Can big data solve your HR problems? The important thing to remember is that the basis of big data is statistics, and you have to be careful interpreting results. So-called confounding variables may muddy the waters. For example:
History can intervene. The measurement may have been accurate, but things changed during the measuring period and that makes conclusions suspect. For example:
- Say you tried a new retention program, and it appears to be working. But also say during the measurement period you have new bosses, new goals, and new technology. Which one is the real cause of the improvement?
- Say your wellness program shows a dramatic improvement in hours exercised from March when the first measurement was taken until July when the second measurement was taken. If you’re in the north, was that caused by your new program, or was it just because people tend to get outside and exercise more in the summer?
Robins Cause Spring
Another common problem is confusing correlation and causation.
- A standard illustration of this is to take the hypothesis—robins cause spring. Collect your big data, and your hypothesis will be clearly supported, because there is a strong correlation between the arrival of robins and the arrival of spring.
- You expand your flextime program and predict that your next annual attitude survey will show great improvement, which it does. But was it the flextime, or was it because:
- During the measurement period, there is a new president who is popular;
- Employees have gotten generous bonuses for the first time in three years;
- The company has become deeply involved in community projects; or
- There’s a new 360 coaching/development program.
So, the point is, don’t take all big data indicators at face value. And, furthermore, even if the big data results are valid for one company, that doesn’t mean they are valid for yours.
Are class action lawyers peering at your comp practices? It’s likely, but you can keep them at bay by finding and eliminating any wage and hour violations yourself. Our editors recommend BLR’s easy-to-use FLSA Wage & Hour Self-Audit Guide. Try it for 30 days … on us.
Using Small Data
Of course, the largest companies can develop big data by themselves. But what about smaller companies? They can still do helpful analysis of their program, policies, and practices.
For example, every organization wants to know which applicants will make the best employees. That’s hard to measure because you’ll never know whether the rejected candidates would have been better or worse than the candidates you selected. But you can measure whether your tests and selection methods are working well.
Take any test that you use (most any selection device is a test, legally, for example, the interview is a test as much as a keyboarding test or a honesty test).Identify the traits, skills, or abilities you were hoping to measure with that test, and then check to see how well you did in predicting the success of the new hires.
You can also check for the validity of the kinds of assumptions and barriers mentioned earlier in this article (job hopping, criminal record, etc.). Which assumptions are you making, and do they hold water?
You can also do analyses to point out discrepancies in how well your managers are following policies. One particular area to check is wage and hour. Wage and hour should be simple, but it’s just not. Are managers allowing off-the-clock work? (Or worse, demanding it?) And then there’s the issue of mobile devices after hours—the list of ways you can get into trouble seems endless.
How do you really know if your managers and supervisors are following your guidelines? There’s only one way to find out what sort of compensation shenanigans are going on—regular audits.
To accomplish a successful audit, BLR’s editors recommend a unique checklist-based program called the Wage & Hour Self-Audit Guide. Why are checklists so great? It is because they’re completely impersonal, and they force you to jump through all the necessary hoops, one by one. They also ensure consistency in how operations are conducted. And that’s vital in compensation, where it’s all too easy to land in court if you discriminate in how you treat one employee over another.
Experts say that it’s always better to do your own audit and fix what needs fixing before authorities do their audit. Most employers agree, but they get bogged down in how to start, and in the end, they do nothing. There are, however, aids to making the Fair Labor Standards Act (FLSA) self-auditing relatively easy.
What our editors strongly recommend is BLR’s Wage & Hour Self-Audit Guide. It is both effective and easy to use, and it even won an award for those features. Here’s what customers like about it:
- Plain English. Drawing on 30 years of experience in creating plain-English compliance guides, our editors have translated FLSA’s endless legalese into understandable terms.
- Step-by-step. The book begins with a clear narrative of what the FLSA is all about. That’s followed by a series of checklists that utilize a simple question-and-answer pattern about employee duties to find the appropriate classification.
All you need to avoid exempt/nonexempt classification and overtime errors, now in BLR’s award-winning FLSA Wage & Hour Self-Audit Guide. Find out more.
- Complete. Many self-audit programs focus on determining exempt/nonexempt status. BLR’s also adds checklists on your policies and procedures and includes questioning such practices as whether your break time and travel time are properly accounted for. Nothing falls through the cracks because the cracks are covered.
- Convenient. Our personal favorite feature: A list of common job titles marked “E” or “NE” for exempt/nonexempt status. It’s a huge work saver.
- Up to Date. If you are using an old self-auditing program, you could be in for trouble. Substantial revisions in the FLSA went into effect in 2004. Anything written before that date is hopelessly—and expensively—obsolete. BLR’s Wage & Hour Self-Audit Guide includes all the changes.
You can examine BLR’s Wage & Hour Self-Audit Guide for up to 30 days at no cost or obligation. Go here and we’ll be glad to arrange it.