5 Key Ingredients To Becoming Data Driven

Blog Images (1)

So you have gathered a ton of data and hired all these superstars to analyze the data, build models etc, but this does not necessarily mean you are now a data-driven organization.

Being data-driven needs to be a priority at the executive level and become part of the culture in your 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'. It is the attitude of the organization and how they partner and collaborate with analytics and data science teams that are the key factors to influence culture.

Here are five 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, and 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 THE DATA SCIENCE/ANALYTICS 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, but this sometimes gets communicated to the data teams as “we’d like to test this thing, this way". 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 this 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. Several different teams across the organization 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 are 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.

However, if two people try to do the same analysis and come up with different numbers, you have 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 not 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 teams 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 but it doesn’t answer the question, the business asks you to pull them more data. Sit down, discuss the problem and let the business know that you are here to help.

If you’re interested in exploring our latest Data Science jobs, check out our live vacancies or upload your CV today to keep up to date with all the latest opportunities.

Leave a Comment

* Indicates a required field