Human error in data analysis and how to fix it using artificial intelligence

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The benefits of analytics are well documented. Analytics has helped organizations transform retail experiences, map train and truck tracks, discover extraterrestrial life, and even predict disease. However, in recent years, organizations around the world have been plagued with the amount of human error that has permeated their analytics attempts, often ending in disastrous results. Whether it’s crashing spacecraft or sinking ships, transferring billions of dollars to unintended recipients, or causing deaths from drug overdose, human error in data analysis has considerable ramifications for organizations.

The reasons for human error in data analysis can be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or all-too-common biases in data interpretation. However, what is common to these errors is that they are related to the reading, processing, analysis and interpretation of data by humans. Artificial Intelligence (AI) can effectively combat human error by taking on the heavy lifting of parsing, analyzing, exploring, and dissecting incredibly large volumes of data. It can also perform high-level arithmetic, logic, and statistical functions on a scale that would otherwise be impossible by human-directed self-service analytics.

Below are five of the most common human errors that can be eliminated using AI:

Confirmation bias
It’s easy to spot a yellow car when you always think of a yellow car. Confirmation bias impacts how we seek out, interpret and recall information. In the business world, instinct quite often trumps data, and data is manipulated, omitted, distorted, or misinterpreted to fit one’s own beliefs. And in cases where the data does not agree with the beliefs, the information is skewed and ignored. Artificial intelligence eliminates this way of selecting data; it uses historical data to find trends, patterns and outliers, providing accurate and unbiased results.

Lockheed Martin, one of the largest aerospace companies in the world, uses historical project data, also known as dark data, to proactively manage its projects. By correlating and analyzing hundreds of metrics, the company was able to identify leading and lagging indicators of program progress, predict program downgrade, and increase the project forecast by 3%.

Inability to break silos
Far too many organizations struggle with data issues, such as multiple data sources being organized, lack of collaboration between data sources, low data accuracy, and poor data accessibility. Artificial intelligence can easily break down silos by communicating with and correlating large sets of data from multiple applications, databases, or data sources using relational data modeling techniques.

Recently, several Indian state governments decided to collaborate with the National Green Tribunal on the Elephant Project – to assess and prevent the death of elephants on interstate railway lines – after The Hindu, a national newspaper, published a report highlighting the timing, frequency and common routes in which elephant deaths frequently occur. The newspaper was able to prepare this report by collating data from the railways and forest reserve departments.

Minimize losses
It’s human nature to be loss averse. Toyota has minimized the impact of faulty brakes in its cars, leading to the removal of certain Toyota models from Consumer Reports’ list of recommended vehicles. BP played down the impact of the oil spill in the Gulf of Mexico by running fancy ads apologizing for a ‘minor spill’, until it received a backlash from the president of the Barack Obama, who said the company should have used its public relations budget to clean up the overthrow instead, at the time.

Minimizing loss creates tunnel vision and prevents leaders from making effective decisions. And in the long run, it can be costly for the organization. Due to the analytical DNA of artificial intelligence, it understands and interprets data as it is and does not favor positive trends over negative ones, indisputably eliminating the human tendency to favor positive outcomes. This makes AI-based analytics an ideal ally for leaders looking to make decisions based on complete facts rather than a partial picture.

Inflated predictions
Another downside of human-led analysis is the habit of presenting inflated predictions of the future. Whether forecasting the organization’s budget needs, predicting property damage after a natural disaster, or forecasting budget deficit or inflation rates, humans tend to inflate forecasts based on their own assumptions and experiences. On the contrary, AI-based analysis tends to be more accurate because it makes predictions based on driving or stopping forces and external or environmental stimuli. The US Navy relies on artificial intelligence and machine learning to proactively predict part failures and plan preventive maintenance for its aircraft and ships. This allows sailors to spend more time focusing on missions and less time fixing aircraft when they break down.

Inability to go beyond surface-level analysis
Analyzing the root cause of problems can put companies light years ahead of others that don’t follow such practices. Root cause analysis can identify the agents causing a problem, suggest corrective actions, and provide ideas for preventing such problems in the future. But with too many data sources, structures, and silos, it becomes impossible for humans to gather, analyze, and drill down to perform root cause analysis. AI-powered analytics can circumvent these obstacles by effortlessly exploring multiple levels of data simultaneously. Additionally, the AI ​​can also overlay several possible scenarios to find the most likely cause of a problem.

This is the age of AI
The benefits of AI-powered analytics are many, from providing actionable insights in minutes to eliminating errors or bias in self-service analytics. Now that more and more business leaders are turning to AI for insights that propel their business, we can expect to see growing adoption of AI in analytics in the Middle East and the world.

Sailakshmi Baskaran is the Analytics Evangelist at ManageEngine

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