Retail and e-commerce data and analytics Leader Veronika Durgin speaks with John Dubois, Consumer and Retail Data and AI Leader at EY, in a video interview about her journey towards data, the impact of AI on leaders, the need for a solid data foundation, different aspects of AI, and the scope of treating data as an asset.

Shedding light on her background, Durgin shares how her immigrant parents wanted her to be a doctor, which led her to college as a pre-med. However, her passion for STEM persisted and she landed a computer operator job while working at a hospital lab.

Durgin recalls that as her first step into a tech career. Eventually, she got introduced to Microsoft Access and dipped her toes into data, which further led her to become a junior DBA.

As Durgin realized her love for data, she wanted to learn more. So, after her bachelor's degree, she went back to college and did her Masters in Software Engineering.

In her years as a DBA, Durgin focused on and specialized in performance tuning. After a considerable period, her curiosity shifted from operational data to analytical data, which changed her journey.

Further, Durgin notes that data is not just a career to her but a hobby. She mentions being with the Snowflake Data Superheroes chapter leading the Data Vault, participating in different events, and speaking about data, apart from her paying job.

When asked about the impact of AI on leaders, Durgin expresses her exhaustion and refers to it as a cognitive overload of sorting through hype versus useful things. In her opinion, GenAI is the cherry on top, but the cake needs to be baked first to put the sprinkles on it later.

With that analogy, Durgin highlights the need to ensure a solid data foundation first to be able to leverage AI and GenAI. Interestingly, she says, GenAI deals with unstructured data, while the leaders are still struggling to organize the structured data.

Therefore, it is critical to figure out and organize the data foundation, because whether one does analytics, AI, or ML, a specific use case will require appropriate quality data.

Moving forward, Durgin breaks down different aspects of AI and states that artificial intelligence includes ML and GenAI. Now, machine learning demands a specific quality and size. For instance, doing predictive analytics requires structured data.

Whereas GenAI requires unstructured data but of good quality, and for apps with AI, governance is crucial, says Durgin. When it comes to preparing structured data, she stresses ensuring that it is stable, historical, and of appropriate quality, depending on the use case.

Regarding unstructured data, Durgin admits to not having everything figured out but affirms that the foundational patterns are the same. Herein, one has to know where the data is, the quality of the data, and its appropriate availability for use.

Speaking of data assets and their future use, Durgin discusses a book called Infonomics by Doug Laney and shares that the book has had a profound impact on her. She maintains that the book talks about the actual value of data as an asset.

Continuing, Durgin emphasizes the personalization aspect, and the book delves into how first-party data plays a role in that. She reflects that every company has valuable data, which has increased with GenAI.

From a value perspective, the data shared willfully is more valuable today from a monetization standpoint. Then, the book talks about “zero-party data,” which is the data somebody willfully gives in exchange for something, and that data is of the highest quality.

Thereafter, Durgin mentions royalty programs as an example wherein, during COVID, some airlines used loyalty data as collateral to get loans from banks. She concludes by stating that this way, data can be used by companies as a massive monetization asset and for other creative things.

CDO Magazine appreciates Veronika Durgin for sharing her insights with our global community.

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