Types of data (based off of Laursen & Thorlund, 2010)
- Real-time data: data that reveals events that are happening immediately, like a chat rooms, radar data, dropwindsonde data
- Lag information: information that explains events that have recently just happened, like satellite data, weather balloon data
- Lead information: information that helps predict events into the future based off of lag data, like regression data, forecasting model output, GPS ETA times
Everyone can easily find lag data, its old news, but what is interesting is to develop lead information from real-time data. That has the biggest impact to any business trying to gain a competitive edge against its competitors. When a company can use data mining and analytical formulas on real-time data they have a head start into generating lead information (Ahlemeyer-Stubbe & Coleman, 2014), which would allow for a company to make data-driven decisions much faster. To do so, one needs to fully automate the modeling towards a predictive target, in an efficient manner (which is of particular importance when dealing with Big Data). An example of zero latency (real-time) data analysis is seen through the production line on any manufacturing plant (i.e. Toyota, Tesla, etc.), data is stored in an enterprise resource planning (ERP) system (Carter, Keith, Farmer, & Siegel, 2014). Speed is vital, thus zero-latency means a manufacturing plant can meet its demands without incurring additional costs, and therefore keeping their profit margins up and their manufacturing programs in the black. Carter et al. (2014) claim that General Electric could extract $150 billion of unrealized efficiencies just by analyzing their data. They could get to that much faster if they drove down their latency to zero. But, there is a caveat, the data must be not only real-time but accurate (Carter et al., 2014).
Item affinity (market basket analysis) uses rules-based analytics to understand what items frequently co-occur during transactions (Snowplow Analytics, 2016). Item affinity is similar to the Amazon.com current method to drive more sales through getting their customers to consume more. But, to successfully implement the market basket analysis, the company like Amazon.com must implement real-time (zero-latency) analysis, to find those co-occurrence items and make suggestions to the consumer. As the consumer adds more and more items into their shopping cart, Amazon in real-time begins to apply probabilistic mining (item affinity analysis) to find out what other items they would like to purchase in conjunction with their primary purchase (Pophal, 2014). For instance, buyers of a $40 swimsuit also bought this suntan lotion and beach towel. Item affinity analysis doesn’t only impact the online shopping experience but also impacts shopping catalog placements, email marketing, and store layout (Snowplow Analytics, 2016).
- Ahlemeyer-Stubbe, Andrea, Shirley Coleman. (2014). A Practical Guide to Data Mining for Business and Industry, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781118981863/
- Carter, Keith B., Farmer, D., & Siegel, C. (2014-08-25). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781118920657/
- Laursen, G. H. N., and Thorlund, J. (2010) Business Analytics for Mangers: Taking Business Intelligence Beyond Reporting. Wiley & SAS Business Institute.
- Pophal, L. (2014). The technology of contextualized content: What’s next on the horizon? EContent, 37(7), 16. Retrieved from http://www.econtentmag.com/Articles/Editorial/Feature/The-Technology-of-Contextualized-Content-Whats-Next-on-the-Horizon-99029.htm
- Snowplow Analytics (2016). Market basket analysis: identifying products and content that go well together. Retrieved from http://snowplowanalytics.com/analytics/recipes/catalog-analytics/market-basket-analysis-identifying-products-that-sell-well-together.html