The Future of Big Data and Retail with Case Studies

This article is the sixth and final in an editorial series with a goal of directing line of business leaders in conjunction with enterprise technologists with a focus on opportunities for retailers and how Dell can help them get started. The guide also will serve as a resource for retailers that are farther along the big data path and have more  advanced technology requirements.

In the last article, we highlighted the various big data solutions available for the retail industry. The complete insideBIGDATA Guide to Retail is available for download from the insideBIGDATA White Paper Library.

The Future of Big Data and Retail with Case Studies

The future of big data and the retail industry is very promising with technology taking a strategic lead for maximizing competitive advantage. Let’s consider a couple of chief inflection points that the future might hold. First, the Internet of Things (IoT) will play an important role in terms of how  sensor data is writing the next chapter in the retail & big data story. As one example, say you’re walking down the aisle at a retail store and an  embedded sensor detects that you have the retailer’s app installed on your cell phone and it offers you the immediate gratification of a coupon.

Next, consider the increasing importance of realtime analytics through use of Spark streaming. This is new technology and still a bit cumbersome to deploy so it’s not being as widely adopted as many might think but it is coming on strong. Real-time analytics represents a tremendous opportunity for retailers who are building their business and retaining their customers.

Case Studies: Dell Focused Customer Use Cases

Some early Dell customers started using big data technology solutions back in 2011 when they came looking for recommendation engine solutions, e.g. the kind of system pioneered by Amazon. The recommendation engine was a seamless way for retailers to start seeing the benefits of big data. Once they saw the value and the ease of Hadoop once the platform had been implemented, the use of the big data technology stack grew from there.

Case Study: Large Retailer

One example is a large retailer who came to Dell for assistance with a recommendation engine project. They collaborated closely on their proof of  concept with the Dell Big Data Specialists in the Dell Solution Center, and proceeded to build an 8 node cluster that grew to over 300 nodes in a 3 year period. Once they got their feet wet and saw the results, they moved on to other solutions like an ETL offload mainframe replacement. The company’s next steps with Hadoop were:

• Taking all their data sources and started to use them for their analytics
• Supply chain analysis
• Central data repository
• Price setting
• Logistics planning

Case Study: Staples

Fortune 500 office and school supplies retailer Staples increased its brand recognition and boosted staff efficiency by improving social media listening and analysis, and reducing “noise” (i.e. irrelevant data) by 75 percent. Staples needed to reduce the noise it collected from social media channels to understand customers’ likes and dislikes more quickly, and improve its offerings. As more people engage on social media channels such as Twitter, Facebook, LinkedIn and others, Staples realized that it needed a better solution to sift through the increasing volume of public data to find tactical
information.

The company engaged Dell Services to deploy and manage a cloud-based social media listening and analysis service centered on big data technology. The solution served to pinpoint relevant unstructured data from social media insights faster, increase customer communication, amplify customers’ voice in day-to-day decisions, and improve corporate agility and offerings.

The results of the project were widespread. Marketing managers could quickly gauge whether media campaigns were effective and worth the investment. The website team could learn if customers were able to easily find the products and information they were looking for on Staples.com.  Plus, executives would be able to easily see which store policies and processes are working, and which ones need improvement.

Case Study: MetaScale

MetaScale provides Hadoop big data solutions, training and support—partnering with Dell—to help clients speed processing, improve decision support and realize more cost-effective ways to derive intelligence from enormous data stores while radically reducing costs.

MetaScale’s first client was one of North America’s largest retail groups, with many thousands of stores and tens of billions of dollars in annual sales. Its daily transactional and operational data volume is several terabytes, generated by millions of shoppers as well as many supply chains. In all, its current data volume exceeds 3 petabytes. The high level goal of the project was avoid the situation where its data management, analysis and reporting capabilities were quickly falling behind the pace of growth of its data.

As one example of where the big data project succeeded, the client company’s pricing business unit needed daily summary reports using data from multiple platforms to measure the effectiveness of its pricing in stores. It was generating these reports from its data warehouse and predictive  analytics tools, both hosted on mainframes. But the mainframes’ batch processing required for these reports was taking 10 to 15 hours, so the  company could manage only weekly data warehouse loads. As a result, the pricing business unit wasn’t getting the decision support needed to  fine-tune in-store pricing.

MetaScale’s approach to solve big data problems for its client was to deploy a Hadoop solution powered by MetaScale big data appliances based on Dell PowerEdge servers. MetaScale worked closely with Dell to develop its bundled Hadoop solutions to meet its client’s growing needs for performance, scalability and support. MetaScale’s big data appliances arrange the server nodes into logical clusters for handling large-scale data sets cost-effectively. The retail client’s Hadoop solution had a cluster with more than 500 server nodes, not counting its backup cluster.

In this particular client’s Hadoop implementation, MetaScale helped its client achieve an ROI in just three months after becoming operational with a cluster that, at the time, harnessed 50 nodes of Dell servers to manage its growing data.

If you prefer, the complete insideBIGDATA Guide to Retail is available for download in PDF from the insideBIGDATA White Paper Library, courtesy of Dell and Intel.