Retailers Challenged by Coming Data Scientist Skill Gap

Big data-driven technology will continue to transform business processes in 2016 and beyond. For web-only and e-commerce marketing and management teams, the growing challenge is how to properly leverage all of the available data. The sheer volume of information can be overwhelming and creates a bit of a quandary where the data can improve efficiency and profits, but the right staff and technology need to be in place in order to extract meaningful observations.

A key for online retailers to gain value from data is to employ data scientists — i.e., experts in the field who understand how to turn raw information into actionable intelligence. Data scientist is a very attractive and in-demand profession, but the explosion of available data has created a skill gap. A McKinsey study estimates a 50 percent to 60 percent gap between supply and demand for analytic talent by 2018, underscoring the need for academic and industry focus on training data scientists.

According to employer review site Glassdoor, data scientist was named as one of the best jobs in 2015 due to attractive compensation and the growing skills gap. Retailers that want to stay ahead in their competitive industry will heavily recruit the available pool of data scientists and need to be willing to pay for top talent.

Educational institutions are quickly creating data science programs, with MIT building an Institute for Data, Systems and Society that's addressing societal concerns while training leading-edge data scientists for the corporate world.

Why are data scientists so vital to online retailers? Consider some real-world examples:

• An electronics web-only retailer notices a decline in sales of certain products over time. A data scientist could correlate the decline with consumers desire for more eco-friendly products, allowing the company to reverse the trend by sourcing more ecologically sensitive products.
• Data scientists are using their talents to create machine learning tools to spot fraudulent transactions based on several actions and parameters.
• Modeling and forecasting are more refined and accurate when conducted by data scientists, who can take patterns from different data sets and combine them into deeper insights for marketing and sales.

The volume of information continues to grow, with big data and IoT driving an information revolution. Retailers need to manage their data science efforts in order to remain competitive, enter new markets and generate more return on investment from marketing campaigns. In order to overcome the data scientist skills gap, retailers should follow several best practices and consider their options:

Search for the most qualified data scientists. Working with large amounts of data requires an art-meets-science approach, where the data scientist must understand technically how to crunch numbers and use math, but also needs to see the nuance and context that goes into analysis. An e-commerce-focused data scientist needs insight into customer behaviors and processes, as well as a deep understanding of the particular industry. Their recommendations should focus not just on generating sales and revenue, but also on improving the customer experience and internal processes when possible.

Retrain current team members to handle data science tasks. Onboarding new staff members (and paying a premium for in-demand data scientists) is a time-consuming and costly process. Online retailers should instead consider training current technically savvy employees on big data platforms so they can analyze data more effectively and make strategic decisions. These staff members should already be involved in data manipulation and have an eye for spotting trends as well as an intuitive sense of the company's target markets and customers.

Outsource the work. The massive amounts of big data can be hard to manage with traditional business intelligence tools and teams. Many retailers are turning to third-party firms to collect and manage data from multiple interconnected sources and turn it into digestible formats. Bringing in external experts is a fast and cost-effective way for retailers to stay ahead of the latest trends and spot ebbs and flows in customer demand.

Introduce automation and algorithms. The role of data scientist shouldn't involve manual coding of every bit of content and customer data. Instead, data scientists need to develop algorithms which automate the data collection and analyzation processes. This “predictive coding” gives data scientists more time to focus on the “art” of their jobs so they can suggest new strategies or target markets.

Automation does most of the “middle” of the process, where humans are needed to set the initial parameters and metrics and then find the hidden correlations. Furthermore, automation helps address the data scientist gap as it allows companies to rely on just a few scientists instead of an entire department of them.

The ability for retailers to analyze massive data sets is becoming a competitive differentiator. The most adept firms will overcome the data scientist skill gap and locate opportunities for growth and innovation in 2016 and beyond.

Anil Kaul is the CEO and co-founder of Absolutdata, a research and analytics consulting firm.