Big Data for Big Decisions: Building a Data-Driven Organization

Description

Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions – the key decisions that influence 90% of business outcomes – starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners' handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization. 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 • 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 • 1.2.3.1 DDO’s and the Theory of Asymmetric Information • 1.3 Data-Driven Organization: Defining the Scope, Vision, and Maturity Models • 1.3.1 Maturity Models • 1.3.2 What is Missing? • Bibliography • 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 • 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 • 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 • 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 • 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 • 6.1.1.1 Step 1: Create a Master List of the Decisions of the Company • 6.1.1.2 Step 2: Identifying the “Big” Decisions • 6.1.1.3 Step 3: Prioritizing the Decisions for Analytics Investments: Need for Cross-Dimensional Analysis • 6.1.2 Roadmap for a Data-Driven Organization • 6.1.2.1 Constituting the Focus Groups • 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 • 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 8 Building a Data-Driven IT Strategy • 8.1 An 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 • 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 ) • Bibliography • 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 • Note Bibliography • 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