I had a great conversation about data strategy with a colleague the other day. While we were talking through the complexities of forming a data strategy, it occurred to me that many business leaders that don’t map a data strategy are not doing it because they think they don’t need it. They don’t recognize the importance of its relationship to the business model. I’ve written about offensive and defensive data strategies before, but this is on a more foundational level as it relates to the business strategy. I’m not naive enough to think that all businesses need a data strategy. If you’re a sole prop that builds sheds for a living, you likely don’t need to spend the time on a data strategy. Unless you’re planning to go C-corp and grow like crazy across the nation, a data strategy could be very beneficial. As a general rule, I would argue that company size and scale tend to have more of a need for data strategies that scale with them. Back to my analogy of a one-person company, no scale is needed; therefore, the data strategy is “don’t invest right now .”There are always exceptions, but that, in concept, is the basis for this article.
Does a Business Strategy need a Data Strategy?
Are data assets and the ability to derive value from that data a strategic capability you need/want in your business model? That’s the core question to ask yourself when thinking about the business. This can be in several forms, not just a singular item. If the business model needs customer adoption of services, then the data strategy might need to seek ways to enable insights into customer channels. If the business needs to understand if a product will work in the market, then the data strategy may seek to run experiments and capture data. Ultimately, the business strategy provides the north start, and the data strategy provides the decision framework for where to invest in data. Is data an asset? That’s a critical element you need to frame up in your data strategy.
One mistake I’ve encountered when talking to other leaders is, “Let’s hire a data scientist, and they can get value from all our data.” That’s not how it works or shouldn’t be the approach. There are stories of companies investing millions in data science teams only to be frustrated with the results because the team isn’t creating a return for the company. Gartner’s Nick Heudecker estimates that 85% of data science initiatives fail, a significant number. Ultimately, a data strategy well-formed aligns with the business objectives and empowers teams to make decisions on data collection, analysis, and sharing within the framework of the strategy. That’s what Signet Bank did, now known as Capital One.
The Tail of Signet Bank
The 1990s gave birth to a data revolution. In banking, we saw the first fraud neural nets, data warehousing from Ralph Kimball and Bill Inmon, and one interesting story about a bank that shifted towards analytic thinking by Richard Fairbanks and Nigel Morris. Banks, at the time, did everything as a singular offering. The credit card offered to you was the same as everyone else. The standing belief was that no customer would stand for different treatment. Fairbanks and Morris had extensive work in analytical thinking, believing that a bank with the right mindset could win in the market. Signet Bank’s leadership team felt that modeling profitability, not just default rates, would allow them to steal profitable customers away from other banks. At the time, they didn’t know how to do it until Fairbanks and Morris found them.
The business strategy was to capture customers from competitors through understanding profitability. The data strategy that formed from Fairbanks and Morris was to acquire data as if it were an asset. That meant, since the data didn’t exist, Signet would have to run experiments to develop a model. Models need data. If you’re treating data as an asset, you might have to pay to get that data, which is what Signet did to know the best credit terms to offer that would generate a return. Signet went from a 2.9% charge-off rate to 6%. That was short-term. As the models became better, they were able to adapt their offering and became very profitable. Not only did they become profitable in the long game they played, but they also boasted some of the best customer retention and lifetime value.
Signet spun off its credit card division into Capital One and made Fairbanks and Morris the company’s leaders. Capital One went early down their data strategy, plowing the way for those of us coming later. It might not be the strategy for everyone, but it’s a great example of aligning the business strategy and data strategy into a superordinate goal for a win!
References:
Most Data Science Projects Fail, But Yours Doesn’t Have To (datanami.com)
Capital One History: Founding, Timeline, and Milestones (zippia.com)
You can find more detail on Signet bank in the book Data Science for Business by Provost and Fawcett - Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking: Provost, Foster, Fawcett, Tom: 8601400897911: Amazon.com: Books