For a modern business, data is everything. It can tell you who your customers are, what they like best, what ideas you should pursue, and how those ideas will eventually perform. If you can ask the right questions and use the right tools to uncover and crunch the data, you’ll be in a much better position to sustain your profitability and outcompete your rival businesses.
The Ignorance of Data
The problem is, many businesses are ignoring data, or neglecting it in crucial ways that prevent their organizations from developing. These are some of the most common ways it’s happening:
Companies are ignoring their “business climate.” As outlined in this post by Flexera Software, it’s incredibly dangerous for businesses to ignore their changing “business climates,” mirroring the risks of climate change denial. A business climate can refer to many things, including the level of competition, type of competition, the disposition of your target demographics, or even the tools you use to properly run and grow your business. These things change over time, and data can help you understand and prepare for those changes—without that data, you’ll remain stagnant as the world around you continues to change, and eventually your business will become obsolete.
Companies can’t perform root cause analysis. Data can give you some surface-level conclusions immediately. For example, it would be easy to tell if your incoming web traffic suddenly dropped to new lows. This is an indication that your customers aren’t as interested in your website for some reason, so at this point, most companies would delve a little deeper, looking for any variables that could have influenced this new trend. The problem is, few companies go deep enough. With so much data available, it’s easy to find one or a few variables that may have contributed to the decline, but it’s exceptionally more difficult to keep working until you’ve identified the real root cause. Accordingly, many businesses end up only correcting a part of the problem.
Companies over-rely on one set of data. Cherry picking presents one of the biggest dangers of big data analysis. When you’re presented with a wealth of data and information and you have preconceived notions (and biases) about the reality of the situation, it’s easy to find data that confirms your beliefs. This is confirmation bias in its purest form, and its effect is compounded when you only choose one set of data to work with. There are too many potential flaws in data acquisition for you to be able to rely on a narrow range of information and walk away with a reasonable conclusion.
Companies collect data indiscriminately. Collecting too much data can be problematic, too. If properly managed, copious amounts of data can be a good thing—it means you have a wider range of information to work with and you can ask more detailed questions to get more detailed, reliable answers. But the more data you add, the more variables you’ll have to consider—and if you don’t consider them, you may accidentally lead yourself in an inaccurate or irrelevant direction.
Companies ask the wrong questions. Data can’t give you conclusions by itself. It can only provide you answers to specific questions you ask of it. Because of this, if you ask the wrong questions, you’ll never get the answers you need to adapt your business. You might ask “leading” questions of your data that point you toward forgone conclusions, or you might completely forget a question that would lead you to an important conclusion about your demographics. Answers don’t just “jump out” of the data—you have to know what you’re looking for first.
Acquiring, organizing, and interpreting data are some of the most important elements of your business. Without those steps, or when you implement those steps inefficiently, you run the risk of corrupting your entire operation. Humans are prone to error, so the course of data interpretation is always going to have some degree of imperfection, but you can guard against the ramifications of this by compensating for your biases and striving for the most complete set of data possible.