If you suspect that your company is guilty of making these mistakes, then it's time to reassess your people, processes, and platforms.

Collecting and analyzing data is essential to ensuring your company is growing in the right direction. But, as the old “garbage in, garbage out” saying goes, your data is only as good as how and why you track it.

There’s a common misconception out there that data analysis is a stone-cold science. But it’s not that cut and dry. In fact, data analysis might, in some respects, be more of an art than a science.

It helps when you think of your company’s data in terms of storytelling. If you’re not paying attention to how you read and interpret the metrics, then you just might miss the moral of the story. That’s why making mistakes when it comes to data analysis can cost you valuable insight into the health and performance of your company.

So, to ensure you’re analyzing your company’s data in a meaningful way, you’ll want to avoid the following:

Mistake #1: Observing metrics at face value

Context is crucial when it comes to data analysis, but many companies make the mistake of accepting some metrics at face value.

A great example of this would be with website traffic metrics. Let’s say your company’s overall page views experienced a sudden spike in March. Without any further analysis, a company could easily assume more traffic is a good thing–who wouldn’t want more potential customers visiting the site?

But if you were to analyze the context of this spike–like determining where the traffic is coming from or how long they are staying on each page–you might discover it’s the result of a hacking attempt or an email campaign that was sent out with an embarrassing mistake.

Bottom line is to never take any metric at face value. Make sure your team is finding out why something happened, regardless of whether it seems like a good thing.

Mistake #2: Not using segmentation

When it comes to analyzing your data you cannot use a one-size-fits-all approach. There are a ton of unique visitors checking out your website, opening an email, or downloading your app–do you know who they are? Yes, it’s impossible to identify and cater to every potential customer, but you should be able to break down large audiences into smaller segments.

For example, not everyone who is coming to your website is ready to make a purchase. These potential customers may land on a page urging them to buy when they’re not ready, and it might be damaging your conversion rates. You’ll want to find out who these people are, where they are coming from, and what you want them to do. You can then make important business decisions that will cater to this particular target’s preferences based on the segmented data you collected and analyzed.

Mistake #3: Not setting a baseline

Runners and professional athletes know the importance of setting a baseline. At any given time they can compare their current performance to their baseline to get an accurate assessment of where they are.

This practice is vital for businesses as well. You need to have a baseline and know what the numbers represent so you that can see where you stand at any given time. Overall, a good baseline will allow you to see the impact of how your company is performing.

Wrapping Up

If you suspect that your company is guilty of making these mistakes, then it’s time to reassess your people, processes, and platforms. Chances are one–if not all three–of these things will not be the right fit for your company.

You’ll need to find out what’s preventing your company from gaining a more nuanced, enlightened view of performance. The insights that you can gain from meaningful, in-depth data analysis can help you make critical business decisions that could improve revenue and allow you to easily outpace the competition.

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