5 Reasons the Future of Big Data Requires Human-Machine Cooperation
SEP 28, 2017 00:21 AM
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5 Reasons the Future of Big Data Requires Human-Machine Cooperation

by Larry Alton
Collecting, analyzing, and interpreting data is becoming essential for more businesses and more individuals than ever before. Now that we have the automated tools to process this data, we can make better decisions—and more cost-efficiently as well. As more companies employ these tactics, competition rises, and it becomes even more imperative to take advantage of this efficiency. However, the real future of data management doesn’t solely lie with machines—instead, it lies with human-machine interfaces and cooperation.
Why Humans Are Still Necessary (and Why We Need Machines)
We already have machines that can beat human beings in games of pure logic—even ridiculously complex ones like Go—so, assuming machine learning algorithms get even better over the next several years, why would humans even be necessary in the collection and interpretation of big data? And if humans are somehow better at making these decisions, why bother creating the machines?
1. Machines need direction. Machines—or at least those we can foresee—are highly skilled at answering questions, and terrible at generating the questions that need to be asked. Big data highlights this problem perfectly; imagine you have quadrillions of data points, collected from millions of people. In all likelihood, if you knew the right questions to ask, and had a machine to pick through the data, you could easily find the answer you seek. But machines don’t see patterns or meaning in data; they can only fetch it, or combine it in ways instructed by humans. Accordingly, humans remain a necessary part of the equation.
2. Not all things are easily quantifiable. You should also realize that not all decisions are easily quantifiable. In some scenarios, you’ll be presented with two options, one of which is inherently more cost-efficient, with no real downsides. But in others, the decision is not so clear. Take project portfolio management as an example; you can’t use a single criterion, or even an unchanging aggregation of criteria, to prioritize one project over another. That’s why it’s helpful for machines to quantify and project what they can, but it’s still necessary for humans to make the final call.
3. Human biases. Humans alone aren’t great at decision-making. When faced with objective values and data, we can’t help but distort that information based on our own persistent cognitive biases. For example, if we plumb the data with an assumption already in mind—even if it’s only subtle, and in the background—we’ll end up finding and prioritizing any data that reinforces those assumptions. Machines can’t do this, because they won’t extend beyond the logical parameters set for them.
4. Processing limitations. AI has yet to exceed the general abilities of the human brain, but in specific applications, it can’t be beat. Anything requiring mathematical calculations can be done faster by a machine than with a human attempting a manual approach. However, machines have limits as well; humans see complex sets of data and automatically filter out what’s unnecessary, instinctively honing in on high-level patterns. In machines, those patterns have to be taught or discovered from the ground up, or else, they’ll brute-force the calculations one at a time until they arrive at a conclusion; this is why Go was much harder for computer programs to master than chess. With both humans and machines having processing limitations, they need each other to keep advancing.
5. Long-term flexibility. Great thinkers have long speculated about the power of a machine-human interface, and some (like Elon Musk) are working hard to make it a reality. We don’t need to have machines embedded in our brains, but working together with human-machine interfaces gives us far more flexibility in future developments than abandoning tech or prioritizing tech usage exclusively.
The Bottom Line
Big data has been an enormous technological step forward for humanity—especially in the tech sector—but if we’re going to use it efficiently, we need to think about how we can apply and interpret it with both machines and human beings. Only together will we have both the processing power necessary to make big data cost-efficient, and the ingenuity to avoid the limits of pure machine processing. 
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