Showing posts from November, 2016

Digital Technologies Must Disappear in 2017

Almost a year ago, I wrote these words, "T echnology has reached the tipping point for me, it moved from a help to a hindrance."  The plethora of adrenaline and endorphin inducing mobile apps, 24x7 news, notifications, alerts and updates, drip fed my brain and hindered my  "deep work and deep thoughts."  In Cal Newport's new book titled, "Deep Work" he posits that most knowledge workers need concentration and substantial time, dedicated and uninterrupted, to produce their best work. He argues that a lot of technologies and open office layouts today inhibit creativity, "deep work" and "deep thoughts," and are the very things that are most highly valued, and one of the key differentiators between humans and robots. Newport argues that we must understand and optimize the conditions that enable our brains to work best.  To sum up his argument, constant drip feeding technologies serve to prevent deep thoughts and deep work, our m

The Day Big Data Analytics Died

The Huffington Post gave Donald Trump a 2% chance of winning, The New York Times 15%.   The best polls, prediction markets and analytics predicted a Hillary Clinton victory in the days before the election, yet they were all wrong.   The national media’s predictive analytic systems failed catastrophically.   Why? Analytic systems require timely data on all the variables that impact a system and measure its performance.   Analytics requires support from an optimized information logistics system (OILS), which describes the a system that manages the full lifecycle of data from collection, transmission, processing, analysis, reporting, data driven decision-making, action and archiving.   An OILS is only as good as the data.  It can only function correctly if it is collecting the necessary data inputs.   For example the sensors in an Internet of Things (IoT) system must be attached to the right “things” that impact operations, to provide full system visibility and insight. The pre-ele

Merging Humans with Enterprise AI and Machine Learning Systems

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them.  Often this means massive amounts of learning can be accomplished every second without stop 24x7x365.  Code doesn't need to take weekends off, holidays, or sick time. Code doesn't get tired. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time).  Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time. I believe the first step is to admit we have a problem - the problem is a difference in the speed that computers can operate and the speeds us humans can operate.  The second is to understand what a solution might look like - how humans and computers can best integrate and oper