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 ... That's what the software architect Grady Booch had in mind when he uttered that famous phrase: "A fool with a tool is still a fool."

From my last post, I talked about humans being able to make the data actionable. The understanding of the data that is used for the model is more important than understanding the math behind the algorithms. Algorithms can find the patterns humans can't, however the algorithms can't determine if the answers are relevant.

We forget that it is not about the data; it is about our customers having a deep, engaging, insightful, meaningful conversation with us

Exactly.

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).

The answers will be fancy, but will not help make decisions for frontline or CRM more effective.

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.

Humans should always be paying attention to the outcomes and put parameters around the use of automated answers. Answers should be used in conjunction with other factors for the best decision.

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.