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Big Data - Which Data? Part 1

Posted on March 17, 2016 at 9:20 PM

I had a convergence of two books I read on vacation last summer that inspired a great deal of thinking. A friend of mine insisted I read Moneyball because he believes that the statistics that really matter to a business are often undervalued because they are not understood. Tom Walker at Praexis Business Labs looks for those undervalued data points and highlights the value they bring.

 

The other book was How Google Works. Eric Schmidt and Jonathan Rosenberg talk about the questions they ask and explain how they look into the future to see what Google should invest in next.

 

In Moneyball, Michael Lewis explains that the statistic of batting averages was overvalued and over compensated in comparison to on base percentage. A preliminary indicator of probability of winning a baseball game was to reduce the likelihood of a player getting out. Because each team has a defined scarcity of outs in each game, avoiding them by putting a person on base by any means provided a statistically better chance that your team would win.

 

The game Google plays is far different: They look for Billion Person Problems and then try to find ways that they can identify a solution for those people. When it comes to those solutions, though, they look at how those solutions will need to access data. For instance, if self driving cars are in our future, who will provide them the analytics data for driving? Google built its own self driving car to find out how that data could be used – so when self driving cars need data, Google will be there with the solution. But really, this question is as old as technology: How will the hardware use the software?

 

When it comes to what I do, I have to wrestle with how I identify the real data points that matter vs. the ones that appear to matter. Then, taking that one step further, what enormous problem looms in the future of my industry? If I can use modern attempts to solve those data points, and ask questions mindful of the future in my industry, I will be not one step, but ten steps ahead of my competition.

 

So let’s take this into a business development environment and build a case study: Cold calling versus Social Selling.

 

Fifteen years ago a wizened old life insurance salesman told me that people like doing business with their friends. So, he told me, make a lot of friends. This salesman’s proverb is more true now than it ever was. People do want to do business with their friends, or with people who have expertise in their industry, who work with a company they are familiar with, or who have a solution that a company or person they respect bought.

 

But why?

 

Simple: At the Director level I am bombarded with so many ads on so many forms of media that I can’t tell what’s real anymore. Are you Joe from the call center this week leaving me a message? Or Sally from that marketing firm across town? I don’t know, I don’t care, and I’m sending you directly to voicemail and deleting it without listening. But, if you’re Phillip, Jim’s friend, who is doing business with my competitor (but one I respect!), I know I want to talk to you if only to find out what my competition has learned that I haven’t.

 

Compare this to a call report of hundreds of cold calls, aggregating in thousands of voicemails left, deleted, ignored. The data points are clear: you made a bunch of calls! In Moneyball, your batting average is great, but you never score. Whereas building, cultivating, and working a network of connections to get in front of people isn’t sexy, but you score. Your company wins and as a salesperson you get paid.

 

When it comes to data, what’s more important? Walks or Batting Average? Connections or Cold Calls? In both cases, it depends on how you use them. But just because something is easily measurable, doesn’t mean it is valuable.

 

We’ll get to Google and solving Billion Person problems in the future.

Categories: Analytics, Automation