The Economics of Data, Analytics, and Digital Transformation

The theorems, laws, and empowerments to guide your organization's digital transformation

Bill Schmarzo

• Publisher: Packt Publishing
• Year: 2020
• ISBN:
  9781800569133
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning

Key Features

• Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset
• Acquire implementable knowledge on digital transformation through 8 practical laws
• Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly

Book Description

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."

What you will learn

• Train your organization to transition from being data-driven to being value-driven
• Navigate and master the big data business model maturity index
• Learn a methodology for determining the economic value of your data and analytics
• Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used
• Become aware of digital transformation misconceptions and pitfalls
• Create empowered and dynamic teams that fuel your organization's digital transformation

Who this book is for

This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.

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4

University of San Francisco Economic Value of Data Research Paper

DEAN OF BIG DATA TIP:

Note: This chapter is a republishing of the original ground-breaking research paper titled "Applying Economic Concepts to Big Data to Determine the Financial Value of the Organization's Data and Analytics and Understanding the Ramifications on the Organizations' Financial Statements and IT Operations and Business Strategies" that Professor Mouwafac Sidaoui and I did while at the University of San Francisco School of Management.

I am including the paper in its original version (except for some minor changes) because the research process and subsequent discoveries provided the catalyst for writing this book.

This paper truly caused me to change my frame with respect to how I thought about determining the value of data, and ultimately, the value of analytics.

I hope you enjoy reading—and studying—it as much as I enjoyed researching and writing it!

APPLYING ECONOMIC CONCEPTS TO BIG DATA TO DETERMINE THE FINANCIAL VALUE OF THE ORGANIZATION'S DATA AND ANALYTICS, AND UNDERSTANDING THE RAMIFICATIONS ON THE ORGANIZATION'S FINANCIAL STATEMENTS AND IT OPERATIONS AND BUSINESS STRATEGIES

Abstract

Companies are contemplating the organizational and business challenges of accounting for data as a "corporate asset." Data is now seen as a currency. This research paper deep dives into the economics of data and analytics and defines these analogies.

Mr. Bill Schmarzo,

Executive Fellow, University of San Francisco School of Management

Dr. Mouwafac Sidaoui,

Associate Professor and Chair Department of Business Analytics and Information Systems

The volume, variety, and velocity of data may have changed over the past few years, but one thing hasn't changed—the value of the data to improve operational decision-making and power business strategies. This research paper will explore the following questions with respect to how organizations maximize the economic and financial value of the organization's data and analytics:

• How does an organization identify and prioritize the business use cases upon which to focus its data and analytics initiatives?
• How does an organization determine the economic value of the data that supports the organization's business use cases?
• How does an organization create a framework that facilitates the capture and reuse of the organization's data and analytic assets?
• What is the role of the data lake, data governance, data quality, and other data management disciplines in managing, protecting and enhancing the organization's data and analytic assets?

Introduction

The importance of data has changed over the years. As the volume, variety, and velocity of the data grew over the past few years, the economic value of data has been transformed by the big data phenomenon^{[citation 1]} that has enabled organizations to capture a broader, more granular, and more real-time range of customer, product, operational, and market interactions. Today, business leaders see data as a monetization opportunity, and their organizations are embracing data and analytics as the intellectual capital of the modern organization.

More and more companies are also contemplating the organizational and business challenges of accounting for data as a "corporate asset."

Data as an asset exhibits unusual characteristics when compared to other balance sheet assets. Most assets depreciate with usage. However, data appreciates or gains more value with usage; that is, the more the organization uses the data across more use cases, the more valuable, complete, and accurate the data becomes.

However, there are severe limitations in valuing data in the traditional balance sheet framework. It is important that firms identify a way to account for their data. To address this challenge, this research paper will put forth the following:

1. A framework to facilitate the capture, refinement and sharing of the organization's data and analytic assets, and
2. A process to help organizations prioritize where to invest their precious data and analytic resources.

It is our hope that this research paper will foster new ways for organizations to rethink how they value their data and analytics from an economic and financial perspective. The concepts covered in this research paper will provide a common vocabulary and approach that enables business leaders to collaborate with the IT and Data Science teams on identifying and prioritizing the organization's investments in data and analytics; to create a common collaborative value creation platform.

Creating the Collaborative Value Creation Framework

Data and analytics are powerful assets in which to inve...