Data is misinterpreted more frequently than you might expect based in part on need. Sometimes organizations oversimplify or magnify their problems. Sometimes, they’re too quick to act on what turns out to be insufficient or abridged information that doesn’t show the whole picture. Yes, the data doesn’t lie but that’s not the point: There are many different ways to read it.
For example, let’s say the Blue Widget Co.’s latest product line isn’t getting the market reception it expects. Sales are slower than anticipated and there’s little buzz from their core audience. Blue Widget isn’t sure why — it’s got current data on what its customers prefer, how they buy and what they buy and if they respond to targeted and personalized messaging. Blue Widget is also up to date on the competition’s widgets. So what gives?
Data isn’t useful without the skills and experience to interpret it. In Blue Widget’s case, management may be examining their data incorrectly. Or they might be asking the wrong questions. Here are five ways to check:
- Take a step back to revisit your research goals. Make sure you know which key performance indicators are important to your business.
- Listen closely to what your customers prefer but don’t analyze so much that you paralyze your marketing strategy.
- Narrow your focus to just the relevant metrics but always keep the big picture in mind. Consider stringing together a variety of metrics for an unexpected insight.
- Look at the data from different angles to find out not just “what’s happening” but “why is it happening.”
- Be certain sure your data scientists are equipped with subject matter expertise. Good business decisions aren’t made in a bubble.
Just because you have good data in your hands doesn’t mean you automatically can digest the information. You may misunderstand it, you may still make the wrong decisions based on good information. It’s important to remember that there’s no single source of information.