As CIO of one of the largest data analytics companies in the world, Nick Daffan, knows a lot about turning data into a valuable asset. Because that is a goal of nearly every CIO in every company, as the role evolves to focus less on system uptime and more on using data to drive the business forward, I asked Daffan for some guidelines for how to take a bunch of data that is sitting in a disparate set of systems and create a leverageable, impactful capability.
While Daffan concedes that creating useful data is “a multi-dimensional challenge,” he does offer some foundational steps every IT leader should take.
1. Build a data-oriented culture
If your company’s culture does not inspire excitement about leveraging data in new ways, then you will have a hard time turning data into an asset. The tip of the spear on creating a data-oriented culture, says Daffan, is having a “team of people who have the ability and curiosity to use data strategically,” says Daffan. “You need people who understand how disparate data sources can interact to solve business problems.”
“We think of our data scientists as a community,” says Daffan. “We have good-sized team reporting to our chief analytics officer, who reports to me. But we also have teams that reside within our business units.” Once the central group of data scientists have developed a data modeling capability that is ready for commercial use, the business unit data scientists then integrate it into a product that addresses a specific client need. “Our concept allows people to move freely through the data science community, rotating into and out of the business units and from one business to another. The overall concept is to have a community where methods and information flow freely to where they are most needed,” he says.
2. Offer interesting, impactful work
Every company is in need of data scientists, making for a tight talent market. To counter this challenge, Daffan suggests that you understand that data scientists want what we all want: interesting work that creates an impact. “They want to get their hands on data that has both depth and breadth, and they want to work with the most advanced tools and methods,” he says. “They also want to see their models implemented, which means being able to help their business partners and customers use the data in a productive way.”
What constitutes interesting data? “The challenge of integrating unstructured data with structured data generates a lot of interest within the data science community,” says Daffan. For example, data scientists at a life insurance company have access to a large volume of structured data, including policy and claims information. Then, they can layer in unstructured data, like image libraries or insurance forms or even data from a voice recognition tool. “That’s when it becomes interesting,” says Daffan.
3. Know the rules
Does everyone in your company understand how your contracts and regulatory issues dictate exactly how you can use your data? If employees don’t understand the limits on their use of data, they might be too nervous to innovate. “If your teams don’t have a thorough understanding of appropriate uses of data, they will either overreach and use data inappropriately or they will go to the other extreme, and just retract and do nothing,” says Daffan.
Since the last thing you want to do is create a data capability that is outside of what you are contractually allowed to do, Daffan suggests “understanding the contractual and regulatory rules around your data and defining them in a way that people can easily understand. Knowing that you are on the right side of permissions is a huge enabler to innovation.”
4. Identify quick wins
Yes, getting your CEO and senior management to evangelize the importance of data is foundational to developing a data-driven company, but don’t bother if you can’t cement that support with delivering real results, or what Daffan calls “points on the board.”
Let’s say you are CIO of a financial services business and you have access to your customers’ banking data and credit card information. “If you are able to pull account, customer payment, and merchant transaction data together, you can produce a view of customer spend you did not have before,” says Daffan. “The ability to provide insights into what payment vehicle a customer uses for different types of purchases can be very useful to a bank’s product development strategy.” Any early revenue lift you can provide from a data analytics project will go a long way toward creating the culture you need for longer-term strategies.
5. Stay flexible
Your culture is hungry for data and your data scientists are on board. The last thing you want to do is lose all of that momentum by making access to the data cumbersome. Verisk provides data solutions to a wide array of industries, so flexibility is key to its business model. “Data scientists in each of our verticals have their own favorite tools and technologies, so we cannot be too prescriptive with our toolsets,” says Daffan. “Rather than lock in on a specific database technology, we are staying flexible by working in a cloud environment.”
Daffan finds that he and his team, with relative ease, can spin up custom cloud environments that allow data scientists to use their favorite tools to access the data. “Within ten minutes, we can create an environment where the data asset is just sitting there waiting for an access path.”