7 Things Leading Enterprises Realize About The Data Tipping Point (That Might Help You)
Way way back in 2000, Malcolm Gladwell defined the tipping point as a moment of critical mass when ideas and products and messages and behaviors suddenly spread like viruses. In his case Hush Puppies and Crime rates.

Only 16 short years later, the Internet Of All Things (IOaT) and Cloud have already gone way beyond their own tipping points.

We’re now all decorated with sensors and devices on our bodies, in our homes and businesses. The amount of ’data-in-motion’ from anything that can emit data is predicted to rise from 4 to 44 zettabytes in this decade alone. Everyone writes about the tsunami of things but I predict the impact of Things and Cloud will be nothing compared to the tipping point for data. Especially the impact on the technology architectures of large enterprises who provide the services and products behind it all.

In my position, I work with the world’s leading users of data, helping them do the right things as they re-platform their data layers and architectures.

Here’s what they know about the tipping point for data that might help you.

They Design With Real-Time in Mind

We used to think that a megabyte was a lot of data to analyze. Now its not unusual to think in terms of a terabyte or even a petabyte of raw data coming from an infinite variety of self configured sources, boiled back down to megabytes or even a few kilobytes of intelligent analysis. It’s a kind of reverse funnel.

While this growth of volume of data is important, the hardest problem hidden here for data scientists is the variety and complexity of new data types, then how to capture it and realize it’s analytic value in real-time.

So volume, variety and complexity in real-time from an infinite variety from things such as self configured devices, infotainment, wearables, cybersecurity, sensors, vehicles and machines create the data tipping point for enterprises. It’s this that is accelerating changes in the IT architectures of big business and government.

What this means is that top data users now design their architecture around how to create data schemas in real time, and to be able to use that data itself as the roadmap to analytics that can differentiate their decisions and build the requirements and reports to run the business.
['Scott Gnau']
['Big Data', 'Data Science', 'Technology']