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Big Data’s Human Component
September 19, 2012 Editor 0
Machines don’t make the essential and important connections among data and they don’t create information. Humans do. Tools have the power to make work easier and solve problems. A tool is an enabler, facilitator, accelerator and magnifier of human capability, not its replacement or surrogate — though artificial intelligence engines like Watson and WolframAlpha (or more likely their descendants) might someday change that. That’s what the software architect Grady Booch had in mind when he uttered that famous phrase about fools and tools.
We often forget about the human component in the excitement over data tools. Consider how we talk about Big Data. We forget that it is not about the data; it is about our customers having a deep, engaging, insightful, meaningful conversation with us — if we only learn how to listen. So while money will be invested in software tools and hardware, let me suggest the human investment is more important. Here’s how to put that insight into practice:
Understand that expertise is more important than the tool. Otherwise the tool will be used incorrectly and generate nonsense (logical, properly processed nonsense, but nonsense nonetheless). This was the insight that made Michael Greenbaum, Edmund and Williams O’Connor — the fathers of modern financial derivatives — so successful. From the day their firm, O’Connor & Associates, opened its doors in 1977, derivatives were treated as if they were radioactive — you weren’t allowed near them without a hazmat suite and at least one PhD in mathematics. Any fool, or mortgage banker, can use a spreadsheet and calculate a Black-Scholes equation. But if you don’t understand what is happening behind the numbers, both in the math and the real world, you risk collapsing the World Financial System, or more likely your own business.
Understand how to present information. Humans are better at seeing the connections than any software is, though humans often need software to help. Think about what happens when you throw your dog a frisbee. As he chases it, he gauges its trajectory, adjusts for changes in speed and direction, and judges the precise moment to leap into the air to catch it, proving that he has solved a second-order, second-degree differential equation. Yeah, right.
The point is, we have eons of evolution generating a biological information processing capability that is different and in ways better than that of our digital servants. We’re missing opportunities and risking mistakes if we do not understand and operationalize this ability.
Edward Tufte, the former Yale professor and leading thinker on information design and visual literacy, has been pushing this insight for years. He encourages the use of data-rich illustrations with all the available data presented. When examined closely, every data point has value, he says. And when seen overall, trends and patterns can be observed via the human “intuition” that comes from that biological information processing capability of our brain. We lose opportunities when we fail to take advantage of this human capability. And we make mistakes. Tufte has famously attacked PowerPoint, which he argues overrides the brain’s data-processing instincts and leads to oversimplification and inaccuracy in the presentation of information. Tufte’s analysis appeared in the Columbia Accident Investigation Board’s Report blaming PowerPoint for missteps leading to the space shuttle disaster.
There are many other risks in failing to think about Big Data as part of a human-driven discovery and management process. When we over-automate big-data tools, we get Target’s faux pas of sending baby coupons to a teenager who hadn’t yet told her parents she was pregnant, or the Flash Crash on Thursday May 6, 2010, in which the Dow Jones Industrial Average plunged about 1000 points — or about nine percent.
Although data does give rise to information and insight, they are not the same. Data’s value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn’t mean you will get “proportionately” more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns). Understanding how to use the data we already have is what’s going to matter most.
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