5 Key Ingredients To Becoming Data Driven

Board Min

So you have gathered a ton of data and hired all these superstars to analyse the data, build models etc. But does this alone make you data-driven?

Well, no that's not really enough. Being data-driven needs to be a priority at the executive level and become part of the culture of the organization; more so than simply having a team with the necessary capabilities. A number of the following points make little reference to fancy models or 'leveraging AI'. To be clear, you do not need fancy models to consider yourself data-driven. Attitudes of the organization and how they partner and collaborate with analytics and data science teams, these are factors that influence culture.

Here are 5 key ingredients to becoming data-driven.

1. Have a data strategy in place.

Your data strategy needs to be driven by the business strategy. It needs to be specific and measurable. Any analysis of data that is undertaken must have a strong use case. It's very rare that a company seeks data insights that don't align with the long term priorities of the business. Analytics shouldn't get side-tracked answering lower value questions when they should be working on the problems that will save the business the most money.

2. Test design and analysis is owned by analytics/data science teams.

Although data science and analytics teams often come up with fantastic ideas for testing. There are also many ideas that come out of a department that is not in analytics. For instance, in eCommerce, the marketing team will have many ideas for new offers. The site team may want to test a change to the UI. This sometimes gets communicated to the data teams as “we’d like to test “this thing, this way”. And although these non-analytics teams have tremendous skill in marketing and site design, and understand the power of an A/B test; they often do not understand the different trade-offs between effect size, sample size, solid test design, etc.

3. Dashboarding is in place

This is a true foundational step. So much time is wasted if you have analysts pulling the same numbers every month manually, or on an ad-hoc basis. This information can be automated, stakeholders can be given a tour of the dashboards, and then you won’t be receiving questions like “what does attrition look like month over month by acquisition channel?” It’s in the dashboard and stakeholders can look at it themselves. The time saved can be allocated to diving deep into much more interesting and thought provoking questions rather than pulling simple KPIs.

4. Data governance and consistent use of data definitions across departments.

This one may require a huge overhaul of how things are currently being calculated. The channel team, the product team, the site team, other teams, they may all be calculating things differently if the business hasn’t communicated an accepted definition. These definitions aren’t necessarily determined by analytics themselves, they’re agreed upon. For an established business that has done a lot of growing but not as much governance can feel the pain of trying to wrangle everyone into using consistent definitions. But if two people try to do the same analysis and come up with different numbers you’ve got problems. This is again a foundation that is required for you to be able to move forward and work on cooler higher-value projects, but can’t if you’re spending your time reconciling numbers between teams.

5. Analytics/data science teams collaborate with the business on defining the problems.

Senior leaders need to make it clear that a data-driven approach is a priority for this to work. In addition, analytics often needs to invite themselves to meetings that they weren’t originally invited to. Analytics needs to be asking the right questions and guiding analysis in the right direction to earn this seat at the table. No relationship builds over night, but this is a win-win for everyone. Nothing is more frustrating than pulling data when you’re not sure what problem the business is trying to solve. You pull the data they asked for, it doesn’t answer the question, so the business asks you to pull them more data. Stop. Sit down, discuss the problem, and let the business know that you’re here to help.

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