The True Chasm Yet to be Crossed for Big Data

Every vendor, customer, and channel player has a common problem with the big data market today. They don’t know how to build a complete solution. There are literally hundreds of companies with products and services being positioned as “big data platforms.” I know this is true, I have a list of each.

At the top level, an elevator pitch of value, the sales and marketing stories all sound the same. Exactly the same promises are made: transform your business, be data-driven, understand your customer more deeply, run operations better, faster, and cheaper than ever before. “All well and good,” you say, “admirable and desirable goals, but how do I do this?”

That’s when the floor drops out of the elevator and you are plummeted to the basement. The vendors suddenly start telling you about their subtly nuanced variations on implementation of an open-source storage system in algorhythmic parsing streaming columnar indexed batches of machine learned location independent masked triples in-memory via a variety of farm-animal themed programming languages or API features.

^^This part really should make no sense to anyone. I just made it up to make the point that there is a huge disconnect between the business rhetoric and the technical details. This chasm threatens to swallow us all.

Marketeers and salesfolk needs to explain in more clearly defined fashion what it is they do and why it’s a good choice. And prospective customers need to have a sharper focused lens on what it is they are actually trying to do.

The Big Data Techcon held recently in Cambridge, Mass. was a perfect illustration of these disconnect issues in the industry. The exhibit hall had lovely, helpful people from influential big boys like HP Vertica, Informatica, HDS, Intel, and SAP, and many hot entrants like NuoDB, Pentaho, Dataguise, Basho and Aerospike, all telling me how big data will change the world. The tutorials had brilliant engineers showing samples of code and how to script with the correct parameters and syntax. There was absolutely no middle ground.

ESG is developing a framework on how to talk effectively about big data products, model their interelationships, and dare I say, cross the chasm to construct actual solutions. More on this very soon….

Topics: Data Platforms, Analytics, & AI