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Mumbai
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2014
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Tejasvi's blog archive

2024 (1) 2022 (2) 2021 (7) 2020 (5) 2016 (4) 2015 (5) 2014 (1)
Tejasvi Addagada
Accolade: Blog group founder

Tejasvi Addagada

Data Governance Head at Fortune 500 financial service provider
Message Message me Posts: 28 Comments: 8
Bio Tejasvi Addagada is a data officer, privacy officer and business transformation specialist with a global bank. Career History Tejasvi Addagada, a seasoned Banking strategist, orchestrates the process and data management symphony. With over 16 years of success, Tejasvi is considered one of the early thought leaders in the industry. The Data Alchemist Tejasvi transitioned from a software engineer to a business analyst to roles like data officer and privacy officer, sensing the need for active data management. Architect of Transformative Programs Across Fortune 500 firms, Tejasvi crafts solutions that resonate with business needs. His toolkit includes data strategy, risk management, app service rationalization, and process excellence. Thought Leader and Author Connected with industry thought leaders, Tejasvi has published two best sellers on data and risk management for leaders and practitioners.

Blogs

Bigger than Technology

Are you confident in the quality of your data and its impact on your generative AI model insights?

22 Feb 2024

The world of banking and financial services is abuzz with the potential of generative AI. It's a game-changer that can revolutionize customer service, boost revenue, and streamline operations. But with great power comes great responsibility - and a whole lot of regulatory challenges. We need a legal framework that balances market safety, consumer ...

Data Management and Governance

Does data risk need to be managed actively through a data risk function?

09 Dec 2022

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. In sectors like Banking, regulations drive enterprises to assess data related risks. Prioritizing and managing data associated with financial or op...

Data Management and Governance

Managing Data Quality is important to the success of digital driven Financial services

03 Apr 2022

Data is increasingly valued as an asset for companies, so ensuring that data is of high quality is imperative. Progressively, in the world of IoT, we are seeing machines make decisions. These decisions either provide insights into what's most beneficial for customers or reduce their service-relationship anxiety. In order for machine learning model...

Banking Strategy, Digital and Transformation

Democratizing data and thus credit to Citizens - Account Aggregator network

04 Sep 2021

Democratizing data for customers through responsible sharing will actually democratize credit for people in the financial ecosystem. As a part of India stack, the Account Aggregator (AA) network is based on a mix of frameworks Open Credit Enablement Network (OCEN) and The Data Empowerment and Protection Architecture (DEPA). Today, in financial in...

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Tejasvi is Commenting on

In Financial Services, can we measure Accuracy of Customer Data with Artificial Intelligence?

  I dont see any such inference even from public models. However, such insights even though can have a basis that cannot be given much weigt in a decision in traditional underwriting. Moreover, getting indicators that usually is not possible through AI/ML is a reality already. These help push the models far left to the value chain and assist in taking better decisions like an Income estimation.

19 Sep 2020 Reply Read article

Data Quality in Machine Learning.

  The first Data Quality challenge is most often the acquisition of right data for Machine Learning Enterprise Use cases. Even though the business objective is clear, data scientists may not be able to find the right data to use as inputs to the ML service/algorithm to achieve the desired outcomes. As any data scientist will tell you, developing the model is less complex than understanding and approaching the problem/use-case the right way. Identifying appropriate data can be a significant challenge. You must have the “right data.” More broadly speaking, Coverage, can be categorized under the Completeness Dimension of Data Quality and called the Record Population concept within the Conformed Dimensions standard. This should be one of the first checks to be performed before proceeding to other Data Quality checks.  

19 Sep 2020 Reply Read article

Navigating Data Residency and Privacy Compliance in the Cloud

  Niall, Good thoughts articulated well! What caught me in your article is the phrase " To do this, certain protocols and rules need to be put in place to ensure good governance over this process." To ensure this we need to progress on Governing the data in the cloud as well. Your further statements clearly bring out the classifications of data to be hosted in accordance to their risk profile. This fundametally requires a change to approach in which data is logically and physically classified. http://www.dataversity.net/integrate-data-privacy/ is an article that briefs on how we can define requirements for data to be hosted on various platforms including cloud.

24 Nov 2017 Reply Read article

A new perspective on the potential of data

  Nice perspective. In most organizations today; data is considered an Enterprise asset. It is no surprise if we see the value of data included in the balance sheets of business. Regulations like BCBS 239, GDPR, EU No 1024/2013, EMIR, MiFID2, as you rightly stated are the primary drivers that created a need to manage risks and value arising from data and its operations. Though, other drivers that are necessitating the needs include strategy changes, business model changes, and advancements in technology. I don’t see any specific time when cost cutting should be triggered. In my view cost optimization is an ongoing enabler in any organization. The existence of Data Governance and Data Risk management functions either centralized or distributed, based on the organization need, will help in realizing the complete value of data. This will further assist in simplifying the data landscape while rationalizing it, thus reducing operating costs associated with data operations and data related issues. This also enables new business discovery. My final thought is that Business should own their data, the value it creates and the loss related to its risks. Organizations should enable, empower and enforce data driven culture, responsibilities and accountabilities of data ownership to make your thoughts feasible. For further reading, you can refer http://dataassociation.net/dablog/call-it-a-success-by-integrating-risk-management-into-your-data-governance  

01 Jun 2016 Reply Read article