DISCUSSION
Do retail marketers have an appetite for data science?
Written by RetailWire Staff
Due to a lack of skills, marketers spend a good portion of their budgets outsourcing analytics, according to a recent Gartner report.
The report states, “A notable share of the analytics budget — more than technology and nearly as much as internal talent — goes to outside experts. The majority of mature data-driven marketers expect external sourcing to grow over the next two years, and 30 percent of them expect to decrease their internal team size, taking more advantage of the efficiency, scale and expertise of service providers.”
This is surprising. Many of these same companies are striving to become data driven and building analytics into their corporate DNA. According to recent Mckinsey research, the top ten U.S. retailers now have an average of almost 70 big data analysts on board.
Consider too that companies realizing business value from Big Data are typically not buying off the shelf services and pre-defined models. They are doing the work themselves to create competitive differentiation.
Somewhat astonishingly, Gartner finds “concerns such as ongoing costs, data security, black-box models and lack of transparency, and loss of competitive advantage are all valid reasons organizations may choose not to use external service providers. However, for most marketers, these considerations will not deter investment.”
There seems to be a disconnect.
Either marketing doesn’t believe their data scientists have the right skills or marketing isn’t aware of the talent available because data scientists are consumed with non-strategic aspects of the job.
Another possibility is that marketing doesn’t believe that their data scientists are able to execute projects in a timely way. This could be because of inefficiencies in the analytics process itself.
“Smartsourcing” is a good way to think about resolving the disconnect:
- Acquire the analytical talent on-demand to support a use case your team cannot handle, and/or;
- Acquire a Big Data cloud capability to account for portions of the analytics process you lack or which takes too much time.