The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

I am offering this cheat sheet as a quick way to summarize the key takeaways from each chapter of the book. While these brief points won't replace the need to read the book, they might help in providing a quick reference and pointer to where the takeaways were covered in the book. Enjoy!

Chapter 1: The CEO Mandate: Become Value-driven, Not Data-driven

• "Data is the new oil" in that the way that oil fueled the economic growth of the 20^th century, data will be the catalyst for the economic growth in the 21^st century.
• If "data is the new oil," then how effective is your organization at leveraging data and analytics to power your business model?
• Data may be the new oil, but it is the customer, product, and operational analytic insights buried in the data that will determine the winners and losers in the 21^st century.
• Crossing the "Analytics Chasm" requires organizations to leverage the economics of data and analytics on a use case-by-use case basis.
• Data science is about identifying variables and metrics that might be better predictors of performance.

Chapter 2: Value Engineering: The Secret Sauce for Data Science Success

• Value Engineering starts with what's important ($$) to the organization.
• While the decisions have not changed over the years, what has changed—courtesy of advanced analytics—are the answers.
• Organizations don't fail due to a lack of use cases; they fail because they have too many.
• Most Digital Transformation journeys don't fail because of technology issues; they get thwarted by passive-aggressive behaviors.
• A diverse set of stakeholders is critical because they provide different perspectives on variables and metrics against which data science progress and success will be measured.
• The heart of the Data Science Value Engineering Framework is the collaboration with the different stakeholders to identify, validate, value, and prioritize the key decisions (use cases).

Chapter 3: A Review of Basic Economic Concepts

• Economics is a branch of knowledge concerned with the production, consumption, and transfer of wealth; economics is about the creation of value.
• The Economic Value Curve is a measure of the relationship between a dependent variable and independent variables to achieve a particular business or operational outcome.
• Analytic Modules are composable, reusable, continuously learning analytic assets that deliver predefined business or operational outcomes.
• The Law of Diminishing Returns is a measure of the decrease in the marginal output of production as the amount of a single factor of production (input) is incrementally increased.
• The Economic Multiplier Effect refers to the increase in final income arising from any new injection of spending.
• Marginal Propensity to Consume (MPC) measures the impact of a change in output (production) as a ratio to the change in input (investment).
• Utility refers to...

Chapter 4: University of San Francisco Economic Value of Data Research Paper

• Accounting is a "Value in Exchange" asset valuation methodology; that is, the value of an asset is determined by what someone is willing to pay for that asset.
• Economics is a "Value in Use" asset valuation methodology; that is, the value of an asset is determined by the value generated using that asset.
• A data lake can be transformed into a "collaborative value creation" platform by facilitating the capture, refinement, and reuse of the organization's data and analytic assets across multiple use cases.

Chapter 5: The Economic Value of Data Theorems

• The Data Economic Multiplier Effect: Data never wears out, never depletes, and can be used across an unlimited number of use cases at near-zero marginal cost.
• Theorem #1: It isn't the data itself that's valuable; it's the trends, patterns, and relationships gleaned from the data about your customers, products, and operations that are valuable.
• Theorem #2: It is from the quantification of the trends, patterns, and relationships that drive predictions about what is likely to happen.
• Theorem #3: Predictions drive monetization opportunities through improved business and operational use cases.
• Theorem #4: The ability to reuse the same datasets across multiple use cases is the real economic game-changer.
• Theorem #5: Trying to optimize across a diverse set of objectives can yield more granular, higher fidelity outcomes that enable "doing more with less".

Chapter 6: The Economics of Artificial Intelligence

• Using Artificial Intelligence, you can create assets that appreciate in value, not depreciate, the more that these assets are used.
• "Orphaned Analytics" are one-off analytics developed to address a specific business need but are never "operationalized" or packaged for reuse across the organization.
• Deep Learning is a set of algorithms that analyze massive datasets using a multi-layered neural network structure, where each layer comprises numerous nodes, to train and learn to recognize and codify patterns, trends, and relationships buried in the data…without human intervention.
• Reinforcement Learning is a class of machine learning algorithm that seeks to "learn" by taking actions within a controlled environment with the goal to maximize rewards while minimizing costs.
• Transfer Learning is a technique whereby one neural network is first trained on one type of problem...

Chapter 7: The Schmarzo Economic Digital Asset Valuation Theorem

• "Economies of Learning" are more powerful than "Economies of Scale".
• A use case-by-use case deployment approach exploits the "Economies of Learning" through the rapid learning and reapplication of those learnings to future use cases.
• The Schmarzo Economic Digital Asset Valuation Theorem leverages the unique economic characteristics of data and analytics to increase organizational value via three "effects".
• Digital Economics Effect #1: Since data never depletes, never wears out, and can be reused against an unlimited number of use cases at near-zero marginal cost, reusing "curated" data and analytic modules reduce the marginal costs of new use cases.
• Data silos are the killers of the economic value of data.
• Digital Economics Effect #2: Sharing and reusing the data and analytic modules accelerate use case time-to-value and de-risks...

Chapter 8: The 8 Laws of Digital Transformation

• Digital Transformation Laws are statements based on repeated observations that describe or predict a range of natural phenomena.
• Digital Transformation Law #1: Digital Transformation is about reinventing and innovating business models, not just optimizing existing business processes.
• Digital Transformation Law #2: Digital Transformation is about reinventing your customer engagements and business operations with continuously learning AI capabilities to derive and drive new sources of customer, product, service, and operational value.
• Digital Transformation Law #3: Digital Transformation is about reinventing your business model to expand upon, exploit, and monetize those sources of customer value creation while eliminating the inhibitors of value creation.
• Digital Transformation Law #4: Digital Transformation is about creating new digital assets that leverage customer, product, and operational insights to...

Chapter 9: Creating a Culture of Innovation Through Empowerment

• Ambiguity—the quality of being open to more than one interpretation—is the key to human, society and organizational evolution.
• Empowering your teams at the front line of customer engagement and operational execution will have more impact on the organization's digital transformation success than the strategizing and pontificating of senior management.
• Empowerment #1: Gaining buy-in to the organization's Mission Statement requires everyone to internalize what that mission statement means to them, their jobs, and their personal principles.
• Empowerment #2: Speaking the Language of the Customer ensures that everyone not only has the same customer-centricity focus but is speaking the same language that the customer uses.
• Empowerment #3: Organizational Improvisation yields flexible and malleable teams that can maintain operational integrity while morphing the team's...