Your Data Could Do More for You If You Learn How to Use It

For years, I’ve watched smart companies buy into big data. Yet despite huge investments, some of them fail to reap the rewards. A 2017 MIT study revealed that 63% of executives expected to see a large impact on their offerings from AI in the next few years. But of the 14% of executives who reported already seeing a large impact, most cited examples in automation, decision-making support, and operational diagnostics for internal capabilities. Those kinds of advances can contribute to higher profits and short-term differentiation, but operational efficiencies do not generally help organizations grow beyond their peers. They also aren’t the deciding factor that will carry you through a yo-yoing economy. So how, exactly, do successful organizations unlock sustained and differentiated value in an AI-enabled world, at a time when immediate change may seem necessary just to survive?

It starts with experimenting and iterating with data—in a responsible way— to find your organization’s unique value proposition. Intentional experimentation mitigates the risks of failure by ensuring that you learn which data skillsets are critical to your organization, while at the same time identifying product-market fit. It’s a process that can happen before, during, or after a broader digital transformation, and there are three different ways to approach these intentional experiments: scaling internal offers, piloting to stretch, and purposeful venturing.

Scaling internal offers

In 2004, a web design company called 37signals needed a project management tool to keep all of its work streams on track. Initially, its solution—Basecamp—was meant to be a tool that would help the company be more efficient, and serve as a single source of truth for all its projects. But the more valuable it became internally, the more the company realized that it could be valuable externally. Eventually, Basecamp became 37signals’ core business; it adopted Basecamp as its name, and folded other offerings under the new brand.

Scaling internal offers is a smart way to create new revenue streams that can also help drive an organizational purpose. It’s a particularly good approach for companies that value creating internal capabilities and investing in their employees.

Another example comes from a liability insurance company that had recently gone through quarterly reviews. The company took a deep dive into large lawsuits, setting case direction and identifying where liability existed and standards had been breached. These observations were used to set financial reserves and, after months of manual curation, create risk-management training materials. While this company had already developed a technique for improving best practices, leveraging data to create a digital tool would give them an opportunity to scale up dramatically. The resulting in-house product allows the company’s claims and risk management representatives to rapidly identify new and emerging trends, as well as strategize case management on the portfolio level, saving thousands of human hours and much more in lawsuit expenses over the course of a year. With a bit more refinement, the firm could offer a version of its tool directly to policy owners, which could allow them to adopt best practices in real-time.

Piloting to stretch

A classic example of this approach comes from Netflix, which started offering streaming video services in 2007, alongside their penalty-free mail order DVD rentals. When the streaming service was born, it was a digital replica of the video store experience, browsing and selecting movies from a wall. But as Netflix accumulated ever more data about what people watched and their preferences, the streaming service became a data-driven way to personalize and recommend content, better fulfilling its purpose of “entertaining the world.” In fact, developing this data-driven capability in-house has given them sufficient insights into viewers’ preferences, which they are now using to create their own content, dramatically shifting the entire media landscape because of the data they own.

Piloting to stretch is a method of launching a new venture that brings in cash and stretches the organization to more intentionally fulfill its purpose. Often, that can require an entirely new business unit, with a new culture and team, so that it can grow without absorbing existing constraints. This approach is particularly appropriate for organizations that are seeking a new lever for growth, or are looking to disrupt their industry.

For Ascensia Diabetes Care, it was about recognizing a service gap for patients newly diagnosed with type 2 diabetes. Most people, shortly after receiving their diagnosis, are scheduled for a brief consultation with a certified diabetes educator (CDE) who helps them learn the basics of managing the disease. But the short time a CDE spends with a client isn't nearly enough to establish new habits and enable sustained lifestyle changes. Ascensia saw an opportunity to create a new remote care service that would allow people with diabetes to interact with CDEs over a prolonged period of time through a telehealth platform, helping them better acclimate to their condition. To design the right program and path for each patient, Ascensia created data-driven tools that would help CDEs know how and when to interact with users. Initially, the company piloted the app with 60 people, and early results showed that users were willing to adopt and adhere to the program. Only two participants failed to complete the study—a stunning success rate when adherence to therapy for chronic illnesses in developed countries averages around 50%.

Purposeful venturing

Google may have started as a search engine, but in 2015 Alphabet emerged as an umbrella organization that made space for Google to pursue other ventures, some that might seem far beyond the scope of the core business. Alphabet’s interests— everything from life science (Calico) to transportation (Waymo)—are all in service of doing the right thing, and separating Alphabet’s other ventures from Google kept them from getting mired in particulars of the Google organization. This nimbleness has allowed Alphabet to fulfill its broader purpose, even during the extreme uncertainty of a global pandemic; the company quickly launched one venture, Verily’s Project Baseline, to research COVID-19, while Google and Apple have partnered to develop an exposure notification system.

Purposeful venturing is a way for an organization to create new business lines that drive growth. These ventures operate independently, with their own teams and cultures, and may require dramatically different ways of working that would be distracting for the core business’ operations. It’s a good fit for companies that might have many different ways of achieving their purpose, or for firms that can create symbiotic effects with auxiliary offerings.

In practice, it can also look like this: A nimble venture firm called 1848 Ventures (a subsidiary of Westfield Insurance) recognized that staffing is one of the biggest uncertainties business owners grapple with. Over-staffing eats into their already razor-thin margins; under-staffing means long lines and lost revenue. By rooting their innovation efforts in their purpose—to help their customers mitigate risk and uncertainty—the firm saw an opportunity to create a service that would help small businesses better manage their expenses through a tool that combines large data sets and an intuitive interface to optimize operational decisions. This new product was piloted with restaurants to help them offset the thin margins they need to survive.

Regardless of whether you experiment by scaling internal offers, piloting to stretch, or purposeful venturing, there are ample opportunities to simultaneously unlock product-market fit while getting a better grasp of the capabilities your organization needs to grow. Design offers a way to mitigate the risk of missing product-market fit and improve your odds of success in a way that dovetails with your existing capabilities and long-term vision. So after you pick your path, get out there and start prototyping early and often with data products, just like you would with anything else. It’s the best way to make sure that your data investment will push you past operational efficiencies, to sustained and real growth.

Illustrations by Nate Kitch