Data Management and Governance Services: Simple and Effective Approaches
Data Management and Governance services book
Synopsis
Today, Organizations realize the direct value and benefit from managing their data as enterprise asset. 33% of the firms are thus actively assessing the direct and in-direct value from their data.
          The regulatory landscape is fast changing with BCBS, GDPR influencing firms and other geographies, to enable a control environment and ensure preparedness. The focus is gradually shifting from just having to clean data to having this asset actively managed by standardizing service operations across the firm. It assists in driving a data driven culture in a distributed way through the grassroots of Enterprise.

The book focusses on:
  1. Overcoming common challenges in data offices
  2. Bridging Gaps in your data management strategy
  3. Setting up data quality and metadata services
  4. Formalizing Governance based on org culture
  5. Defining a benefits realization model to measure outcomes

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When Tejasvi first approached me to contribute to his book ‘Data Management and Governance Services’, I did not have much awareness of his past work. What intrigued me though was the scope of the book. With so much buzz about big data & data analytics, people tend to forget that managing and governing data and ensuring that underlying data is of high quality is crucial to delivering actionable business insights. I always would like to support any book that focuses on ‘simple and effective approaches to data management and governance’.
​This book is a solid guide and reference to standing up and managing a data governance and quality function. Addagada acknowledges many of the challenges in doing so and how to overcome them. His examples are fun and informative, and frameworks are useful. Although I strongly oppose the notion of a "data owner" (due to this concept encouraging data silos/hoarding and inhibiting information's status as an *enterprise* asset), the accountabilities and responsibilities he lays out are legitimate and comprehensive. 
Ramesh Dontha
Doug Laney
Data Strategy, Data Governance / Data Management, Big Data Professional, Entrepreneur, Writer; Twitter: @rkdontha1
VP & Distinguished Analyst, Chief Data Officer Research and Advisory, Gartner
Tejasvi’s book stands out for three reasons: (1) It’s practical (2) It’s simple to understand and follow (3) It broadens the appeal to a larger audience.  

It’s practical: There are lot of really good books on theory of data management and governance but enterprises are looking for guidance on practical steps. This book focuses on key components such as establishing a standardized metadata service and running a data quality service in almost a step-by-step fashion. I could see myself following the steps outlined in the book to setup data management and governance completely.

It’s simple to understand: Tejasvi leverages his practical experience to convey intricate concepts in an almost folksy manner to relate to everyday experiences. Let me give an example with discussion on data ownership in this book. Data ownership is a tricky item in data management and data governance. This book breaks that topic into 10 steps that anyone can understand and relate so it becomes easy to implement.

It broadens the appeal: One of the challenges with data governance is that few people understand it’s scope and importance. The benefits of data governance will multiply if more people in organizations can internalize the principles behind data governance, Tejasvi’s book accomplishes that objective by focusing on the most important features of data management and governance and explaining them in simple but practical terms.

I commend Tejasvi for tackling this subject and attempting to broaden it’s appeal. I’d recommend this book to anyone who wants to understand data management and governance concepts and plans to implement them in their organizations. Happy reading.
Also, it would have been good to see a more comprehensive representation of existing materials on the concept, e.g. the 100s of best practice documents, and maturity model published by Gartner.

And while the book discusses the difference between data value and data management value, and includes a value-risk framework model, I would have liked to see more depth in Chapter 7 - Assessing Value from Data Management and Governance, in particular a discussion of the various data valuation models available today and how they could be used to justify/prove the benefits of data mgt and governance.