Who’s Answering the Next Big Question? VP of Data Amit Sethi of Momentive

During each episode of the Next Big Question podcast, we speak with executives and thought leaders about a big, timely business question. In this episode, Liz Ramey and I spoke with Amit Sethi, vice president of data at Momentive. We asked Amit: How does data strategy keep up with the pace of technology? 

In more than 20 years as a data and analytics leader, Amit has witnessed firsthand the interplay between technology and the business potential of data. He talks in detail about how technological capacity informed data practices and increased the business appetite for insights – which in turn spurred continued evolution in technology. 

But businesses today must move so rapidly to compete in the marketplace that it would be beneficial to break this iterative cycle and synchronize capability with demand. Here, Amit looks at the recent trend toward data products and the ways they create a more holistic data framework. He also thinks about a framework for balancing the constituent elements across the enterprise data journey:

It’s very important for us to stay close to trends and what happens in the industry, as leveraging the right technology stack for the right use case can – and will – provide your business a competitive advantage in terms of speed to insights, finding the secrets hidden in plain sight, and getting the answer to the questions you aren’t even thinking about asking.”

Amit Sethi:

One of the key things that’s happening as you start thinking about data as a product… you think about a product as a roadmap with a set of features. A product is not designed to solve for a particular use case; it’s solving for multiple use cases – it has a product/market fit. 

I’ll take one example of a very mature product that a lot of companies use under their data and analytics, and it goes by unified customer profile or Customer360, it goes by multiple names. But this is where you bring all the data that is related to your customers – your demographic data, the transactional data, the financial data, the clickstream data – and you bring it together. And for the first time you’re able to see how the customer is interacting with different aspects – how they are interacting with the customer support team, how the product usage is happening and is it going up or down, or things of that nature. 

If you zoom out, we know that data and analytics have become very important; nowadays, it’s very difficult to walk into a meeting where you’re making decisions without facts or data. The data analytics have truly integrated into the business process. And there are terms that are being used – data-driven insights, data-driven operating model – and if that’s truly the case, then data and analytics are also getting closer and closer to the domains and the business functions. 

So, we have this equation of elements, and on the left side is the systems and applications that are producing data, and that number is continuing to grow because if you look into modern IT, there are so many SaaS applications and different tools and enterprise applications that are getting used. 

The second thing, on the left side of the equation – and this is really, really growing – is that we are trying to have a better understanding of our customer’s life cycle. How the customers are discovering the digital properties or downloading the software or using the software or signing up. And product managers are interested in learning more about how their products are being used, so this is growing. 

Then on the extreme right side of the equation we have the consumer of the data. Because of data-driven insights, the number of consumers is increasing. We have more and more business functions asking for insights. Not only are more people asking for more insights, but we have more insights and more questions that need to be answered. Then there is the velocity, the time to market. Before it was fine if you put together a request for insight and it came in a quarter or multiple weeks, but now business is looking for fast results, because they depend on it to get to market faster. If you make a decision slightly late – depending on what industry you’re in – it could mean a loss of millions of dollars. 

Creating this equation, we talked about the left side and the right side, but in the center what we have – and this evolved through Hadoop and the tech-data journey – is the central data team or platform, which is now giving the perception that they are slow or the bottleneck. If the left side of the equation keeps going up and the right side of the equation keeps going up… the middle portion looks like a bottleneck.

In order to address this, there are a couple of new business models or process models that are coming together. They are not so much about technology; it’s more about a change in the mindset. The difference is that it’s very difficult for a central data team to keep up with all the knowledge as the business and domain is evolving. So, it’s the concept of having cross-domain ownership, working very closely with your business partners on the left side and having the co-ownership. As the product models are changing on which we are doing data instrumentation and product-usage, both teams are working together to evolve the data model and those data schemas. 

Listen to the full episode of The Next Big Question featuring Amit Sethi from Evanta, a Gartner Company here, or on Apple Podcasts, Spotify, or your favorite podcast app. 

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