Big Data Without Big Headaches
While "big data" is certainly a popular topic, retailers of all sizes struggle to understand exactly what big data is and how to use it to improve their bottom line. Many assume big data is a black box that churns out valuable insights that are then massaged into charts and graphs. Not so. Big data is a game changer that allows retailers to harness all accessible information on customer shopping behaviors and turn it into insight. However, in discussions with retailers, I've learned that many don't know how to get to these insights. This article won't solve your big data problems, but it will provide insight into how exactly merchants can look to big data for help.
The commonly held belief is that merchants need experts in order to analyze big data. As this McKinsey & Co. study shows, however, there's a shortage of qualified data scientists to do that. Contrary to popular belief, retailers don't need to hire $200,000 per year data scientists or invest in complicated data architectures and tools to manage their data. Companies can steer around the data scientist bottleneck by using readily available software tools, such as Wise.io and BigML.
Tools like these make big data accessible to non-IT functions, including merchandisers and marketers. Data scientists will play an important role in developing and ensuring the integrity of these abstractions, but for most retailers the use of purpose-built data refinery solutions takes away much of the technical complexity so merchandisers can easily wield the power of big data insight.
However, many retailers don't have machine learning and data science at the core of their company's competence. Think about the three specific areas served by the data scientist and you'll see what I mean. Data scientists are experts in data architecture, machine learning and analytics, and most retailers don't need a highly specialized data team of the sort you'd find at Amazon.com or eBay. For many, the solution to the data scientist bottleneck will be found by the commercial design and deployment of data refineries that abstract away as much of the technical complexity as possible.