Do you remember what it was like the first time you got your hands on an iPhone? When you realized that all the things that you used to have to do on separate devices now could be accomplished on one single device? Well, the minds behind LogicBlox would like you to feel the same way about its foundational technology that collapses multiple technology stacks into a unified smart database environment that aims to enable enterprises to create sophisticated and easily iterated applications in one place.
Transaction and analytics co-exist in the platform, with the system utilizing a single declarative language with extensions – such as Machine Learning capabilities and statistical relational models – to support prescriptive and predictive analytics. Users can leverage LogicBlox’ full-blown database functionality to train Machine Learning models, for use in solving forecasting or optimization problems, for instance. It all runs in the Cloud to take advantage of unlimited computing power on demand at low cost, handy for the spikes that can come with big Machine Learning runs.
LogicBlox was in development starting back in the mid-2000s, says Rafael Gonzalez Caloni, the company’s CAO and EVP of Marketing, and long before the Cloud was an accepted enterprise platform – back when Big Data was not quite even a whisper. The idea was to do “paradigm-changing stuff,” he says, not only by leveraging the Cloud, but also by turning on its head the concept of building apps that were glued together conglomerations of multiple technology components, drawing from multiple databases and layers, statistical or optimization tools, and programming languages and skill sets. It could take years to build sophisticated systems that were then hard to change because of fears that their very brittleness would break them. “That stifles innovation in many respects,” he says.
Given its plans to be game-changing, though, the company needed to prove its point. That led to the birth of Predictix, a separate company but jointly owned with LogicBlox. The goal was to pick a vertical that relied on large scale sophisticated applications to drive a lot of value, and the decision was to start with retail. “It’s a fantastic industry to prove we can do something very different with this underlying technology,” Gonzalez Caloni says. Using the LogicBlox platform, Predictix bet on bringing applications to market that help retailers handle challenges like forecasting demand for products, particularly when it comes to promotions and particularly when it’s something they’ve never promoted before, and handling assortment planning across potentially thousands of stores carrying hundreds of thousands of products.
Learning from All the Data
“When dealing in huge companies like [some retailers], the idea that people are going to get their heads around every intricacy is not realistic,” he notes. Compared to traditional methods, Predictix has been able to deliver a 25 to 50% higher accuracy with its Machine Learning approach and forecasting app built on its smart database. Traditional methods include, for instance, time-series based forecasting, which basically is a way of gleaning trends based on looking at historical data.
That’s fine for simple ongoing product forecasts – for instance, stores can easily gauge from their sales history that they sell more bottled water in summer than in winter. But the situation changes when there’s a new product to be promoted and no history to hark back to. You need to be able to extrapolate a promotional forecast from a lot of different data points – not about the particular product that the retailer has never promoted in this or any other way before, but about that product from data on thousands of other promotions of other products in stores across regions and seasons, brought to buyers’ attention via different methods from emails to in-store flyers.
“Our approach says let’s see all that data,” he says.
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