The 7 Data Mistakes You’re Probably Making

6 Min Read

Data is becoming a more important fixture for modern businesses, serving almost as a kind of currency that can facilitate everything from measuring the effectiveness of marketing campaigns to evaluating employee performance. But many entrepreneurs see data as something valuable in and of itself; the more data you have, the better, and if you have it, you’ll make better decisions.

The truth is, gathering data is just the first step of the process, and data alone can’t possibly give you a full, accurate illustration of your subject. You also need to be able to collect, organize, interpret, and showcase that data effectively if you want to succeed—and most people are making critical mistakes that prevent them from doing this.

The Most Common Mistakes

Entrepreneurs and data analysts too frequently make these critical mistakes:

1. Not gathering enough data. It’s a bad idea to run with the philosophy that “more data is better,” because it prioritizes volume over accuracy. However, you need a minimum quantity of data before you can start feeling confident about your conclusions. For example, if you have 1,000 customers, you can’t select 2 of them to interview and feel confident about the attitudes of your entire population. You need a bigger, more representative sample size.

2. Gathering the wrong types of data. You can also make the mistake of gathering the wrong types of data. If you’re running an auto repair shop and you learn the intricacies of your target demographic’s eating habits, that information isn’t going to help you. This is an egregious example, of course, but the principle is the same; you need to gather data points that allow you to form conclusions and take action, rather than collecting data for the sake of collecting data.

3. Using the wrong dashboards. Your dashboards have a bigger impact on your results than you might imagine. These tools are responsible for collecting all your data in one place, giving you the power to crunch the numbers and generate reports, and giving access to multiple team members. With so many options available, it’s hard to tell which one is the right choice for your business, but you’ll need to wade through all of them if you want the best tool for your arsenal. Otherwise, you might spend an excessive amount of time training new employees, or generate lackluster reports that don’t emphasize the key variables you want to zoom in on.

4. Allowing bias to distort your conclusions. The human mind is significantly flawed, so it’s usually a bad idea to trust your instincts when it comes to analyzing data. We’re prone to a host of cognitive biases, from confirmation bias to survivorship bias, that can quickly distort even the objective information in front of us. It’s best to learn these cognitive biases, and figure out ways to compensate for them, so your conclusions aren’t muddled or warped.

5. Comparing apples to oranges. Most newcomers try to reach when there isn’t a clean comparison, putting data from one selection against the data from another selection. This “apples to oranges” comparison can lead to false conclusions, so it’s better to compare your data sets as cleanly as possible.

6. Failing to isolate variables. Modern applications usually demand reviewing data sets with dozens, if not hundreds of distinct variables—especially in the marketing industry. When you find a correlation, such as a relationship between content length and visitors, it’s tempting to conclude a causal relationship, but this is dangerous (and sometimes hilarious). Instead, you need to isolate the variables you’re working with so you can prove or disprove causation, and learn more about the relationship between your data points.

7. Asking the wrong questions. Data isn’t going to give you any conclusions on its own. Your charts and graphs typically won’t give you an obvious breakthrough. Instead, you need to ask questions of your data, and use the tools you have to uncover the answer. If you’re asking the wrong questions, whether they’re misleading or non-actionable, it doesn’t matter how good your data is or how intuitive your tools are.

Data Isn’t Perfect

It’s true that data is so valuable that it’s becoming commoditized, but it’s practically worthless unless you know how to use it effectively. There will always be hiccups with your methodology, your organization methods, and even your interpretation, but the more familiar you are with best practices and the more committed you are to utilizing your data effectively, the more likely you’ll be to land on accurate, worthwhile conclusions. Don’t assume that your efforts are working; challenge them, and keep tweaking your approach to uncover your hidden biases, ask better questions, and get more out of your analytical efforts.

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