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Data for Business Performance: The Goal-Question-Metric (GQM) Model to Transform Business Data into an Enterprise Asset

Book Description

Today, digitization is dramatically changing the business landscape, and many progressive organizations have started to treat data as a valuable business asset. While many enterprises are investing in improved data management, only a few have leveraged data to truly impact business performance. To address this problem, Data for Business Performance provides readers with practical guidance and proven techniques to derive value from data in today's business environment. Specifically, this book:
  1. is holistic, as it looks at deriving value for all three key purposes of data: decision making, compliance, and customer service.
  2. is for practitioners, with practical guidance and proven techniques supported by real world examples.
  3. is relevant for the current business and IT landscape.
  4. is novel, with the adoption of the Goal-Question-Metric (GQM) framework as the core mechanism to monetize data in the organization, based on business goals, key questions, and key performance indicators (KPIs).
  5. is technology-agnostic, as concepts are used for unlocking the value of data without any reference to proprietary technologies.
This book is absolutely timely and relevant in today's data-driven world. Most of the books on data available in the market today focus on data quality, governance, and analytics. This book from Dr. Prashanth Southekal is brilliant as it puts the business stakeholder at the center by addressing the key value propositions of the business user. This book is holistic and I strongly believe it will help to bridge the gaps we have today.
Mario Faria Managing Vice President, Gartner, US

Packed with insights and leveraging a process oriented approach, this book covers a unique combination of the science, the art and the strategy of unlocking the potential of data for enterprises in a real-life context. The author has managed to provide a clear action plan for creating data analytics and its management a key function in a modern enterprise.

Ashish Sonal (Vir Chakra)
CEO, Orkash, India

This book is one of the most practical sources for how companies can greatly improve their bottom line by improved data management and becoming a data-centric company. It combines leading data management theory with step-by-step implementation and real-life examples, and is a must-read for those wanting to derive more value from their corporate data.
Lance Calleberg
Application Architect, Husky Energy, Canada

Prashanth has given a very practical guide to implement data culture in an organization. The book Data for Business Performance talks about building the organization of the future and the role of data. Prashanth rightly believes and demonstrates that data is not an asset of the IT team and is an organization-wide asset. He proposes the need for the chief data officer (CDO) as a role that should anchor data and report to the CEO, and manage the stakeholders' data needs.
Harshajith Umapathy
Senior Vice President, Hansa Cequity, India

Dr. Southekal provides valuable insights on data and information management in mostly short and clearly written sections. Anyone interested in the data-driven company should read this book and learn about the hurdles on the road to be data-driven, and his valuable suggestions on how to overcome them. His wisdom may prevent some of the failures that helped him learn.
Erik van der Voorden
Domain Architect, Independent Consultant, Netherlands

