Data and Analytics: Understanding the Human Element
In my last post, I compared today’s Internet of Things (IoT) to the web circa 1998. The promise to make better decisions is massive, even if relatively few organizations and industries have taken the plunge.
One of the biggest impediments to mainstream adoption of the IoT remains the lack of universal protocols and standards. Few people want to come home from work and spend four hours trying to configure smart devices. Make no mistake, though. That’s hardly the only formidable obstacle. Security remains a major concern. Case in point: Last October, news of a widespread hack proved what many industry experts and IoT skeptics have feared for years: Despite its enormous promise, security threats loom large.
The Thorny Human Element
Much of the ultimate success of the IoT hinges on what we do with data. Remember that “data” and “analytics” typically don’t act on their own volition. (Insert Terminator reference.) We do. At least today, algorithms that power search engines and high-frequency trading remain the exceptions that prove the rule. It’s helpful here to think some fundamental human questions:
- Do we know our limitations?
- Are we really willing to go where the data takes us?
- Or will we fall victim to confirmation bias? Will we simply dismiss new data that doesn’t conform to our world views?
For instance, even the most skilled and knowledgeable doctor cannot possibly read—let alone understand and interpret—every cancer study. The corpus is massive, complex, and constantly evolving. Yet, there’s evidence that artificial-intelligence engines such as Watson can help medical professionals make better decisions. As Robert Hackett writes:
No human could possibly read the entirety of medical literature, personal health records, and case file histories that might inform a doctor’s professional opinion when trying to save a cancer patient’s life. But a machine can.
I’m no doctor, but I can see how someone who spent a decade studying her craft incurring massive debt might have a hard time taking advice from machines. This is the same tension that Billy Beane experienced as the general manager of the Oakland A’s in Moneyball. Traditional baseball talent scouts didn’t need newfangled analytics telling them which ballplayers were worth drafting. They just knew. (Of course, they were wrong.)
Simon Says: In chaos lies opportunity.
I have no doubt that new data sources and technologies will continue to unearth fascinating insights into just about all walks of life. Healthcare and the IoT are just the tips of the iceberg. I am equally sure, though, that many professionals will continue to discount their power. And it is here that the next generation of companies will distinguish itself from the pack.