We live in a data-drenched world, where everything – from daily sales to employee biometrics – can be tracked, stored, and analyzed. With such an influx of information, it can be hard to understand what types of data will get you the best answers to guide your decision-making process.
Data management has become an essential part of decision-making for nearly every business no matter their size, and for good reason: it’s an invaluable way to get real-time feedback on how your processes are working and what needs to change. Given its significance, it’s tempting to collect as much as possible, then find a way to sort and arrange it afterward, but this is a costly mistake. Making sense of all this information in a way that can lead to results is time-consuming and can be quite difficult, depending on what kind of information was collected and what the end goal is.
Data should be collected for a specific purpose
A true data driven leader collects data intentionally, with a clear use for this information and a way to put it into action. They recognize that numbers are meaningless without context, so they look at the big picture and try to understand how the numbers are guiding them.
When considering data collection, don’t jump in right away: identify the goals first, then collect the data. Scientists don’t just start recording evidence before they have a framework; instead, they come up with a hypothesis, decide how and what to collect, and then make sense of it and draw conclusions.
If you already have begun collecting data on a few topics, that’s still valuable, but it should now be the background of your new effort. You can use it to review what’s happened before, come up with a new goal or hypothesis, and then revisit it to see if anything has changed after an intervention.
Data should be collected in a manner appropriate for the subject
There are many forms of data capture, each with their own purpose. The Harvard Business School outlines seven of the most popular and effective data collection methods, but there are even more out there, some hybrids of these seven methods.
If you don’t collect information in the right way, you’re not going to get definitive answers, and you may end up drawing the wrong conclusions and heading in the wrong direction. If you want to know about customer satisfaction, data about daily sales isn’t going to tell you anything: there might have been a huge purchase that day from a very satisfied customer, then many small transactions from those who were less impressed. For that, you’ll need to find different methods, such as customer satisfaction surveys or tracking purchases over time from the same customer.
The best way to draw good conclusions is to combine multiple tracking methods to get an overall picture. For example, if you want to measure employee satisfaction, you can look at a number of factors: employee retention, absenteeism, and anonymous surveys. Employee retention will show you whether your company has built a strong connection with its employees; absenteeism will provide you with information about job fit for particular employees; and anonymous surveys allow you more qualitative feedback that can be matched to trends in other areas.
Data should be contextualized carefully
As any scientist knows, it’s incredibly easy to draw false conclusions about a given dataset and move in the wrong direction with further research. Again, numbers are meaningless if you don’t make sense of them and then use them to make changes in your organization.
Data collection, especially in today’s world, is relatively easy: the real job is understanding it and implementing the changes. To start, you can use statistical analysis programs to organize the data in a manner that makes sense, then bring it all together to figure out what it means. Depending on how much data you have and what type it is, you might decide to bring an outside consulting firm with experience in statistical analysis, as they can present it in an easy to comprehend way.
Once you actually have the results, you need to step outside of the data and consider what was actually happening in your organization – and the world as a whole – to see what it means moving forward. One of the largest disruptors of our era was the Covid-19 pandemic, and you can’t expect that data collected during that period will necessarily represent failures of your organization; however, it can show how your business adapted to the new situation and what changes might be worth keeping.
For example, when all your employees worked remote, did you notice a boost in productivity and a reduction in absenteeism? This, in addition to employee feedback, might suggest that your employees would work better with a hybrid arrangement that allows them more flexibility in their working lives.
One of the most important things is to be willing to look at the data objectively and with context, then make decisions that might not necessarily be what you expected to do. This may be the hardest part of all, but it’s also the most important: a true sign of a data driven leader, who does what’s best for the organization even if it’s not exactly what they wanted in the beginning.