Documents have come a long way since humans first painted animals on walls 40,000 years ago. A leisurely progression through stone and wax tablets, papyrus and paper sped up dramatically in the most recent 100 years, leading us to a digital world encompassing electronic documents and the internet.
Yet in spite of the internet’s embrace around the world, and the millions of databases crammed full of facts and figures about everything from our shopping habits to our health, a great deal of two-dimensional information still persists – flat data. From electricity bills and mortgage applications to invoices and delivery notes, “flat” documents, both electronic and physical paper versions, continue to exist in complete isolation from the rest of the digital world.
For businesses this is costing a fortune. Most documents are still processed manually, whether they arrive via email, post or electronic transfer. Customer or invoice numbers, along with names, addresses and other details, are assiduously transcribed from a document into a database, while a record of the document itself will be stored electronically, taking up storage space without offering any additional value. As recently stated by IDC, “Data, in the absence of meaning and context, is worthless and costly.”
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A vision of the data-based future
In the film Minority Report, we are given a glimpse into the way things might – and should – be. In one famous scene we see Police Commissioner John Anderton (played by Tom Cruise) flicking through archives of documents as he strives to catch future criminals before they perpetrate their crimes. Anderton is able to jump from one document to another, connecting images to information, placing them in context and following a logical train of analysis as he monitors his suspects. It is this context that allows Anderton to make quick, informed and highly accurate decisions. He is supplied with everything he needs to know in real-time and uses this insight to stay ahead of the curve.
In 2020 we have not quite arrived at this point, but we are taking some large strides towards it. Artificial intelligence is eliminating entire swathes of manual intervention in the processing of documents, and, more importantly, adding context to them. It’s not enough to simply scan a document and store it along with a reference number: the technology must be able to add meaning to it and to create links with other related data, structured or unstructured.
This type of technology falls into a category that we call Context Driven Productivity. At its core is the ability to extract information from flat data and transform it into semantic data, whereby links are created to other data sources, both internal and external, building relationships, connections and additional meaning. Semantic data allows humans or AI robots to gain contextual information automatically, rather than having to rely on a limited number of hard-wired connections.
In practical terms, the possibilities are enormous. Not only will administrative workers be freed from the tedious task of manually processing incoming documents, but the resulting context-driven data will be infinitely more useful to any organisation. When a mortgage request comes in, for example, it will be possible to link not only to a customer’s account details, but also potentially to their credit record, to the history of the house being purchased, or even the insurance details of the property.
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Levelling the playing field
Big data companies such as Facebook and Google have long been building these kinds of links from the information we share with them.
In the retail world, it surprises us more if a company doesn’t know what we bought last or the ages of our children than if they do. The next step is to empower the less glamorous back-office functions with the same level of functionality, and we need AI to do it.
As the use of artificial intelligence becomes more commonplace, two-dimensional documents will be automatically transformed into meaningful, relevant and connected information, creating an immeasurable improvement in the working lives of back-office staff. The end is nigh for flat data — as soon as we train our robots to put everything into context.