How modern BI & AI systems reduce human errors in data

It’s no secret that human error is common. After all, your employees are only human, and they’re prone to making mistakes. Data entry has an error rate as high as 4%. That means without verification, your company could be producing 400 errors for every 10,000 entries into databases.

There are numerous cases of human error when it comes to data management that should resonate with you and your team. If a NASA engineer uses inches instead of centimeters in a design, it could mean an explosion. If a doctor prescribes the wrong medication because it was notated by a human incorrectly, it could kill a patient. In data management, reducing error is critical to success and decision making. Data quality is next to sainthood as far as business intelligence is concerned.

Why does human error matter?

The cost of these errors can be quite high. They can be challenging to detect. They can lead to bias in your organization and its processes. You might not always have the time or resources to make the best decisions available. Humans aren’t always capable of being objective enough when it comes to data analysis, especially in highly emotional situations. Even if you think you’ve done everything right, you should still have processes in place and checklists for your team members. Humans can still make mistakes at any given point in time due to personal issues (stress from too much work), cognitive biases that lead them astray, or any number of other reasons.

What does this mean for businesses like yours? It means that without safeguards against human error, there’s a good chance that your company will end up making costly mistakes due to human error alone. That is outside of typical concerns and factors that affect business success, like market conditions.

In the United States alone, the potential cost of bad data caused by human error is roughly $3.1 trillion a year. That is money that could be spent on new product development, ensuring strong customer relationships, or expanding to new areas of business.

How does AI affect business intelligence?

In this day and age, when you collect so much data, your human intuition is not enough to process it. AI can help you discover trends in your data that might otherwise go unnoticed or be forgotten by the time your BI system gets them.

A modern business intelligence platform with integrated AI will help you automatically find patterns and trends in your data. You can also program it to recommend certain actions based on those insights. For instance, if the system discovers a drop in sales for a particular product, it can send an alert to the product manager responsible for that SKU so they’d know about it before next quarter’s results are available.

AI does not have bad days. There is no worrying about someone spilling coffee on an AI’s freshly laundered suit on the way to work. It performs at optimal speeds and functions all hours of the day regardless of what is happening to your human assets.

What types of human errors occur in data analytics?

In data collection, the most common human error is collecting improper or insufficient data. For example, if you’re trying to collect data on a specific product line as part of a sales report, but an employee forgot to include that information in their weekly email, that missing information can throw off your analysis.

In data analysis, the most common human error is using incorrect calculations and formulas. For example, when analyzing your company’s sales reports, you may have accidentally used the average instead of the median in your calculations, which can skew your findings.

In data reporting, the most common human error is presenting inaccurate or incomplete information. For example, if you’re reporting on customer satisfaction in response to an email campaign and forget to include feedback from a few respondents on one side of the spectrum (either very positive or very negative), this could paint a skewed picture of customer sentiment overall.

In data storage, the most common human error is storing redundant files and failing to delete outdated documents. This makes it difficult for teams to find what they need and increases security risk since old files may contain sensitive information about customers or employees.

How can AI reduce human error?

Humans can misjudge the effectiveness of their decisions due to a lack of access to relevant data. But what if you have all the right data? Could AI still help?

In theory, making good decisions is a straightforward exercise. You need only ask yourself two questions:

What is the goal I’m trying to achieve?

How will my decision affect achieving that goal?