Fuzzy Logix: IoT retail data is gonna be (h)yuge

Fuzzy Logix: IoT retail data is gonna be (h)yuge

Not just a drop in the ocean. The data surge created by the retail industry's use of IoT technologies is a torrent that needs a new approach to data management. Image Source: Fuzzy Logix

The Internet of Things (IoT) and the retail industry are going through an emotionally torrid relationship right now. In and out of counselling and marriage guidance, one day… soon, we might get to a point where they can both cohabit in the same home. Sorry, we mean the same shop, store, marketplace and mall, right?

Problems & possibilities

It’s no trumped up suggestion to say that nobody knows how (h)yuge the data is in the Internet of Things (IoT) better than the retail industry. Indeed, we might go further and say that today, nobody has more respect for the IoT’s (h)yuge data than the retail industry.

From store premises now being built with cameras, sensors, movement tracking devices and customer alert systems to generate special offers bases upon defined behaviors… downward and through the factories and warehouses in the retail supply chain. The use of IoT data in the retail industry is spiralling massively.

As Internet of Business writer Freddie Roberts has already said, IoT in retail creates as many problems as does possibilities.

Roberts asks, “One of the most significant challenges, notably [is] how retailers cope with direct interaction with the customer, which some have never experienced before, and how can they make that interaction relevant?”

Will intelligent use of algorithms and new smart devices be enough to  get us out of this pickle, or do we need to look deeper still? What it (arguably) comes down to at the foundational IT architecture level is not a question of the devices or algorithms on their own; our success in this space will also be governed by our approach to data itself.

Fuzzy Logix

Looking to try and get us thinking ‘IoT and data’ before we think ‘IoT and devices’ is software analytics firm Fuzzy Logix. The company is now putting its analytics suite DB Lytix on the Cloudera Enterprise 5 data platform.

What this means (in the most simple terms) is predictive advanced statistical analytics running natively on a cloud cluster. That ‘native’ part is important because it means software running in its most native state in terms of its ability to execute, compute and produce results.

The firm claims that DB Lytix can empower large retailers to optimize a supply chain across thousands of stores in multiple countries, with tens of thousands of products, taking into account factors such as local weather patterns and differences in demographics at each of its stores.

According to Fuzzy Logix, “The huge data volumes generated by Internet of Things (IoT) devices can be effectively handled for large-scale applications in transportation, health, manufacturing, agriculture and utility industries.”

Big data analytics problems are growing exponentially in scale and complexity. These problems far outstrip the capabilities of legacy hardware platforms and traditional analytics tools. In answer then, Fuzzy Logix claims that its analytics solutions combine the advanced analytics software with the latest data storage platforms which can store massive amounts of data.

The 4 pillars of -ized : optimized, parallelized, standardized, native-ized

Data solutions in the IoT space should then be optimized, parallelized, standardized, native-ized scalable and fast with analytics that runs where the data resides.

These elements should (arguably) start to surface as prevalent themes in the most efficient deployment of IoT data-centric software that we see today. Fuzzy Logix is by no means the only player reflecting these trends… so expect more -ized pillars to surface.


I am a technology journalist with over two decades of press experience. Primarily I work as a news analysis writer dedicated to a software application development ‘beat’; but, in a fluid media world, I am also an analyst, technology evangelist and content consultant.

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