Tejasvi Addagada

An experienced Data Risk Specialist and Data Strategy Consultant, and best-selling author. on data management. Assisted organizations in winning data-based benefits based on custom operating models, and organizational structure, while also leveraging deep-tech innovation.

As one of the early data provocateurs, I specialize in bringing data monetization plans to reality. I am always keen to connect with organizations and practitioners with a passion to transform their organizations with data-based capabilities.


You can be a Chief Data Officer (CDO) or you can be a data practitioner; If you are looking for thought-share, look no further and give me a shout-out. I can share my real-world scenarios of building or running a data office.

Some of the dimensions I closely manage today are data warehousing, data quality, metadata, data fabric, central data operations, master data management, reference data management, data governance, cloud data management, and data architecture.

data risk specialist

My Books & Publications

You must hear this often if you manage any kind of risk - risk and value, go together. And that's true, of course, for data! Both data and its infrastructure must be managed for their benefits and risks. The purpose of the book is to elaborate on this need to formalize data risk management.

Today, regulations drive enterprises to assess data-related risks. Prioritizing and managing data associated with financial or operational risk has been the cornerstone of most regulations like BCBS, CCAR, and GDPR, to name a few. Nevertheless, data risks can extend beyond regulations to improve existing control environments in companies. By doing so, we will maximize the potential of data capabilities to reach 100%.

Through structural alignment within the board and formalizing a data-risk function, the book focuses on managing data risks. Furthermore, the book explains quantitative and qualitative approaches to data risk assessments along with popular tools and techniques. Also, Tejasvi discusses a proven approach to managing data risks called capability-based assessment. As a technique, this can also be applied to data risk planning and formulating a data risk strategy.

Twenty data risks and privacy risks are provided in this book by way of examples. These are accompanied by details such as risk statements, scenarios, causes, and categories of impact if the data risks are to manifest.

Tejasvi Addagada-Data Management & Governance

The book provides frameworks for business, operational, and technology leaders that are simple and effective in managing data. Many data offices have challenges in actively managing data and deriving consistent value from the data science, Bigdata, and reporting programs.


Organizations across industries are embracing data management and governance practices - primarily driven by regulation and value creation by monetization. However, it is important to set up a data office while the firm must ensure the sustenance of such function. Moreover, data governance is a pervasive enabler that supports a firm's corporate governance principles.


Tejasvi Addagada, through the book, highlights how an Enterprise can: - Overcome challenges in data offices today

- Create a data management strategy and capabilities to traverse data maturity

- Set up metadata and data quality management as services

- Formalize governance as a function through an operating model, based on an enabling culture

- Define a benefits realization model to assess and monitor the value of managing and governing data .


                         Learn More About Me

tejasvi addagada

Tejasvi Addagada is a data practitioner and consultant assisting fortune 500 firms. He helps to build and optimize data management and governance solutions. Tej provides a wide range of services including data strategy, risk management, app service rationalization, digital transformation and process excellence. 

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I write articles while I also speak about the aspects that intrigue me and that make organizations better at managing data and privacy.

Data Governance Can Create “Data Trusts” for Consumers in Organizations

Data Governance Can Create “Data Trusts” for Consumers in Organizations

How does Data Governance Differ from Data Platform Governance?

How does Data Governance Differ from Data Platform Governance?

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Customer Data Protection: Deriving Value and Ownership

Customer Data Protection: Deriving Value and Ownership

The Data Quality Dimension “Coverage” is the Most Prominent for AI Outcomes

The Data Quality Dimension “Coverage” is the Most Prominent for AI Outcomes

Interviews & Fireside Chat