Defining Enterprise Data and Analytics Strategy

ISBN-13: 9789811957215 / Angielski / Miękka / 2023

Prakash Sah

Opis

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.

Spis treści

Chapter 1: What is Data and Analytics Strategy

Key elements that should be part of the strategy

Data and Analytics Strategy and its Criticality to Drive Enterprise Digital Initiatives

Data and Analytics Strategy: A Case in Point

Five Elements of Data and Analytics Strategy

▪ Business capabilities

▪ Technology and architecture

▪ Team, processes, and governance

▪ Organizational change management

▪ Value measurement framework

Summary

Chapter 2: First Element of Strategy – Business Capabilities

Taking a top-down approach to align with business strategy

Aligning with Organization’s Business Priorities

Establishing Enterprise Performance Management Framework

▪ A brief historical perspective on performance measurement

▪ Key performance indicators (KPIs): lagging and leading indicators

▪ KPI trees to drive enterprise performance management

▪ Challenges of implementing enterprise KPI framework

▪ KPI framework defined for scenario 1 (organization A) discussed at the beginning of this chapter

Driving Enterprise Digital Strategy

▪ Approach for scenario 2 (organization B): Digital transformation leveraging data and analytics

Approach for Defining Data and Analytics Strategy, Starting with Business Capabilities

▪ Step 1: Enterprise churning – “Samudra Manthan”

▪ Step 2: Defining required business capabilities and other strategy elements

▪ Step 3: Prioritizing and creating an integrated roadmap

Summary

Chapter 3: Second Element of Strategy – Technology and Architecture

Establishing technology and architecture foundation that is futuristic and flexible

How Not to Define Technology and Architecture Strategy?

Understanding Non-Functional Requirements to Define Data and Analytics Architecture

▪ Data sources

▪ Mode of delivery/access (of data)

▪ Temporal

▪ Data security

▪ Data type

▪ Data atomicity

▪ Latency

▪ Data quality and integrity

▪ Business model

▪ Data usage

▪ Metadata

Defining Data and Analytics Architecture

Selecting Relevant Technologies after Defining Data and Analytics Architecture

Summary

Chapter 4: Third Element of Strategy – Team, Processes, and Governance

Establishing building blocks, including an agile team, for success

Why Data and Analytics Organization and Processes Need to be Different from Other IT Functions?

Choosing the Right Data and Analytics Organization Model

▪ Decentralized organization

▪ Centralized organization

▪ Federated organization

Defining Data and Analytics Organization and Processes

▪ Governance tower

▪ Business tower

▪ Technology and architecture tower

▪ Solution delivery tower

▪ Service delivery tower

Week-in-the-Life of Data and Analytics Team

Summary

Chapter 5: Fourth Element of Strategy – Organizational Change Management

Driving and managing change across the enterprise to ensure success

Need for Change Across the Enterprise

▪ Till 2010 – A brief history of MIS era

▪ The 2010s – Data visualization becomes all-pervasive across enterprises

▪ The latter half of 2010s - Advent of digital technologies

▪ Why organizational change management

Driving Change - Focus Areas & Objectives

▪ Four Focus Areas – People, Processes, Technology, and Data

▪ Three OCM Objectives

▪ Twelve OCM Strategy Elements for Data and Analytics

Stages of Change & Change Leadership

▪ The Three Stages of Change

▪ Importance of Change Leadership

Summary

Chapter 6: Fifth Element of Strategy – Value Measurement Framework

Establishing a framework to define and measure value of data and analytics program

The Need for a Value Measurement Framework

Defining and Measuring Business Value

▪ First impact area: Revenue increase

▪ Second impact area: Cost reduction

▪ Third impact area: Business risk mitigation

▪ Fourth impact area: Company’s image building

▪ Business value measurement: Correlation does not necessarily mean causality

Defining and Measuring Operational Efficiency – Continuous Improvement

▪ People performance

▪ Process effectiveness

▪ Technology capability

▪ Data maturity

▪ Operational efficiency and maturity assessment

Calculating ROI from Data and Analytics Investment

▪ Calculating benefits

▪ Calculating costs

▪ Calculating ROI

Summary

Chapter 7: The Profile of a Data and Analytics Leader

Key skills of a leader who can lead the enterprise to success

Key Skills that any Enterprise Data and Analytics Leader Must Possess

Hard Skills

▪ Technology

▪ Data science

▪ Business

Soft Skills

▪ Dealing with ambiguity

▪ Team leadership

▪ Innovation and risk-taking

▪ Organizational change management

▪ Design thinking and empathy

▪ Marketing

Summary

O autorze

Prakash Sah has three decades of multi-faceted consulting and leadership experience in data and analytics. His experience spans across multiple industries and cultures. While working in 13 different countries, he helped CXOs and other leaders of many Fortune 500 companies in defining their enterprise data and analytics vision, strategy, and roadmap.

Prakash has been invited as speaker/panelist at various international conferences and innovation events. He has also been delivering guest lectures at few B Schools. He has been mentoring many students and professionals in academia and industry respectively. He has authored various papers and articles on enterprise data and analytics.

Prakash is a mechanical engineer from IIT Kharagpur and an MBA from IIM Calcutta – two premier institutes in India. He is currently working as Managing Partner at TCS (Tata Consultancy Services), one of the world’s leading consulting and IT services MNC. Prakash is based out of Thane in Mumbai metropolitan region, India, where he stays with his wife and two daughters.

Linked-in profile: https://www.linkedin.com/in/prakash-sah-9581964/

Blurb

This book describes key elements of enterprise data and analytics strategy and prescribes a pragmatic approach to define strategy for large enterprises. It 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 inherent complexities of such initiatives. The book discusses key challenges that enterprises struggle with, such as—defining enterprise data and analytics strategy, and key elements that should be considered while doing so; limitations of one-size-fits-all approach which does not work for all enterprises; aligning data and analytics initiative with business strategy of the CEO; establishing a futuristic technology and architecture foundation, given the exponential rate of innovation in data and analytics technologies; defining the right data and analytics organization model and structure; reasons why data and analytics organization and processes need to be different from other functions; managing organizational change to ensure success of data and analytics initiative; defining a business value measurement framework and calculating ROI from data and analytics initiative; and key skills required in a data and analytics leader to wade through political and other challenges of a large enterprise.

Often, data and analytics leaders define a strategy that is focused primarily on technology and architecture. This leads to failure of a majority of data and analytics initiatives across enterprises. The book recommends defining a holistic strategy through five key elements – (a) business capabilities, (b) technology and architecture, (c) team, processes, and governance, (d) organizational change management, and (e) value measurement framework.

The book helps executives, chief digital/analytics officers, data and analytics professionals, consultants, and students in addressing various challenges and dilemmas that they face every day to make their enterprises more data driven