In yesterday’s Advisor, we explored five dangers of big data; today, we present distribution dangers, stratification problems, and more.
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[Go here for dangers 1 to 5)
6. Distribution Dangers
There are two primary distribution dangers you’ll face: outliers and stratification.
Outliers
Here is the situation. You have surveyed 22 employers in your industry that are similar to your employer in size and sales, and you find that their average cost per hire (CPH) is $3,433. You are feeling pretty smug because your CPH is $2,800. But dig into the data.
Here are the CPH figures for the 22 companies surveyed:
|
$2,000 |
$2,600 |
|
$2,300 |
$2,400 |
|
$2,200 |
$2,300 |
|
$2,300 |
$2,500 |
|
$2,100 |
$2,300 |
|
$2,700 |
$1,900 |
|
$2,300 |
$2,150 |
|
$2,450 |
$2,300 |
|
$2,400 |
$2,200 |
|
$2,100 |
$14,700 |
|
$2,400 |
$15,800 |
In fact, 20 of the companies have CPHs well below your $2,800. However, two of the companies’ CPHs are much higher, one with $14,700 and one with $15,800. Those two outliers are skewing the data. Research reveals that in the company with $14,700, the only new hire reported was a new president. And the company with $15,800 had to hire from abroad and relocation expenses were included in their figures.
Eliminating the outliers, the average CPH for the remaining 20 companies was $2,300. And your CPH suddenly doesn’t look so good.
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7. Stratification
Another type of danger comes from how you array your data. When collecting data from a large number of subjects, you typically stratify the data. For example, if looking at the number of employees, you typically divide the responses into groups, like 1 to 49 employees, 50 to 99 employees, 100 to 499 employees, etc.
Here’s an example of some weekly readings on some factor:
|
Info 1 |
3 |
|
Info 2 |
7 |
|
Info 3 |
22 |
|
Info 4 |
45 |
|
Info 5 |
81 |
|
Info 6 |
23 |
|
Info 7 |
23 |
|
Info 8 |
81 |
|
Info 9 |
45 |
|
Info 10 |
22 |
|
Info 11 |
7 |
|
Info 12 |
3 |
For ease of charting, you choose to band together the data points in groups of 2. So, Info 1 (3) and Info 2 (7) combine for an average of 5, and so on.
|
Info 1 to 2 |
5.0 |
|
Info 3 to 4 |
33.5 |
|
Info 5 to 6 |
52.0 |
|
Info 7 to 8 |
52.0 |
|
Info 9 to 10 |
33.5 |
|
Info 11 to 12 |
5.0 |
And here’s the chart that produces:
That’s an expected normal curve, so you assume normal distribution. But what if you instead chart all the original data points? Now, the chart looks like this.
Obviously, something is going on with this bipolar distribution. Again, the data are the same in both charts; it’s just the approach to charting that changes.
8. History
You’ve established a program to improve engagement, and your survey shows improvement. But is it due to the program? Has something else happened at the same time that might explain the improvement? For example, is there new leadership with a new outlook? Is there a sharp rise in sales and profits and a great bonus?
9. Interpretation
Similarly, data are subject to different interpretations. For example, say you have been tracking turnover, have installed programs to improve retention, and can show the board or the C-suite that you have improved retention by 22 percent year over year.
One board member says, I disagree with your conclusion; I think your retention is improving because you have hired such weak people that none of them could find a job anywhere else.
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