Analytics Revolution and Democratization of Data

Transformation. Disruption.  George Washington and Thomas Jefferson ushered in the ‘self-evident truths’ of how a country could be run by the people.  James Watt sparked the Industrial Revolution transforming manufacturing with his invention of the steam engine.  The world was never the same after these events, and neither will it be following the analytics revolution and the explosion of applied data we are experiencing now.  It is the self-evident truths of our time.

Data Democratization – Power of the People

This revolution is not a battle for freedom, rather a naturally evolving freedom and openness to data. This democracy is the participation of many in the process of collecting, contributing and collaborating with data.  Take Healthcare for example.  A little more than a decade ago, 9 out of 10 doctors kept patient’s records in handwritten notes.  Today 90% of providers use electronic medical records (EMR).  Further through Patient Portals, you can view and update your own data online.  Health Analytics, a field that didn’t exist a decade ago, is consuming tons of data (33 zettabytes by 2020) contributing to better managed care costs, and improved patient outcomes.  And yes of course, Amazon Web Services and its focus on healthcare has a solution for that too.

The revolution also enables people to have access to tons of data, and increasingly inexpensive [dare I say free] abilities to use it.  Previously this was reserved for deep-pocketed-tech companies.  Making data tools available and nearly free allows A LOT OF CREATIVITY for how people can use it.  Even silly ideas can be explored, experimented (yes, even analyzed) when costs are so few.  Such as a hedge fund manager exploring optimal hockey ‘empty net’ scenarios, or teaching a machine learning ‘bot visual recognition to Find Waldo.

Freedom of Data

Data from nearly everything, from nearly everyone.  Connected devices from Mack trucks, my coffee maker, and cute teddy bears are tethered to the internet and creating a flood of data.  Connected cars are expected to upload 25 gigabytes of data each hour from dozens of sensors tracking everything from engine performance to road conditions.  Each day if you are one of 130 million who own a smart watch or Fitbit, you are contributing data nearly every second about your fitness activities, location and heart rate – great when you are data motivated and working on your new year’s resolution.  With all this data, Fitbit and Amazon’s Alexa are even exploring how to predict illness, perhaps even before you start felling sick.  Democratization of data really is becoming a collaborative effort with retailers, service providers, online devices (IoT) and consumers all playing a role.

This Time is Different

The industrial revolution from 1760 to 1820 took 60 years to transform industries.  Creating new ones and eliminating others.  The period gave workers, companies and whole industries time to transition.  The difference with the analytics revolution is how incredibly fast it is occurring to the point it is often called disruptive.  Disruptive because of the vast amount of changes it is having on industries.  Amazon’s disruption of retail and the collapse of Sears is the most iconic example of rapid change in an industry.  Technology expert and author, Kai-Fu Lee recently noted, AI can eliminate 40% of repetitive jobs in the next 5 to 15 years. For a simple example think about how Amazon Go is eliminating checkouts in stores.

The analytics revolution is here and now and it is fueled by an unprecedented amount of data collection, faster and cheaper computing power, new low-cost open source software giving rebirth to old ideas like neural networks, and a desire by a new group of people called ‘data scientists’.  Not since the industrial revolution has a genre of technology had such broad application to nearly every business and service in our society.  Every business and organization from your church, the factory, our hospitals and retail have a need for data scientists, to collect and consume data in a useful and strategic way, and to transform themselves into a data-driven technology company.  Failing to do so would be like trying to compete with James Watt’s steam-powered textile factories of the 1800s while holding just a needle and thread.  You get the point.

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