The power of data analytics can’t be understated. It can help organizations maximize profits and improve customer experiences, assist with technological breakthroughs, and enable strategic movements that would otherwise be impossible when leveraged correctly.
The problem? Data analytics simply isn’t used properly because of human error. Fortunately, artificial intelligence technology now allows us to overcome human error and bias in data analytics to achieve even more impressive outcomes.
Today, let’s break down how your organization can leverage artificial intelligence to fix human error in data analytics.
The Prevalence of Human Error in Data Analytics
Data analytics is a broad field, and those who work in it are invariably intelligent and statistically literate. That said, they are still susceptible to a variety of human errors and biases, including but not limited to:
- Confirmation bias. Confirmation bias is an error in thinking that places greater value on data or revelations that match preconceived notions or ideas. In other words, human analysts are more likely to believe data that they already think is right rather than believe data or conclusions that counter what they think is true.
- Inability to crossover or break data silos. It’s tough for many organizations to organize data from multiple sources. On top of that, humans find it difficult to correlate understanding or revelations from multiple data sources or poorly accessible data. Basically, it’s tough for human analysts to make full use of all the data their employers bring in – it’s just a fact of how the human brain works!
- A tendency to downplay losses or negative data. Humans are loss-averse by their nature, so human data analysts tend to downplay losses or data that seem like they indicate an upcoming disaster. It can hinder leadership, lead to poor business decisions, and prevent an organization from responding to a crisis properly. It may even exacerbate damage or costs.
- Inflated predictions. Many humans accidentally offer inflated predictions for the future, particularly in areas like the fintech industry or financial markets. They might be too optimistic based on personal biases or interpret data incorrectly because of certain assumptions. This can cause mistakes from leadership or lead to major losses where a victory or financial success is expected.
- Difficulty analyzing surface-level data patterns. Even trained analysts or statisticians may find it hard to dive below the surface-level data their organizations collect. This can make it difficult for them to come to the correct conclusions, predict major problems accurately, or prevent them from occurring in the future.
Each of these flaws in human thinking is due to the limitations of biology or experience. However, artificial intelligence technology has no such limitations. It can be used in various industries and technologies, ranging from statistics software suites to time tracking software to market production solutions and more. That’s why many companies are looking to invest in AI for the future.
How AI Can Combat Human Error
Artificial Intelligence can effectively combat human error and operate more efficiently in data analysis. Here are some specific ways that AI can proactively counter mistakes while boosting data analytics results overall.
Avoid Confirmation Bias
AI can help humans overcome or avoid confirmation bias because it doesn’t have the same emotional triggers or weights toward certain data sets. AI digests and analyzes all types of data from all sources. In this way, AI does not cherry-pick certain data points to help it come to crucial but incorrect conclusions.
AI can also look at historical data to successfully identify patterns, trends, or outliers. Thus, Artificial Intelligence data analytics might offer more bias-free and overall accurate results than analytics performed by humans.
Correlate Large Data Sets
Next, AI can correlate large data sets better and break data silos where humans cannot. Artificial intelligence technology or programs can communicate between large data sets, correlate data points or outcomes, and come to wide-ranging, broad conclusions that humans may not imagine.
AI can correlate data sets using relational data modeling techniques, making the conclusions AI tools come to from these large data sets accurate and useful.
Doesn’t Downplay Losses or Favor Positive Data
Because AI tools don’t have the same emotional biases as humans, they are not in danger of downplaying losses, favoring positive or confirming data, or making similar mistakes. Instead, AI can help executives avoid tunnel vision and ensure that they make smart decisions.
When an AI program or tool looks at a dataset, it weights all data equally. This may prevent organizations from favoring positive datasets that confirm their biases or encourage their current decisions or trajectories.
Similarly, AI can fix human error in data analytics by showing executives or individuals when losses should not be ignored. A single loss might not be a big thing, but it could signify that changes are necessary to avoid catastrophic defeats or downfalls later down the road.
Naturally, these differences mean that AI tools can make improved predictions relative to their human counterparts. Humans are vulnerable to inflated predictions (i.e., overconfident predictions regarding the company’s success or executive decision). AI tools are not.
Therefore, they may make more accurate or statistically relevant predictions and allow leaders to use that information to excellent effect. For example, the U.S. Navy currently uses AI and machine learning tools to predict part failures when considering the maintenance schedule of its aircraft and ships.
If AI can predict this specifically, there’s no telling what it can do for large-scale predictions or statistical datasets involving market research or large groups of people.
Deep Analytics Data Absorption
Last but not least, AI tools are better able to perform quality data analytics for “deep” or non-surface level data. AI tools can drive down into various data sources, silos, or structures. They can analyze the root patterns buried in a lot of data that might confuse human minds.
By digging into lots of levels of data simultaneously, AI-driven analytics can:
- Absorb more data than human data analysts
- Come up with more varied scenarios when addressing or predicting the cause of a problem
- Come up with better or more varied solutions for executives to consider
Artificial Intelligence is sure to be one of the cornerstones of the future data analytics industry. With AI assisting skilled human professionals, companies, government organizations, and even individuals will be able to make more accurate predictions by looking at complicated data sets, plus protect themselves against faulty and biased thinking.