Human error in data analytics, and how to fix it using artificial intelligence
The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations.
The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what’s common among these errors is that they are related to humans reading, processing, analysing, and interpreting data. Artificial intelligence (AI) can effectively combat human error by taking up the heavy lifting involved in parsing, analysing, drilling down, and dissecting impossibly large volumes of data. It can also perform high-level arithmetic, logical, and statistical functions at a scale that would otherwise be impossible by human-led, self-service analytics alone.
Below are five of the most common human errors that can be eliminated using AI:
It’s easy to spot a yellow car when you’re always thinking about a yellow car. Confirmation bias impacts the way we search for, interpret, and recall information. In the business world, gut instinct quite often trumps data, and data is manipulated, omitted, misrepresented, or misinterpreted to concur with one’s own beliefs. And in cases where data doesn’t concur with beliefs, the information is faulted and disregarded. Artificial intelligence eliminates this way of cherry-picking data; it uses historical data to look for trends, patterns, and outliers, providing accurate, bias-free results.
Lockheed Martin, one of the world’s foremost aerospace companies, uses historical project data, also called dark data, to manage its projects proactively. By correlating and analysing hundreds of metrics, the company was able to identify leading and lagging indicators of program progress, predict program downgrade, and increase project foresight by 3 per cent.
Far too many organisations struggle with data-related issues such as organising multiple sources of data, a lack of collaboration between data sources, low data accuracy, and poor data accessibility. Artificial intelligence can easily break silos by communicating with and correlating large data sets from several applications, databases, or data sources using relational data modeling techniques.
