Big Data for Big Decisions

Length: 266 pages
Edition: 1
Language: English
Publisher: Auerbach Publications
Publication Date: 2022-12-30
ISBN-10: 1032017244
ISBN-13: 9781032017242
Sales Rank: #3895706 (See Top 100 Books)

Building a data-driven organization (DOD) is an enterprise-wide initiative that may consume and lock-up resources for a long term. Understandably, managers in organizations considering such an initiative would insist on a roadmap and a business-case to be prepared and evaluated prior to approval. Executive management would expect any such DDO initiative to have a clearly defined scope, objectives, and measures of success including a quantified monetary return from data-driven decisions. To meet this need, Big Data for Big Decisions: Building a Data-Driven Organization covers this white-space. It presents a step-by-step methodology to create a roadmap and business case and provides a narration of the constraints and experiences of managers who have attempted setting-up a data-driven organization.

Many CIOs are struggling to explain to senior management the value an organization can gain from analytics. Given the hype in the market for Big Data, analytics, and AI, every organization has a budget for analytics, but very few make any headway. Most companies end-up investing into a visualization platform, which basically is an improved version of their business intelligence (BI) dashboard. Industry analysts have estimated that more than 75% of all analytics projects fail to deliver any value. This book provides a method to ensure materially improved decision-making and demonstrable value from investments in analytics.

The book emphasizes Big Decisions, which are the 10% of organizational decisions that influence 90% of business outcomes and are the key-decisions that seriously affect the profitability and growth-potential. Qualitative improvement of such key-decisions made on the basis of actionable insights have the potential to determine an organization’s competitive advantage in the market. Also covered are:

Decision prioritization The concept of knowns and unknowns Johari window for the organization as a person Decision prioritization Every enterprise aspires to become 100% data driven. This book provides guidance for such endeavors and encouragement for organizational managers as they make the data journey.

