There’s no mistaking it, the development of technology is accelerating to hyperspeed. Whereas we used to have more storage power than data, we are about to reach a big tipping point with Big Data. With the four V’s (volume, variety, velocity, and volatility) on a major rise, we will soon have more data than linear technology will be able to handle.

The dilemma? BI and Analytics cannot handle this amount of data.

The solution? Think more like a human.

The world, especially the tech world, likes to look at technology as being superior to humans. With capabilities and a potential that seem far beyond the capabilities of us mere mortals, it’s easy to overlook something that’s been under our nose, or rather our skull, the whole time. We need to look at the capabilities of the human brain and make tech that isn’t better than us, but works how we work, in order to complement our abilities and meet our needs.

Think like a human

First, we need to get technology to think like a human.

We start with creating tech that can process large amounts of data, at the edge, and in milliseconds, mimicking the human mode of processing with the new class of data cognition engines. As the newest category of analytics engines, data cognition engines leverage deep neural networks to mimic human brain operation in order to go deep into data and deliver significant capabilities. Building an algorithm for normal analysis is no longer enough.

I mentioned millisecond processing times already, which may seem impossible when we’re talking about terabytes of data, but trust me, it’s possible. Here at Sisense, we have a customer working with Sisense Hunch™ on a 6 terabyte database that contains 1.9 billion records from IoT sensors. Sisense Hunch replaced this vast dataset with a 2 megabyte (!) neural network and was able to process (the equivalence of) all of that data in 0.1 milliseconds. Previously, the response time on a query for this specific use case was 30-minutes.

This trend in technology can move beyond business, though. By placing these small neural networks at the edge, we are essentially able to turn any device into a supercomputer. Imagine, for example, a sensor on your smartwatch that can carry the equivalence of petabytes of data and analyze patterns in the data right there on the watch, without needing to send it to a centralized database. As you go for a jog, it can effectively monitor your biomarkers in real time and compare them to a huge database of hundreds of millions of other smartwatch users, giving you advice as you work out.

Speak like a human

Next, we need to look at how we can get technology to communicate like humans.

When I started my career, I was coding with really low-level languages, most notably C and Assembly. It was extremely painful—coding with bits and hexa is not easy. Essentially I was forced to learn the way computers ‘think’ in order to best interact with them. But since then, what we’ve seen is a huge change, which will accelerate in 2019, where instead of humans learning machine language, machines will learn and ‘understand’ human language.

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Neural networks support the rise in natural language processing, natural language generation, and natural language querying. Allowing you, for example, to ask questions to your Alexa, and have it look at your data and respond to you in an easy to understand language. What I believe will happen, with this movement in tech, is that the world around us will become much more human and not more alienated by technology. Tech will adjust itself. It will have soft edges.

This coming year will see the rapid rise of tech creators making sure machines understand humans and not that humans have to adjust to machines.

Do good like a human

What’s left? It’s time for us to make sure tech does good like humans.

With more and more millennials permeating the workforce every day, we’re starting to see the rise of the conscientious worker and the conscientious consumer. Millennials are a powerful force with a belief in having a responsibility towards our planet and making a positive impact—not just money. Once organizations become data-driven on top of these morals, they will start to become truly effective in ending disparities across people.

Take, for example, the common knowledge of the pay disparity between genders. Guessing or assuming you reward employees with equal pay for equal work doesn’t cut it. With conscientious employees at the wheel, you can look at the data, see if your organization pays equally no matter the gender, and fix the issue if you don’t. This is a test we ran here at Sisense for the past few years and it is data we continue to look at to make sure we’re not just talking the talk, but actually using data for good. We at Sisense pride ourselves at having achieved pay parity and it is all thanks to data in support of values.

2019: The Rise of the Human

Tech has always served us in order to help humans do things better, not to purely do things better than humans. As we continue developing technologies at a staggering rate, it’s important that we keep reminding ourselves that tech is here to augment, to aid, and to help us interpret the world around us. Analytics technology, with a mix of artificial intelligence and human intelligence, can give us a deeper understanding of our lives but it can’t live them for us.

Here’s to 2019 filled with innovation, huge strides in BI technology, and, of course, humans.

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