This is the first of its kind book that describes key elements of enterprise data and analytics strategy, and prescribes a pragmatic approach to define the strategy for large enterprises. The book is based on successful digital transformation experience of multiple Fortune 500 and other large enterprises. It is estimated that more than 50% of data and analytics initiatives fail globally because of the inherent complexity of such initiatives. Some of the questions that enterprises struggle with are: How to define enterprise data and analytics strategy? What are the key elements that should be considered while doing so? Why one-size-fits-all approach does not work for all enterprises? How to align data and analytics initiative with the business strategy of the CEO? How to establish a futuristic technology and architecture foundation, given the exponential rate of innovation in data and analytics technologies? How to define the right data and analytics organization model? Why data and analytics organization and processes need to be different from other functions? How to manage organizational change to ensure success of data and analytics initiative? How to define a business value measurement framework and calculate ROI from data and analytics initiative? What are the key skills required in a data and analytics leader to wade through political and other challenges of a large enterprise? This book will help executives, chief digital/analytics officers, data and analytics professionals, and consultants, in answering the above questions. It will help them in addressing various dilemmas that they face every day and making their enterprises data-driven.
Author(s): Prakash Sah
Series: Management for Professionals
Publisher: Springer
Year: 2022
Language: English
Pages: 185
City: Singapore
Preface
Why Did I Write This Book?
How Did I Write This Book?
Acknowledgements
Contents
About the Author
1 What Is Data and Analytics Strategy
1.1 Data and Analytics Strategy and Its Criticality to Drive Enterprise Digital Initiatives
1.2 Data and Analytics Strategy: A Case in Point
1.3 Five Elements of Data and Analytics Strategy
1.3.1 Business Capabilities
1.3.2 Technology and Architecture
1.3.3 Team, Processes, and Governance
1.3.4 Organizational Change Management
1.3.5 Value Measurement Framework
1.4 Summary
2 First Element of Strategy—Business Capabilities
2.1 Aligning with Organization’s Business Priorities
2.2 Establishing Enterprise Performance Management Framework
2.2.1 A Brief Historical Perspective on Performance Measurement
2.2.2 Key Performance Indicators (KPIs): Lagging and Leading Indicators
2.2.3 KPI Trees to Drive Enterprise Performance Management
2.2.4 Challenges of Implementing Enterprise KPI Framework
2.2.5 KPI Framework Defined for Scenario 1 (Organization A)
2.3 Driving Enterprise Digital Strategy
2.3.1 Approach for Scenario 2 (Organization B): Digital Transformation Leveraging Data and Analytics
2.4 Approach for Defining Data and Analytics Strategy, Starting with Business Capabilities
2.4.1 Step 1: Enterprise Churning—“Samudra Manthan”
2.4.2 Step 2: Defining Required Business Capabilities and Other Strategy Elements
2.4.3 Step 3: Prioritizing and Creating an Integrated Roadmap
2.5 Summary
3 Second Element of Strategy—Technology and Architecture
3.1 How Not to Define Technology and Architecture Strategy?
3.2 Understanding Non-functional Requirements to Define Data and Analytics Architecture
3.2.1 Data Sources
3.2.2 Mode of Delivery/Access (of Data)
3.2.3 Temporal
3.2.4 Data Security
3.2.5 Data Type
3.2.6 Data Atomicity
3.2.7 Latency
3.2.8 Data Quality and Integrity
3.2.9 Business Model
3.2.10 Data Usage
3.2.11 Metadata
3.3 Defining Data and Analytics Architecture
3.4 Selecting Relevant Technologies After Defining Data and Analytics Architecture
3.5 Summary
4 Third Element of Strategy—Team, Processes, and Governance
4.1 Why Data and Analytics Organization and Processes Need to Be Different from Other IT Functions?
4.2 Choosing the Right Data and Analytics Organization Model
4.2.1 Decentralized Organization
4.2.2 Centralized Organization
4.2.3 Federated Organization
4.3 Defining Data and Analytics Organization and Processes
4.3.1 Governance Tower
4.3.2 Business Tower
4.3.3 Technology and Architecture Tower
4.3.4 Solution Delivery Tower
4.3.5 Service Delivery Tower
4.4 Week-In-The-Life of Data and Analytics Team
4.5 Summary
5 Fourth Element of Strategy—Organizational Change Management
5.1 Need for Change Across the Enterprise
5.1.1 Till 2010—A Brief History of MIS Era
5.1.2 The 2010s—Data Visualization Becomes All-Pervasive Across Enterprises
5.1.3 The Latter Half of 2010s—Advent of Digital Technologies
5.1.4 Why Organizational Change Management
5.2 Driving Change—Key Focus Areas and Objectives
5.2.1 Changing Business Environment
5.2.2 Four Key Focus Areas
5.2.3 Organizational Chaos Theory and Three Key Objectives of OCM Strategy
5.2.4 Inter-relationships Between the Focus Areas and Key Objectives
5.3 Driving Change—Twelve Elements of OCM Strategy
5.3.1 1A: User Persona Focus
5.3.2 1B: Collaboration and Motivation
5.3.3 1C: Communication
5.3.4 2A: New Ways of Working
5.3.5 2B: Innovation Process
5.3.6 2C: Interaction Model with Different Functions
5.3.7 3A: Training on New Technologies
5.3.8 3B: Exploration of Fit-for-Future Technologies
5.3.9 3C: Institutionalization of New Technologies
5.3.10 4A: Data Literacy
5.3.11 4B: Data Thinking
5.3.12 4C: Data Democratization
5.4 Stages of Change and Importance of Change Leadership
5.4.1 Stage 1: Prepare and Initiate
5.4.2 Stage 2: Scale-Up
5.4.3 Stage 3: Institutionalize
5.4.4 Importance of Change Leadership
5.5 Summary
6 Fifth Element of Strategy—Value Measurement Framework
6.1 The Need for a Value Measurement Framework
6.1.1 Data and Analytics Efficiency-Value Matrix (EV Matrix)
6.2 Defining and Measuring Business Value
6.2.1 First Impact Area: Revenue Increase
6.2.2 Second Impact Area: Cost Reduction
6.2.3 Third Impact Area: Business Risk Mitigation
6.2.4 Fourth Impact Area: Company’s Image Building
6.2.5 Business Value Measurement: Correlation Does Not Necessarily Mean Causality
6.3 Defining and Measuring Operational Efficiency—Continuous Improvement
6.3.1 People Performance
6.3.2 Process Effectiveness
6.3.3 Technology Capability
6.3.4 Data Maturity
6.3.5 Operational Efficiency and Maturity Assessment
6.4 Calculating ROI from Data and Analytics Investment
6.4.1 Calculating Benefits
6.4.2 Calculating Costs
6.4.3 Calculating ROI
6.5 Summary
7 The Profile of a Data and Analytics Leader
7.1 Key Skills That Any Enterprise Data and Analytics Leader Must Possess
7.2 Hard Skills
7.2.1 Technology
7.2.2 Data Science
7.2.3 Business
7.3 Soft Skills
7.3.1 Dealing with Ambiguity
7.3.2 Team Leadership
7.3.3 Innovation and Risk Taking
7.3.4 Organizational Change Management
7.3.5 Design Thinking and Empathy
7.3.6 Marketing
7.4 Summary
