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Tejasvi Addagada

Tejasvi is an experienced data management specialist as well as a data strategy consultant.  He is a best-selling author of two books on data management and data risk. Moreover, Tejasvi has assisted fifteen organizations in creating business models that won data-based benefits based on custom operating models, and tailored organizational structures, while also leveraging deep-technology engineering.

As one of the earlier data provocateurs, Tejasvi specializes in bringing data monetization plans into reality. He is always keen to connect with organizations and practitioners with a passion to transform their business through data-based capabilities.

You can be a Chief Data Officer (CDO) or a data engineer; If you are looking for thought-share, look no further and send a message. Tejasvi can share my real-world scenarios of building or running a data office.

Some of the dimensions he actively manages today are central data operations, data engineering, big data lake, 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
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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%.

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.

Book on Data Risk Management by Tejasvi Addagada
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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 .

Gradient


                         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.

More collection of articles are available in the BLOG section.

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?

Read More
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

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Interviews & Fireside Chat