Would you choose where to go on vacation if you could only access 10 to 20 percent of the reviews and information on a travel website? If you do, you will probably have an unforgettable trip, but for reasons you might not like. Yet government organizations and businesses – from manufacturing to insurance companies, and healthcare to banking – are making decisions along this very same line. And they’ve been doing so for years. They look at the easy information they can get from structured data while ignoring their unstructured data, which Deloitte believes may account for80 to 90 percent of content generated globally, making unstructured data a tremendous source of untapped value.
Fortunately, advancements in AI (Artificial Intelligence) and machine learning now make it possible and affordable to sift through and find meaning in vast amounts of unstructured data obtained from video and audio files, emails, logs, social media posts and even notifications from Internet of Things (IoT) devices. All of this data can bring about enormous benefits, such as when used to automate tasks that are manually intensive and often highly repetitive. One task, for example, is to watch out for red flags: specific criteria or behaviors that may indicate something is amiss and corrective action must be quickly taken. Let’s look at a few cases from different industries.
How about an insurance claim that appears fine on the surface, but deserves to be investigated or, a job applicant who may be hiding information? What about a shipment of highly perishable pharmaceutical products that may not have been refrigerated for a portion of their journey, or a contract that may be in violation of a country’s laws or breaks an existing agreement with another company? The important thing is a red flag indicates issues that if left unchecked could cause great damage.
Artificial Intelligence is massively data hungry How does AI and machine learning enable more efficient and effective data analysis? Through feeding it data. By giving a machine learning model examples of good and bad transactions, it teaches itself to distinguish between the two types. And the more data the machine learning model processes, the greater it reinforces those lessons, enhancing accuracy.
So while AI and machine learning are making great strides, businesses and other organizations need to catch up. Think of it this way: data is like fuel. We need it to power our thinking in order to make wise decisions. But we’ve mined all the easy stuff, the structured data that arrives in nice and neat packages.