Cover Page
Half Title page
Title Page
Copyright Page
Dedication
Contents
Acknowledgments
Author
Introduction
    I.1 Inception
    I.2 Data-Driven Organization: The Stakeholders’ Expectations
        I.2.1 Stakeholders' Expectations
        I.2.2 The Other Stakeholders’ Dilemma
    I.3 Setting Up a Data-Driven Organization; Constraints and Experiences
    I.4 What This Book Covers
Chapter 1 Quo Vadis: Before the Transformational Journey
    1.1 Data-Driven Organization: Refining the Meaning and the Purpose
        1.1.1 From Data-Driven, to Insights-Driven
    1.2 Before the Journey: Deconstructing the Data-to-Decisions Flow
        1.2.1 The Data Manifest
        1.2.2 Data Catalog and Data Dictionary
        1.2.3 Data Logistics: Information Supply and Demand
    1.3 Data-Driven Organization: Defining the Scope, Vision, and Maturity Models
        1.3.1 Maturity Models
        1.3.2 What is Missing?
    Bibliography
Chapter 2 Decision-Driven before Data-Driven
    2.1 The Three Good Decisions
    2.2 Decision-Driven before Data-Driven
    2.3 The “Big” Decisions Need to Be Process-Driven
        2.3.1 Decision Modeling and Limitations
    2.4 Conclusion
    Bibliography
Chapter 3 Knowns, Unknowns, and the Elusive Value From Analytics
    3.1 The Unknown-Unknowns
    3.2 Decisions That You Are Making and the Data That You Need
    3.3 A Johari Window For an Organization
        3.3.1 Customers’ Perspective
        3.3.2 Employees’ Perspective
    3.4 In Search of Value From Analytics
        3.4.1 In Theory
        3.4.2 In Reality
    Bibliography
Chapter 4 Toward a Data-Driven Organization: A Roadmap For Analytics
    4.1 The Challenge of Making Analytics Work
        4.1.1 Investing in Analytics: The Fear of Being Left Behind
    4.2 Decision-Oriented Analytics: From Decisions to Data
    4.3 The Importance of Beginning From the End
    4.4 Deciphering the Data behind the Decisions
    4.5 Meet the Ad Hoc Manager!
    4.6 Local vs. Global Solutions
    4.7 Problem vs. Opportunity Mindset
    4.8 A Roadmap for Data-Driven Organization
    4.9 Summary
    Bibliography
Chapter 5 Identifying the “Big” Decisions
    5.1 Taking Stock: Existing Analytics Assets
        5.1.1 Project Trigger
        5.1.2 Business Value Targeted
        5.1.3 Ad Hoc-ism
    5.2 The Lost Art of Decision-Making
    5.3 Prioritizing Decisions: In Search of an Objective Methodology
    5.4 Learning from the Bain Model
    5.5 Decision Analysis
    5.6 Decision Prioritization: Factors to Consider
    5.7 Decision Prioritization: Creating a Process Framework
        5.7.1 Cross-Dimensional Comparison
        5.7.2 The Process Framework: Identifying and Prioritizing the “Big” Decisions
    Bibliography
Chapter 6 Decisions to Data: Building a “Big” Decision Roadmap and Business Case
    6.1 Toward a Data-Driven Organization: Building a “Big” Decision Roadmap
        6.1.1 Identifying and Prioritizing the Decisions
        6.1.2 Roadmap for a Data-Driven Organization
    6.2 The Data behind the Decisions
        6.2.1 Decision Modeling and Analysis
        6.2.2 Deciphering the Data behind the Decision
    6.3 Building a Business Case
        6.3.1 Analytics and the Sources of Value: The Value-Drivers
        6.3.2 Estimating Returns: Comparing KPIs with Industry Benchmarks
        6.3.3 Estimating the Investments
    6.4 From Decisions to Data: A Summary View
    6.5 The Data, Trust, and the Decision-Maker
        6.5.1 What Else Can Potentially Go Wrong?
        6.5.2 Value Promised vs. Value Delivered
    Bibliography
Chapter 7 Unchartered: A Brief History of Data
    7.1 The History of Data
    7.2 Growth of Enterprise Data
    7.3 Enterprise Applications: Rise of ERP
    7.4 Need for “One Version of Truth”
    7.5 Evolution of Databases
    7.6 Evolution of Enterprise Data
    7.7 Y2K and the Aftermath
    7.8 Enterprise Application Integration
    7.9 Life before the Internet: Electronic Data Interchange
    7.10 Master Data Management (MDM)
    7.11 Managing the Enterprise Content: Structured & Unstructured
        7.11.1 Searching across Documents
        7.11.2 Searching within a Document: Markup Languages
        7.11.3 Structured Data vs. Unstructured Data
        7.11.4 Enterprise Content Management Systems
    7.12 The Era of the Internet: External Data
    7.13 Conclusion
Chapter 8 Building a Data-Driven IT Strategy
    8.1 Information Technology Strategy: Introduction
    8.2 Information Technology Strategy: Decoding the Problems
