Organisations across the world recognise the importance of having effective data analytics capability that enables them to make business-critical decisions based on fact rather than instinct. Many businesses, however, are not fully realising the value of this capability.

There is currently a void, often referred to as the ‘data skills gap’, between a few expert data scientists and analysts within a business, and the managers on the ground delivering projects and initiatives.

A lack of understanding results in insights not being acted on, or interpreted badly. Managers require the tools to effectively interpret and use data to make strategic decisions at an operational level.

In our research with leading organisations in the UK, we identified that managers and team leaders need to be able to:

1. Understand how databases work so that they can understand the context of data analyst requests and resourcing of such tasks.

2. Validate data analysis – to enable them to effectively challenge and interpret the meaning of what they are being presented with.

3. Tell a narrative with data – so they can use data to influence and drive decision making.

4. Understand the cognitive aspects of how humans process information and therefore how to visualise data to tell a story.

5. Effectively communicate with data analysts/scientists – who often need those who are knee-deep in the reality of the operation to be able to properly communicate the context, thereby enabling the data scientist to build a meaningful model or process.

6. Understand the link between the data and the real-world drivers – the ability to interpret a dashboard and understand what the metrics mean for the organisation and the opportunities they reveal.

7. Have effective decision-making frameworks – ultimately there is no point having the data, being able to validate, interpret and challenge it unless managers know how to properly factor it into any decision they make.

8. Know enough about data science to know what could be done – managers don’t necessarily need to know how to deliver a predictive analytics dashboard that forecasts sales, but they need to know that it’s possible and appreciate the value of such insights.