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The Importance Of Building A Data-Driven Culture

We talk to Stewart Duncan, Director of Data Science at Simply Business

4Jan

We spoke to Stewart Duncan, Director of Data Science at Simply Business, the UK’s largest on-line insurance business, about how adopting a data driven culture has given his company great advantage and insight. 

Why is building a data culture important?

We believe that the key to great products and services is trying out new ideas as quickly as possible, measuring the results, and then picking the good ones and scaling them. This virtuous cycle of build-measure-learn is fundamental to how we work, and would be impossible without a proper data driven culture to help our continuous experimentation.

What we have learnt about our large SME customer base is that they are incredibly entrepreneurial and diverse - and this gives us plenty of opportunities and ideas for ways to serve them better in a fairly un-transformed insurance market. Without a good way to test out the impact of these ideas at the lowest possible risk, we would struggle to find and focus on the best ones. Being data driven allows us to hone in on the innovations that really matter, with great results for both our customers and our insurance partners.

For us, being data driven is about making sure that our decision makers have the data they need when they need it. It also means they have to actively use it to help them decide what to do next, what not to do, and where to double-down. They are busy people, so the insight has to be easy-to-use and clearly visualized, which makes our choice of analytics tool very important.

What have we done already and any advice?

We have taken the approach of making as much data available as possible to our decision makers. We don't want them to have to fret about what questions they can ask of the data, or be overly concerned about taking up the data team's time. Sometimes, the best insights come from a whim and we want them to be able to indulge their curiosity. So if a marketer has a hypothesis that IT professionals are more likely to be looking for equipment cover during the Black Friday sales (because they've possibly bought a shiny new laptop), they should be able to check for a correlation from our captured data easily.

Our choice of data architecture and tools really helps in this regard - analytics tools that work well on top of scale-out databases like RedShift make it possible for all that granular information to be made available with relatively little overhead. By creating a series of 'template' analyses that the data team maintain, it is possible to empower users to experiment by editing and changing these, using user-focused query tools like Looker, which has allowed Simply Business to quickly and easily visualize data from their event capture platform to provide great insight while also enhancing the customer experience. The templates give them a touchstone that allows them to know if they have strayed too far off course, so that they can be confident in the results.

What does the year ahead hold?

For us, next year is about extending our data architecture so that it provides intelligence into our existing systems - to date, we have used it to provide intelligence to decision makers (i.e. people) through analysis and dashboards, but with some enhancements we can use the same data capture and enrichment architecture to allow for automated decision making. 

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