Tag Archives: data usage

You Need Scouts

I don’t think there has been a baseball movie made that didn’t feature some weathered old guy seated in the bleachers somewhere.  He usually utters undecipherable baseball jargon while taking copious notes.  This, dear reader, is the baseball scout, who used to be how talent was discovered.  If you’ve seen or read Moneyball, you know that the scout is an endangered species.  This article from USA Today last week talks about how many pro scouts are still unemployed one month before the start of spring training.  The reason?  Data.

Photo by Justin Lafferty 00:19, 7 December 200...

(Photo credit: Wikipedia)

Baseball is in the throes of the Moneyball movement.  Teams have been laying off scouts and turning to sabermetrics, which Wikipedia defines as the empirical analysis of baseball, especially baseball statistics that measure in-game activity.  Baseball has fallen in love with data.  Maybe your business has too.

Here is the problem, both for you and for baseball.  There are certain things that don’t show up in data.  A player’s leadership qualities in the dugout aren’t quantifiable.  Potential can often be visible but not measurable.  That’s true in your office as well.  The data may show you what it happening but it’s hard for it to show you what could be happening.  That requires humans: scouts.

We all need scouts.  We need people who use the data as a tool but who also have the experience and wisdom to know when the data is missing something.  That doesn’t mean projecting one’s wishes into the numbers nor distorting the story those numbers tell.  It is, however, an acknowledgment that there is often a bigger picture than what’s inside the frame.

Here is a quote from a scout:

I’ve got 23 years in the business,’’ Wren said, “and now clubs don’t want that experience? I look at teams now, and they’re hiring guys who aren’t really scouts. They’re sabermetric guys from the office, and they put them in the field like they’re scouts, just to give them a consensus of opinion.

That’s dangerous for a baseball team.  It could be fatal for you.  You’re up!

 

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Filed under Helpful Hints, Huh?, sports business

It’s Not Just Data

There is an interesting case that was argued before the Supreme Court the other day and it just might have an impact on your business.  There was also a lawsuit filed in an unrelated matter that could have the same effect.  A third item is a study that’s kind of scary. Let’s have a quick look at them and think about what they might mean to anyone who gathers information about their customers. 

First, the case before The Supremes.  It involves Spokeo, one of the large data aggregators.  Spokeo’s information about a consumer was almost 100% wrong.  As Justice Kagan said, “They basically got everything wrong about him. They got his marital status wrong. They got his income wrong. They got his education wrong. They basically portrayed a different person.”  The plaintiff was seeking a job when he filed suit, and worried that the errors in the report would affect his job search.  The other suit involves Ashley Madison.  They were sued for allegedly misleading users by inflating the number of women who belonged to the service.  As we have found out from the data hack, only a small percentage of the profiles belonged to actual women who used the site.  The company hired employees whose jobs were to create thousands of fake female profiles.

I suspect that a third form of data abuse will be in the courts shortly, as a recent study found that the average Android app sends potentially sensitive data to 3.1 third-party domains, and the average iOS app connects to 2.6 third-party domains.  None of the apps notify users that their information is being shared with third parties.  Data that’s wrong, data that’s fake, and data that’s shared without permission.  I suppose if we could get the fake guys to populate the wrong guys, sharing it without permission wouldn’t be a big deal.  Since it’s your personal information, it is.

If you gather data (and who doesn’t), you have a responsibility to keep it secure and not to use it for purposes beyond what the owner of the data (that would be you and me) reasonably expects you’ll be doing with it.  If you’re disseminating data, especially data that could impact someone’s life and not just your own business, you need to be sure it’s accurate.  And if you’re making stuff up, please just go away.

They’re not just data points, folks.  They’re people.  Maybe they’re lawsuits in waiting, or maybe they’re your spouse, kids, or parents.  Let’s be careful out there, ok?

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Filed under Helpful Hints, Huh?

Who’s Working For Whom?

Ever encounter a situation where things seem backwards? Maybe you’ve seen a parent being told what to do by a child or a customer being berated by a service rep. It makes you wonder who is in charge or who is working for whom. I have another thought along those lines today, and it has to do with data. There was a post from AdAge by their data reporter, Katie Kaye who wrote the following about the NY Times piece on Amazon: 

The article should inspire us to question the value of decisions based entirely on data to create business efficiencies at the expense of human empathy and the arguable imperfections that can benefit any organization or project.

I like that. It makes you ask who is in charge here: the humans or the numbers. We all ingest more data than we can consume, and, unfortunately, some of us allow that massive intake to be regurgitated as unconsidered decisions. That’s a bad idea. The data is there to serve us, not the other way around.

I’m the first to say that we need lots of data. Without impartial feedback, we’re flying blind, and data can help us make better decisions. The key there is “help US”.   Data without the context of a plan is useless. Data that’s not actionable is useless.  Data that causes us to overreact, however, is  dangerous.  If you watched any election coverage last night, you probably heard a lot about early results and the need to wait for data from key precincts.  How many times has someone in your organization overreacted to an early piece of data, only to find out that it was not at all typical of the overall results?  We need a plan, we need context, and we need a little patience.

When we chase after outliers, we’re working for the data.  That’s backward.  Data, and all the other technological tools in our arsenals, needs to work for us.  Make sense?

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