Table of Contents

  1. Foreword
  2. Introduction
    1. About the Book
    2. Organization of the Book
    3. Who Should Read this Book?
    4. Stay in Touch
  3. Part I- Define
  4. Chapter 1- Establishing the Terminology
    1. Understanding the GQM Model
    2. Understanding Data
    3. Understanding the Enterprise
    4. Associating Enterprise and Data
    5. Understanding the Asset
    6. Understanding Transformation
    7. Data and Business Value
      1. Decision Making
      2. Compliance
      3. Customer Service
    8. What has Changed?
    9. Conclusion
  5. Chapter 2- Demystifying Business Data
    1. Business Data in the Enterprise Value Chain
      1. Primary activities
      2. Support activities
    2. Business Data versus Non-business Data
    3. Types of Enterprise Business Data
      1. Reference data
      2. Master data
      3. Transactional data
      4. Metadata
    4. Key Characteristics of Business Data
    5. One Data, Many Views, Many Classes
      1. Purpose
      2. Origination
      3. Processing
      4. Analysis
      5. Time horizon
      6. Sensitivity
      7. Ownership
      8. Treatment
      9. Lifecycle
      10. Security
    6. Conclusion
  6. Chapter 3- Discerning Data in Systems
    1. Business Data Lifecycle
      1. Origination
      2. Capture
      3. Validation
      4. Processing
      5. Distribution
      6. Aggregation
      7. Interpretation
      8. Consumption
    2. IT Functions
      1. Storage
      2. Security
    3. IT Systems
    4. Databases
      1. Relational or SQL databases
      2. Non-relational or NoSQL databases
    5. SQL or NoSQL?
    6. OLTP Systems
    7. Integration Systems
      1. EAI (Enterprise Application Integration)
      2. ETL (Extraction, Transformation, and Loading)
    8. OLAP Systems (Business Intelligence Systems)
    9. Analytics Systems
    10. Relating OLAP and OLTP Systems
    11. Managing the Systems
      1. SoR (Systems of Record)
      2. SoD (Systems of Differentiation)
      3. SoI (Systems of Innovation)
    12. The Big Picture
    13. System Architecture — The Data Model
    14. Conclusion
  7. Part II- Analyze
  8. Chapter 4- Knowing the Limitations of Data
    1. Key Limitations of Data
      1. Data is normally obscured and biased
      2. Data doesn’t always translate into actions and results
      3. Relevancy of data is a function of time, space, and stakeholder type
      4. Data has the potential to cause “analysis paralysis”
      5. Stakeholders’ needs precede metadata and data dictionary ontology
      6. Data management is expensive and time-consuming
      7. Data might distort innovation
      8. Data is never real-time; it is always historical
      9. Data has no relevance for first-time events
      10. Data can mislead decision making
    2. Conclusion
  9. Chapter 5- Normalizing Enterprise Data Quality
    1. Data Quality Dimensions
      1. Completeness
      2. Consistency
      3. Conformity or validity
      4. Uniqueness or cardinality
      5. Accuracy
      6. Correctness
      7. Accessibility
      8. Data security
      9. Currency and timeliness
      10. Redundancy
      11. Coverage
      12. Integrity
    2. Consequences of Poor Data Quality
    3. Data Depreciation and its Factors
    4. Causes of Poor Data Quality
      1. Data silos resulting from organization silos
      2. Interpretation and consumption of data happen in different ways
      3. Frequency of use and the number of users
      4. Poor business case for data origination and capture
      5. Data searching and retrieval challenges
      6. System proliferation and integration issues
      7. Different value propositions between consumers and originators of data
      8. Data rules affect business operations
      9. Data quality is time-sensitive
      10. Results of data quality improvements are normally transient
      11. Data conversion and migration issues
      12. Interface feeds
      13. System upgrades
      14. Manual errors
      15. Poor database design
      16. Data purging and cleansing
    5. Conclusion
  10. Chapter 6- Leveraging GQM for Information Management
    1. Deep Dive of the GQM Framework
      1. Conceptual level — the goal
      2. Operational level — the question
      3. Quantitative level — the metric
    2. Leveraging GQM for Data Management
      1. Step 1: Conduct stakeholder analysis
      2. Step 2: Formulate the stakeholders’ goal(s)
      3. Step 3: Identify the parameters in the goal statement
      4. Step 4: Translate the goals and parameters to quantifiable questions
      5. Step 5: Answer the questions and derive the hypothesis
      6. Step 6: Derive attributes from the questions, answers, and hypothesis
