This is the era of “big data” and “analytics” if you listen to the pop literature. And to some extent it is… or should be.
Management increasingly demands evidence to support recommendations, rather than “that’s what others do” or “it is prevailing practice.” And management is also realizing that mimicking the strategies others use to compete for talent can only move the organization towards parity. There is also an increasing recognition that in order for a strategy or program used “there” can only be expected to produce a similar result “here” if “here” is identical to “there.” And that is almost always a bad assumption.
Rewards practitioners should of course use benchmarking to determine how pay structures and pay rates compare to prevailing market rates. The benchmark data is used to support recommendations to move the pay structures x% and to budget y% for base pay adjustments and incentive awards. Information is in plentiful supply… research studies and compensation surveys are full of data. But deciding whether the information is of high quality, whether research findings are sound and whether prevailing practices are appropriate to an organization to emulate requires the appropriate level of expertise in research evaluation and quantitative analysis.
And even after the credibility and relevance of data to the organization is established how the data is applied matters greatly. If appropriate analysis is not applied correctly the data can become misinformation. High quality analysis can provide a clear picture of “what is,” but there is still the issue of “what to do about it.”
An excerpt from “Analytics In Rewards Management” by Robert J. Greene, PhD, CCP, CBP, GRP, SPHR, GPH. Email email@example.com to request a copy of the full article.