Being data-driven means sometimes stepping back, says Strava data director

Cathy Tanimura outlines her methods for success when using data to build a product and encourages others to understand that data is not a silver bullet

15May

"Part of being data driven, particularly when you are building a product, is stepping back and saying data doesn't always have all the answer," Cathy Tanimura, senior director of analytics and data science at Strava, remarked as she began her presentation at DATAx San Francisco. "Instead it is about really thinking about what the data can do to help us understand the opportunity."

Tanimura urged companies to instead use data to help them "find a beacon through the sea" and uncover good ideas.

"The reality is when you go through a journey, you're going to have some dead ends and challenges along the way," she observed.

However, on the point of data application she noted that, when putting an idea forward, executives must always think about what data they have to guide them whether to go forward or not.

Tanimura outlined three lessons she had learned when it comes to using data to enhance the customer experience.

Find the insights that matter

Understanding customers is key to creating loyal users of your product, Tanimura explained, and this means cutting through the noise and uncovering the insights which will be the most helpful. She noted that for Strava this was discovering the social aspect to its platform and using this to understand users.

For example, the firm's data uncovered that people who exercised with others reported more data on the app, with those in clubs being three times as likely to upload data onto the app.

Gather data from many places

Behavioral signals are one of the ways that Strava gathers its data.

"We can see people following each other, we can see people viewing each other's activities and commenting, so we know they are being social, but this does not always help us with products," Tanimura said, echoing her earlier sentiment that it is key to know when to step back.

She explained how her team had also looked at their own experiences for insight, but quickly added with this approach companies have to be particularly careful, as their team will always only represent certain subsection of society. Community input was the area Strava had found the most useful.

"We have community forums, we have people telling us what they don't like which is helpful," she said. "We also get requests from what people would like us to add to the platform."

Layering ML/AI on top of your strengths

When it comes to applying AI and machine learning in business, Tanimura reiterates the importance of taking a moment to consider its benefit from a distance.

"The important thing is to bring it back to: Where is this business value? What does this add?" 

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