    8.3 Should Data Drive Your IT Strategy?
    8.4 Getting IT Right
        8.4.1 Business-Aligned Information Technology
        8.4.2 Benchmarking
        8.4.3 Organizational Workflow: Information Supply Chain
        8.4.4 Workflow and the Speed of Information Supply Chain
        8.4.5 Enterprise Value-Chain and Information Supply Chain
        8.4.6 Resource Optimization
        8.4.7 Value from IT
        8.4.8 Enterprise Architecture: Compatibility and Cohesiveness
    8.5 Data-Driven Application Portfolio Analysis and Rationalization
        8.5.1 Playing Catch-Up
    8.6 Summary: The Making of the Holy Grail!
    8.7 Does Information Technology Really Matter?
    Bibliography
Chapter 9 Building a Data Strategy
    9.1 When Data Fails to Deliver
        9.1.1 Water, Water Everywhere!
        9.1.2 Legacy Data: Data Warehouses or Data Lakes?
        9.1.3 The Data Conundrum
    9.2 Enterprise Data Strategy
        9.2.1 Defining Data Strategy
        9.2.2 Do Organizations Need a Data Strategy?
        9.2.3 Who Owns a Data Strategy?
        9.2.4 Recruiting a CDO
        9.2.5 Skill Set of a CDO
        9.2.6 Who Should Be Owning a Data Strategy?
    9.3 A Framework for Building a Data Strategy
        9.3.1 Components of a Data Strategy
        9.3.2 Before Building a Data Strategy: A Time for Organizational Introspection
    9.4 The New Dimensions of the Data
        9.4.1 How Would You Know If You Have Big Data in Your Organization That You Need to Handle Differently?
        9.4.2 Do Organizations Need a Separate Big Data Strategy?
        9.4.3 Why Most Data is Big Data Now: The Big Multiplying Effect
    9.5 Big Data for Big Decisions
        9.5.1 Big Data, AI, and the Age of the Robots…
        9.5.2 Transformational Data Strategy for Building a Data-Driven Organization
    9.6 Integrated Analytics Strategy
    Appendix 9.A: A Framework for Building a Data Strategy – Step by Step (Figure 9.7)
        A. DUE DILIGENCE and PRE-WORK: Identifying the Sources of Data in Your Organization
        B. Create a Data Inventory – (For Both Structured and Unstructured Data)
        C. Evaluate: Data Requirements and Data Adequacy Analysis
        D. Set and Consolidate Corporate Business Objectives – For an Integrated Data Strategy
        E. Build a New and Comprehensive Enterprise Data Structure
        F. IMPLEMENT and INSTITUTIONALIZE – The New Data Structure, Process, Protocols and Governance Model
        Bibliography
Chapter 10 Building a Data-Driven Marketing Strategy
    10.1 What Prevents the Companies Making Data-Driven Marketing Decisions?
    10.2 The Data that You Need vs. The Data that You Have
    10.3 Should the FSE Be Collecting Data or Acting Based On It?
    10.4 Marketing Strategy: The Anatomy of Hitherto Unresolved Problems
    10.5 Operating Blind
    10.6 And the Blind Leading the Blind
    10.7 The Importance of Location Data
    10.8 Sight to the Blind: Building a Data-Driven Marketing Function
        10.8.1 Building Geospatial Analytics for Micro-Market Data
    10.9 The Big Marketing Decisions
    Bibliography
Chapter 11 Integrated Data Governance
    11.1 The Need for Data Governance
    11.2 Need for Data Governance in Global Organizations: Addressing the Stakeholders’ Concerns
        11.2.1 What Is so Different about Global Organizations?
        11.2.2 Local vs. Global: The Need for Integrated and Centralized Data Governance
    11.3 Recognizing Poor Data Governance: The Markers
        11.3.1 Measuring Data Quality
        11.3.2 Dimensions of Data Quality
    11.4 The Cost of Poor Data Governance: Overshooting Overheads
    11.5 Transformational Roadmap for Designing and Institutionalizing Data Governance: An Overview
    11.6 Step 1: Discovery
        11.6.1 Data Catalog and Data Dictionary
        11.6.2 Data Lineage and Data Traceability
    11.7 Step 2: Value Definition
        11.7.1 Prioritizing Data for Governance
        11.7.2 Creating a Business Case for Data Governance
    11.8 Step 3: Plan and Build
        11.8.1 Components of Data Governance
        11.8.2 Designing a New Enterprise Data Governance Framework
    11.9 Step 4: Grow and Consolidate – Institutionalizing Data Governance
        11.9.1 Pilot and Roll Outs
        11.9.2 Institutionalizing Data Governance
    11.10 Data Governance for Big Data: Emerging Trends
        11.10.1 The Growing Importance of Data Governance for the AI Economy
        11.10.2 Data Lakehouse
    11.11 The Evolving Role of a CDO
    Bibliography
Index