      7. Step 7: Derive metrics from the attributes
      8. Step 8: Apply the reverse GQM or MQG framework (Metric-Question-Goal)
      9. Step 9: Derive the data elements from metrics or answers
      10. Step 10: Profile the data elements and build the data model
    3. A Simple GQM Example
    4. Case Study 1: Application of the GQM for Insights
      1. Step 1: Conduct stakeholder analysis
      2. Step 2: Formulate the stakeholders goal(s)
      3. Step 3: Identify the parameters in the goal statement
      4. Step 4: Translate the goals and parameters to quantifiable questions
      5. Step 5: Answer the questions objectively
      6. Step 6: Derive hypotheses and attributes
      7. Step 7: Derive metrics from the attributes
      8. Step 8: Apply MQG framework
      9. Step 9: Define data elements from the metrics
      10. Step 10: Profile the data elements and build the data model
    5. Case Study 2: Application of the GQM for Compliance to Industry Standards
      1. Step 1: Conduct stakeholder analysis
      2. Step 2: Formulate the stakeholder goal(s)
      3. Step 3: Derive the parameters for the goals
      4. Step 4: Translate the goals to quantifiable questions
      5. Step 5: Answer the questions
      6. Step 6: Derive hypothesis and attributes
      7. Step 7: Derive metrics from the attributes
      8. Step 8: Apply the MQG framework
      9. Step 9: Derive data elements for these metrics
      10. Step 10: Profile the data elements and build the data model
    6. Conclusion
  11. Part III- Realize
  12. Chapter 7- Building Blocks of a Digital Enterprise
    1. Reference Architecture
    2. Principles
      1. Principle 1: Data is managed for a purpose
      2. Principle 2: Quality business data is an enterprise asset
      3. Principle 3: Realizing quality data takes investment
      4. Principle 4: Enterprise data has clear ownership
      5. Principle 5: Data shall always be accessible and shared
      6. Principle 6: Enterprise data is secure
    3. Patterns
      1. Baseline the current level of data management maturity
      2. Data-driven initiatives should be tied to a strong business case and business KPIs
      3. Enterprise goals should precede LoB goals
      4. Manage core business processes in the SoR
      5. Reference and master data should be based on data standards and MDM
      6. Data integration (EAI and ETL) should be specific to the data types and the business rules
      7. Distribute reporting in OLTP, BI, and analytics systems
      8. Data security practices should be integral to business operations
      9. Enterprise data governance (EDG) should be an active and functional business entity
      10. Data-driven culture should be enterprise-wide
    4. Standards for Data Management
    5. Chief Data Officer (CDO)
    6. Conclusion
  13. Chapter 8- Realizing the Data-Driven Enterprise
    1. Complying with Regulations Using the COBIT Model
    2. Compliance with Industry Standards
    3. Compliance to Internal Policies
    4. Business Insights and the DIKW Model
    5. Intuition and Decision-Making
    6. Enterprise Analytics
    7. Small Data, Big Data and Hadoop
    8. Transforming Data into Insight
      1. Identify the problem domain and define the goal
      2. Formulate questions and hypotheses
      3. Derive KPIs
      4. Identify and profile data elements
      5. Collect and normalize the data
      6. Visualize the data
      7. Analyze the dataset
      8. Synthesize the dataset
      9. Interpret and validate insights
      10. Communicate insights (data storytelling)
    9. Analytics in the Real World
    10. Conclusion
  14. Chapter 9- Managing Change
    1. Stakeholders in the Data Lifecycle
    2. Managing Change
    3. Aligning LoB leadership to the Enterprise
    4. Managing Change in Individuals and Teams
      1. Understanding the key deliverables of the stakeholders
      2. Identifying the elements that pertain to the behavior of the individual
      3. Understanding the change process
      4. Transitioning to the desired state
    5. Conclusion
  15. Chapter 10- Summary
  16. Appendix A- Interviews
    1. 1. Mario Faria, Managing Vice President, Gartner, US
    2. 2. Dr. Brandon Rohrer, Data Science Researcher and Author, US
    3. 3. Rohit Girdhar, VP, Infineon Technologies, Singapore
    4. 4. Ram Kumar Venkatachalam, SVP, QBE Insurance, Hong Kong
    5. 5. Bob Pollock, CIO, Cenovus Energy, Canada
    6. 6. Tobias Eckert, CIO, Leapple AG, Germany
    7. 7. Melanie Mecca, Director, CMMI Institute, US
    8. 8. Paul Zikopoulos, VP, Big Data and Analytics, IBM, Canada
    9. 9. Vibhav Agarwal, SVP, Reliance Power, India
  17. Appendix B- References
  18. Appendix C- Abbreviations
  19. Appendix D- Glossary
  20. Index