chapter 6, The Economics of Artificial Intelligence).
  • Change KPIs and Metrics against which the organization measures business and operational progress and success.
  • Change hiring, management, and promotion models to create a Culture of Collaboration and Innovative Exploration.
  • Change the Compensation system to create a culture that rewards sharing and collaboration that proactively seeks to identify and eliminate business, operational, and organizational silos.
  • Create a culture that leverages Deep Reinforcement Learning and AI that empowers front-line employees so the organization never misses an opportunity to learn.
  • Apply Artificial Intelligence to create a continuously learning and adapting (autonomous) business model 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.

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

 

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 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.

 

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?

About the Author

  • Bill Schmarzo

    Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses “Big Data MBA” and “Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.

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