Approaching Data as a Product with Sanjeevan Bala, ITV’s Group Chief Data and AI Officer

Sanjeevan Bala is the Group Chief Data and AI Officer at ITV, a vertically integrated producer, broadcaster, and streamer based in London. He’s charged with leading the company’s digital data and AI transformation to enhance how ITV produces, promotes, distributes, and monetizes content, and was named to the number-one spot on the latest DataIQ 100 list of the most influential people in data.

ITV is also an Alation customer, and Sanjeevan was a guest recently on the Data Radicals podcast hosted by Alation CEO Satyen Sangani. They discussed the increasingly popular notion of treating data as a product and creating data product manager roles to improve data maturity for the business while increasing business literacy for the data team.

Storytelling with data

Sanjeevan’s background spans management consulting gigs working with huge enterprises to data-focused roles at more entrepreneurial startups. He’s been in data and data science leadership roles for nearly a decade now, with four of those years as ITV’s Group Chief Data & AI Officer. Surprisingly, his consulting experience put him on a track toward a career in data.

“[I was] really honing the art of storytelling because you have to be quite credible with a client as you move from one sector to the next,” Sanjeevan explained. “You become really adept at absorbing vast amounts of information, synthesizing it very quickly, and becoming quite credible when you're engaging with clients.”

As Sanjeevan moved into roles with startups and larger scale-ups, he found that his experience in building credibility as a consultant was based on his ability to craft compelling stories around the data. That then informed his approach to managing and building data teams and eventually viewing data as a product the business uses to drive results.

“Often there's misalignment in teams, in organizations, and that, I think, creates a level of discomfort and confusion around why you are here,” he says. “We spent a lot of time at the start really being absolutely crystal clear why we are here, what we are trying to achieve, [and] linking it specifically to business outcomes. And then it creates a whole set of different conversations around, well, what's that mean for the culture we're going to create? How might we think about diversity in terms of recruitment? How might we think about not just data literacy but also business literacy?”

Aligning data with business outcomes

By linking data programs to business outcomes, Sanjeevan uncovers a playbook to guide the entire data strategy. He says the data team’s purpose is to drive business outcomes, but that then highlights an apparent disconnect for most organizations.

“One of the common challenges I think you see with data programs is they're not always linked to a business outcome,” added Sanjeevan. “We talk a lot about the last mile to ensure everything in the business is set up, everything in the last mile is there so the business can actually activate and realize the value we are mapping and we're working towards.”

Sanjeevan faced a similar challenge in his early days at ITV. The company was moving data to the cloud and had a talented team of data scientists, analysts, and engineers. However, the team needed a link to what the business was trying to accomplish with data.

“It was a defensive strategy, one where it was receptive to requests; it would take orders from the business and then fulfill those orders. There wasn't a connection to the outcome. There wasn't a story you could really tell.”

Embedding data teams with the business

So Sanjeevan enlisted his team to create value cases by working with the business to understand their needs better. For example, ITV wanted to retain more customers and have them view more content. When working with the marketing team, acquiring more users for ITV products could be connected to a cost and a business result like increased viewing and advertising revenue.

“You can translate a lot of [data products] to those value outcomes, which then means the business owner is responsible for the realization of that value,” continued Sanjeevan. “But you can very elegantly then link what you're doing with those outcomes. That was a massive connect, and once we did that, we then got a huge amount of board and [executive committee] sponsorship.”

The data team needs to understand the business intimately to build data products the business needs. A data scientist won’t understand the importance of cost-per-click over cost-per-acquisition as much as a marketer, for example; the same as the marketer won’t understand the language of data science. Unless, Sanjeevan thought, the data team was decentralized and embedded with the business to be a part of those last-mile discussions. Being close to the business also helped the data team find more opportunities to help other parts of the business.

“Once you fully decentralize, you can build a data product in marketing that's phenomenally useful to marketing, but often marketing and sales are solving the same problem, but looking through the other side of the telescope,” Sanjaveen said. “So what you often find is something you build for marketing…would create a lot of value for [sales]. We're seeding ideas. We look at other opportunities to extend a particular data product because it could then have multi-use across the business.”

Data literacy and business literacy

Sanjeevan’s conversation with Alation CEO Satyen Sangani on the Data Radicals podcast continued to drill into experimentation to drive organizational change, why data team decentralization is critical, and where AI is having the most impact. However, the crux of it all is increasing data literacy across the business by increasing business literacy on the data team.

“One of the things we did very early on is we made sure these (data) teams were embedded in the business units,” said Sanjeevan. “It's really important they go native, and then they feel like part of the organization, a part of the fabric of those teams, and then the ideas come from within. That helped really accelerate and galvanize the strategy and get us to where we are today.”

A pragmatic approach to AI for business

As Chief Data and AI Officer, Sanjeevan revealed core tenets of his process for AI, which include:

A Pragmatic Approach to AI: Leaders should deploy AI where it directly impacts business outcomes, such as increasing advertising yield or improving productivity. Use cases are grounded in real-world strategies rather than hyped possibilities. “Our approach has very much been grounded in what are we gonna do with it and how are we gonna use it,” he shares.

Selective Experimentation: Rather than pursuing numerous experiments with marginal gains, Sanjeevan emphasizes identifying high-impact projects that could yield significant benefits, even if fewer in number.

Last Mile Focus: Success hinges on aligning AI initiatives with business objectives and ensuring buy-in from key stakeholders. The "last mile" of implementation and adoption is crucial for realizing the full potential of AI solutions. “How do we create that pull in the business?” Sanjeevan elaborates. “Do we get confidence from the business…? …And if you can get that bit right, I think going from trial to production gets a whole lot easier and you get the support, the endorsement.”

Balancing Innovation and Regulation: While acknowledging the need for regulation, Sanjeevan points out that the US model offers a framework that allows for innovation and adaptation over time. The US model, with a phase of litigation preceding regulation, is potentially more conducive to fostering innovation than what’s done in the EU.

Overall, Sanjeevan’s AI approach involves a blend of strategic alignment, selective experimentation, and a cautious yet innovative outlook toward regulation, aiming to maximize the value derived from AI while mitigating risks.

Listen to the interview, and be sure to subscribe wherever you listen to podcasts: Apple Podcasts, Spotify, or search for “Data Radicals” from your podcast app of choice.