There are countless examples of companies who have adopted a data-driven approach to address flatlining sales and decreasing profitability. Target, the retailer, suffered from flat in-store sales in the mid-2010’s. The retailer decided to go deep into data science and data engineering capabilities in discrete smaller opportunities, such as improving on-shelf availability of merchandise, reducing inventory, and improving operating efficiency. The result was a big boost in profitability for the entire organisation.
To make data-driven decisions, a basic level of handling, understanding and communicating with data, is required across companies. 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, because a lack of understanding results in insights not being acted on, or interpreted badly. These managers require the tools to effectively interpret and use data to make strategic decisions at an operational level.
When questioned, managers from across a range of industries felt that they were lacking some consistent skills:
Research from Accenture indicates that only 20% of all employees said they felt confident working with data. This lack of confidence prevents those employees from using the data to inform their decisions, with half of all employees stating they tended to rely on gut-feel rather than through evidence-driven insights.
This skills gap is estimated to cost £10bn in lost productivity each year.
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