Data Innovators: Nitin Gupta

Here’s the second in our series of conversations with Data Innovators, sharing an abridged version of WisdomAI Co-founder & CPO, Kapil Chhabra, chatting with Visa’s Head of Enterprise Analytics, Data & AI, Nitin Gupta. 

I’d love to start by understanding your current role. 

In my current role, I lead Enterprise Analytics globally within the Data & AI group at Visa Inc. Leading a team of data professionals, I help drive the company's data strategy and utilizing AI to derive critical business insights. My team is focused on delivering enterprise-wide BI & Analytics, AI & ML and Gen AI products enabling critical data insights using payments data for Visa’s customers and various internal business units. One such example is turning Visa payments data and consumer travel data into actionable insights that provide deep, timely insights into visitor and resident spending patterns, seasonal visitation trends, peer destination performance, and trip details down to the postal code level.

What data transformations have taken place in the past few years that have changed your company and your team the most?

Here are the three examples that come to mind first:

• Adoption of Big Data Technologies: The shift from traditional data processing systems to big data technologies like Hadoop and Spark has allowed us to process and analyze large volumes of data at unprecedented speeds. This has led to more data-driven decision making.
• Growth of AI and Machine Learning: The integration of AI and machine learning into various products and processes has enabled us to uncover deeper insights and automate various tasks. This has transformed the roles within teams, with a greater emphasis on data science skills.
• Focus on Data Privacy and Security: With data breaches becoming more common, there has been a greater focus on data privacy and security. This has led to the implementation of stricter data governance policies and the creation of roles specifically focused on data security.

What are the biggest problems still holding back data teams today?

Here are a few key issues that I’ve seen and peers at other companies bring up with me:

1. Data Quality: Poor data quality can lead to inaccurate insights and faulty decision-making. This includes issues like missing data, inconsistent data, and outdated information.
2. Data Silos: Data stored in isolation, or in 'silos', within different departments can prevent a unified view of information. This makes it difficult to derive meaningful insights from the data.
3. Lack of Skilled Personnel: There is a high demand for skilled data professionals, including data scientists, data engineers, and data analysts. 
4. Data Privacy and Security: Ensuring the privacy and security of data while focusing on innovation is a significant challenge, especially with the increasing number of data breaches and the implementation of stricter data protection regulations worldwide.
5. Integration of New Technologies: Integrating new technologies like AI and machine learning into existing data processes can be complex and time-consuming. 
6. Data Governance: Establishing effective data governance policies to manage the availability, usability, integrity, and security of data can be a complex task.

How has AI changed your role already, and how do you see it evolving in the future?

AI has changed so much of what we do, most notably:

• Data-Driven Decision Making: AI has enabled data leaders to leverage large volumes of data for decision making. Through machine learning and predictive analytics, data leaders can uncover trends and patterns that inform strategic decisions.
• Automation of Tasks: AI has automated many data-related tasks, such as data cleaning and pre-processing. This allows data leaders to focus more on strategic tasks, like aligning data initiatives with business goals.
• Enhanced Data Analysis: AI tools and techniques have allowed data leaders to conduct more sophisticated analysis, uncovering deep insights that were previously difficult to obtain.
• Data Security: AI has played a crucial role in enhancing data security. Machine learning algorithms can detect anomalies and potential threats, helping data leaders safeguard their organization's data.

Looking forward, I can see AI leading to:

• Greater Emphasis on AI Governance: As AI becomes more prevalent, data leaders will need to focus more on AI governance, ensuring ethical and transparent use of AI.
• AI Skill Development: Data leaders will need to champion the development of AI skills within their teams, as the demand for these skills increases.
• Integration of AI in Business Strategy: Data leaders will play a key role in integrating AI into the broader business strategy, helping their organizations leverage AI for competitive advantage.

What data advancements are you most looking forward to?

Three big ones I see are:

1. Automated Machine Learning (AutoML): AutoML platforms are becoming more sophisticated, enabling more efficient model selection, hyperparameter tuning, and feature engineering. This can help democratize machine learning by making it more accessible to non-experts.
2. Explainable AI (XAI): As AI models become more complex, understanding their decision-making process is crucial. XAI aims to make AI decision-making transparent and understandable, which is essential for building trust and facilitating wider adoption of AI.
3. Federated Learning: This is a machine learning approach that allows model training on a large number of decentralized devices or servers holding local data samples, without exchanging them. This is particularly exciting for privacy-preserving AI.

I’m also excited about Quantum computing, performing complex calculations at rates far beyond current technology, as well as Edge computing, processing data closer and therefore much faster than we can today with centralized cloud-based locations. Synthetic data generation and concepts like differential privacy and homomorphic encryption are also intriguing because they’ll make it easier to do analysis without exposing the actual data (or the actual data won’t be as sensitive if it’s synthetic). 

Let’s imagine a new product could use AI to offer every employee access to their own personal data analyst; how would this transform your team and company?

It would be transformative in so many areas; to highlight three:

• Data-Driven Decision Making: if everybody were capable of understanding and interpreting data, we’d bring data to many more decisions than we can today. I believe data leads to more informed and insightful decisions, so more data-driven decisions would massively expand the number of informed and insightful decisions we could make. 
• Innovation and agility: if we could all quickly and easily access and interpret data, we’d be able to respond so much more quickly to market changes, customer needs, or operational issues. And the faster we can spot trends, correlations, and anomalies, the sooner we can innovate, whether capitalizing on opportunities or course-correcting to solve challenges. 
• Collaboration: If everybody speaks “the language of data,” it would improve collaboration between departments. For example, there would be fewer anecdotal data driven arguments and a greater ability to align together based on shared data insight foundations. 

Finally, it's important to note that such a transformation would also require robust data governance policies to ensure data privacy and security, as well as training and support to help employees adapt to new tools and ways of working.

We certainly hope that WisdomAI can be as transformative as you’ve outlined!

As a final question before we close, what’s your favorite example of data-driven discovery across history?

My favorite one for data exploration is Florence Nightingale's use of data visualization during the Crimean War in the mid-nineteenth century. Nightingale was a British nurse, statistician, and social reformer all combined together, and collected data on soldier mortality during the war. She created a type of chart known as a "coxcomb" or "rose" diagram, which was a combination of stacked bar and pie charts, to represent the number of deaths that occurred due to preventable diseases, wounds, and other causes. Her estimates suggested 10X the number of soldiers dying from diseases such as typhoid, cholera and dysentery compared to deaths on the battlefield. Her ability to visualize the data and to turn it into insights led to the UK government making numerous sanitation reforms and establishing the foundations of what we consider modern day hygiene practices. 

It’s amazing how much she innovated across nursing, stats, sanitation practices, and more. What a perfect close. Thanks, Nitin, this was a great conversation.