Editor's Note: Our data collection capabilities and the technology supporting them continues to advance, but the questions and steps necessary to draw knowledge and insights from them remain constant. Here with Classic advice and an analogy on how to create gems from the rock we mine is Stephanie Thomas.
In a recent post, Margaret O'Hanlon told you how to get rich quick using the goldmine of information just sitting there, waiting for you. Mo is absolutely right - data can be immensely valuable in daily conversations, and having fresh information about the distribution of employees by key characteristics - and how those distributions vary across the organization - can ultimately lead to improved decision-making.
If you're mining your data, or are thinking about starting, there is one essential point to keep in mind: data has no value on its own.
It doesn't matter how many data points you collect, how frequently you update them, or how many cool dashboards you build with them if they don't lead to more knowledge about your processes and performance. Data in-and-of-itself cannot really inform decision-making; it's the knowledge we gain from that data.
Think of it in terms of diamond mining. You're out working a particular piece of land (your HRIS database). You come across a vein of diamond ore, and you break off a chunk (the data). Let's say you manage to find an actual diamond in the ore. Relatively speaking, it's pretty ugly and not really usable in its current form: the diamond is probably embedded in other minerals, may contain impurities, and almost certainly is covered in earth.
Before you can do anything with it, it needs to be separated from the ore, cleaned, and cut. But it's not a gem yet. It still needs to be polished. Diamond cutting and polishing can take anywhere from several hours to several months. During this process, the typical diamond will lose half of its original weight.
We now have a gem, but we're still not done. Sure, it's beautiful to look at, but you can't really do anything with a loose stone. It has to be set into a ring or other piece of jewelry. Only then does it really attain its ultimate purpose.
Data mining is a lot like diamond mining. Assuming you know what you're looking for and where to look (not always an easy task), mining really is only the first step in the process.
Who cares about the percentage of bonus-eligible employees who did not receive a bonus! The interesting questions - the knowledge - are related to why did X% not receive bonuses. Did productivity change dramatically? Did we experience higher/lower turnover? Did our goals and expectations change dramatically? Are our goals and expectations reasonable?
It's the answers to these why questions that inform strategic decision-making. Data itself is nothing more than the raw material. We have to transform that raw material into information by looking at the patterns, associations and relationships among the data. Only after we've cleaned, cut and polished our raw materials can the insights they contain dazzle brilliantly in the sunlight of informed strategic decision-making.
Stephanie Thomas, Ph.D., is a Lecturer in the Department of Economics at Cornell University. She teaches undergraduate and graduate courses on economic theory and labor economics in the College of Arts and Sciences and in Cornell’s School of Industrial and Labor Relations. Throughout her career, Stephanie has completed research on a variety of topics including wage determination, pay gaps and inequality, and performance-based compensation systems. She frequently provides expert commentary in media outlets such as The New York Times, CBC, and NPR, and has published papers in a variety of journals.