Through a special arrangement, what follows is a summary of an article from Retail Paradox, RSR Research's weekly analysis on emerging issues facing retailers, presented here for discussion.
At its recent annual analyst meeting in Steamboat Springs, CO, SAS referred multiple times to four key user groups. To paraphrase a bit, the four are business management (basically, deciders), business analysts (the people who prepare the analysis that helps deciders decide), IT staff (which keep the infrastructure running), and data scientists (who basically make the data useful and easily consumable by the business analysts).
Data scientists? I can honestly say that I have never met a data scientist in retail. They are a class of user that appears to have all the frequency of unicorns and dragons in the retail enterprise.
In theory, data scientists help business analysts model and understand their data. When data scientists exist, data can actually be democratized (as in pushed closer to the front line), because it is the data scientists that ensure that the data is described, staged, and modeled in a way that makes it more accessible to casual users. Casual users best understand the problems they are trying to solve, and data scientists best understand the opportunities and constraints of the data that is available to provide insight into how to best solve problems. Only when the best of both worlds is combined can real insights be gained — and acted upon.
So why are there no data scientists in retail? First, they tend to be expensive resources. Retailers are competing with investment banks, pharma, insurance companies and telcos — industries that have known the importance of data actually for a lot longer than retail has.
Second, historically, the only data that was important to retailers was product data. Specifically, sales data (demand) and inventory levels (supply). You don't need data scientists for these well-understood types of data.
But a lot of new kinds of data have been added in the last decade or so, most notably a vast expansion in types of customer data — and huge growth in the importance of that data to the enterprise. Top it all off with the extremely messy realm of sentiment and social media, and all customer data challenges become compounded.
And then there's the notion that sales don't just happen in stores. The relationship between influence and sales has become very messy as new channels and touchpoints are added.
Will retailers recognize and respond to the need for this layer of resource within the enterprise?
Who should drive a retailer's analytics strategy?