With all of the real-time analytics, visualizations and analytical systems deployed from the cloud, the world makes you feel like you’ve got all the access to information you need. You are truly a citizen of Digital Economy, aren’t you? So why do the answers to any of my new questions come back AFTER I already forgot what I asked in the first place?
How long did it take to set up a new analytics environment last time your Enterprise did it? How much did it cost? Does it influence your will to do it again? Don’t blame the data scientists!
Imagine you’re building a new IoT device and you have to manufacture every single part of the hardware on your own, all of its electronics, the screws, plastic cover and even the wiring. That’s what the job of a Data Scientist looks like today. They have to pull the data from different sources, then clean/transform it, then mash it into a sensible data model and after that work is done, they can do the data science magic. Pretty ineffective, isn’t it?
The undisputed bottleneck today is collecting and organizing the data. This is what can turn your enterprise analytics setup into something between an annoying delay and an aggravating nightmare.
But there’s hope! Imagine you have the ability to set up a data warehouse that will pull the data from tens of different sources and organize the data into a hub for statistical and other best-in class analytical tools – but in this case also enabling non-technical business users. Imagine it can take three days or less to set up the infrastructure and that extracting the insights can be reduced to hours or minutes. Imagine it works on billions of records and eliminates the performance challenges, even when running on a laptop. Think of how this could accelerate the business cycle.
There is a reason why Gartner split the market into Reporting vs Analytics. Turning the crank to get the same report you had in the past is easy. But we live in time when businesses have to evolve faster than ever and that evolution is based on information. This is critical for use-cases such as fraud detection or anti-money laundering, as well as many others.
How’s this possible? Have a look at this video. And if you don’t believe that it’s possible to blend and model data from many silos in days, then let us prove it to you in a one week POC!