At this time of the year, my inbox gets pretty boring. Aside from the usual gush of press releases and announcements that hit my email each day, from about mid-November till the week before Christmas, I get treated to a special type of press release – the ’2016 predictions’ release.
To be honest, I rarely ready them as they are almost always self-serving, rarely reflect on the previous year’s predictions and are filled with whatever entries are most likely to win this Christmas’ game of buzzword bingo.
Instead of rehashing a bunch of predictions from others I’ve been giving this matter a lot of thought. And there’s one thing that really stands out to me.
The last year has highlighted to me that we have made a massive transition over the last couple of years.
Back when I completed my IT degree, one of the first things we learned about was the relationship between data, information, knowledge and wisdom. Essentially, data was the firehose, information was a distillation of the data. Knowledge was about application and wisdom was about insight, understanding and being able to look ahead.
That’s a bit of a simplification and, in some ways, it’s a flawed model. But it’s good enough for thinking about how we can use the massive volumes of data we generate.
What I’ve noticed over the last couple of years – and this comes from talking to enterprise architects, data scientists, security experts and a bunch of other Really Smart People is that we have slipped back.
We once called the time we lived in the Information Age, but I think we’ve regressed. I think we’re in the Data Age. Sure, there are pockets of the world where we’ve moved ahead and most people have access to a lot of data. But we lack the tools to use it in an actionable way.
We’ve become data rich but insight poor.
Here’s a personal example. I like to exercise. My primary form of exercise is running although I throw some other activity into my week like boxing, pilates, weight training or tennis.
I’ve been collecting run data for the last three years. For every run I’ve taken I have access to my pace per kilometre, where I ran, including the inclines, declines and terrain, my heart rate as it changed over the course of a run, the time of day I ran and the weather.
For about a third of that time, I can tell you what I ate during the day with a breakdown of calories, fats, carbohydrates, protein and other major food components.
I can tell you how long I slept, whether I was restless and how many times I got out of bed.
Now, with all data, I’d like to answer one simple question.
What’s the difference between a good run day and a bad one?
How is it that I can run a sub 26:00 five-kilometre one day but struggle to beat 30:00 on another?
For large companies, with access to analytics and market information – the same sorts of problems exist. If you’ve visited a shopping centre lately, you’d see that Dick Smith Electronics is selling a lot of its stock off at ludicrously low prices because its warehouses were overstocked.
I recently read that the company that bought Australian clothing retailer Rivers was landed with a warehouse full of product it couldn’t sell.
How does this happen?
If there’s one technological trend that I think is a dead certainty for significant change over 2016 and beyond, it’s machine learning and artificial intelligence.
It’s clear that humans are not equipped to deal with the massive volume of data we are flooded with. We need tools to help us sift through the data and deliver outputs that we can use.
However, once systems can work out what is needed and what we need to do, they will be able to automate the response.
Already, there is software on the market that can detect anomalous behaviour on a network, shut down the activity, notify the right people and fix the damage. The biggest limitation is that the software can only detect what it’s told to look for.
But there’s enough research going on where that step will be automated as well.
Tesla released a software update recently that added a number of autonomous actions to the eponymously named car, so that it can self-park and change lanes. And Google has driverless cars already zipping along the roads around its Silicon Valley campus.
In other words, we’re going to start using computers for carrying out more and more jobs for us. Not just ‘paper’ tasks but actions that require some sort of physical activity and interaction with the world.
Get ready for the Age of Computer Autonomy. And it will reach into almost everything we do – either directly or indirectly.
I think someone called this Skynet.