Data analytics is becoming incredibly popular for business owners all over the world. Now more than ever, entrepreneurs can quickly analyze massive quantities of data and form predictions about the future. They can collect data from millions of customers, study historical patterns, and correlate otherwise disconnected variables — but they can also be misused.
Without a strong data foundation, it won’t matter how many AI-powered data analytics tools you use or how much data you’re collecting. The mistakes you make along the way could compromise any conclusions or actionable insights you derive.
The Biggest Data Mistakes to Avoid
These are some of the biggest data-related mistakes new entrepreneurs make:
1. Gathering too little data
The more data you have, the more accurate the conclusions you can draw. Even the best predictive analytics algorithms in the world require a minimum threshold of data before they can operate successfully. Depending on your situation, that may mean surveying more customers and prospects, allowing your data-gathering tools to function longer, or simply researching different areas.
2. Focusing on the wrong data
It’s also possible for you to focus on the wrong types of data. “More” isn’t always better if you’re supplying your dashboards and algorithms with data points that don’t really matter to your bottom line or could possibly lead to a different conclusion.
3. Entering the data incorrectly or inconsistently
This is one of the biggest mistakes made in the data analytics world because humans are often the ones entering the data. There are dozens of potential data entry errors that could compromise your system, including incorrect data formatting, simple typos, duplicate entries, and more. There are a few ways around this problem, but it may never be perfect. You can, for example, incorporate as much automation as possible in your data collection strategy; machines tend to make far fewer and less noticeable mistakes than their human counterparts. You can also provide better training and more rigorous review standards for your human workers.
4. Using the wrong tools
It’s also possible to use the wrong tools for data collection, data management, or data analysis. “Wrong” is a broad term that could refer to problems with consistency, effectiveness, usability, reliability, or security. For example, if you invest in a tool with lax security standards, it could compromise the integrity or safety of your data. If you invest in too many tools at once, without understanding their strengths and weaknesses, it could lead you to inefficient practices.
5. Allowing human bias to enter the equation
The human mind is riddled with cognitive biases that negatively affect our ability to think logically. For example, take confirmation bias — the natural tendency to select data that confirms or reinforces our established assumptions. Confirmation bias can influence you to selectively ignore the variables that seem to contradict your initial assumptions, instead directing you to the pieces of data that reinforce them. If you’re not careful, your biases can cause you to completely misinterpret the otherwise “good” data you’ve gathered.
6. Studying multiple variables simultaneously
Just like in a science experiment, it’s usually best to study things one variable at a time. When you isolate your variables, you can much more reliably determine whether a specific variable is influencing your results. Otherwise, there will be too much noise for you to reasonably conclude the cause-and-effect relationship of your data.
7. Comparing apples and oranges
It’s also tempting to compare two unlike selections of data, but this can lead you to faulty conclusions. It’s almost always better to compare “apples to apples,” or similar segments of data, against each other. For example, you wouldn’t want to compare the performance of a print ad to an online ad if the ads are designed completely differently.
8. Asking the wrong questions
It’s best to think of data analytics as a tool that answers the questions you ask it. If you’re asking the wrong questions, you’re going to get unhelpful answers. Here’s a simple analogy: Let’s say you ask an algorithm “Is it going to rain tomorrow?” and the algorithm “knows” there’s going to be a blizzard. Technically, the answer is no, but a “no” response is super misleading because you’re trying to see if there’s any precipitation to worry about.
For your data analytics approach to be successful, you need a strong foundation in place. That means setting strict rules and procedures for how to collect and analyze data and choosing the right tools for the job. You have to learn from your mistakes and perfect your approach early, scaling up from there. Otherwise, even the best AI systems in the world won’t be able to help you.