In May 2017 The Economist proclaimed "The world’s most valuable resource is no longer oil" yet data analytics remains a neglected aspect of many an innovation and transformation strategy. Take an industry founded on data analytics- INSURANCE.
Typically 80% of more of its data is today hidden away in data silos. Inaccessible, not valued and not analysed. The signs are that must and is changing.
"IDC" reckons that spending on big-data and business-analytics software will reach $67bn this year. But it will, boosters say, at last allow businesses to see the computer age in their productivity statistics, freeing them from the shadow of Robert Solow, a Nobel-prizewinning economist, who in 1987 observed that investment in information technology appeared to do little to make companies more efficient."
The Economist
How can insurers leverage the untapped value of that hidden 80% of data. Mostly unstructured it is found in incoming emails, medical reports, free-text comments in claims forms (even in modern automated digital claims processes) , annotated reports, images, recorded conversations.
Thy lack of analysis is one of the key reasons fraud is so often undetected and claims routed to the wrong teams. It is a key reason AI will never achieve its promise if the data is not converted from its raw and unjoined crude form to refined and joined format that is "AI Ready".
Is it necessary to license these new data analytics unicorns to start on that two or three year path to leverage AI?
No- there are companies that already incorporate in their platforms the data analytics capabilities described by The Economist. 360Globalnet's 360Retrieve accesses unstructured and semi-structured data across all these silos and from external data. It joins it up with the 10% to 20% data already analysed (typically structured) to give a complete picture of customers, events, trends and the key insights to make & execute better decisions. This is the first step to enabling predictive analytics.
In this case it is across the whole insurance claims value chain and answers the challenge described in the linked Economist article.
"Data analytics have a long way to go before they live up to these expectations. Extracting and analysing data from countless sources and connected devices—the “Internet of Things”—is difficult and costly. Although most firms boast of having conjured up ai “platforms”, few of these meet the usual definition of that term, typically reserved for things like Apple’s and Google’s smartphone operating systems, which allow developers to build compatible apps easily."
This in no way invalidates the case in The Economist that AI platforms like c3.ai and Databricks will make large inroads into large enterprises.
For now insurers need to take the first steps if they are to achieve the currently hyped benefits of AI by leveraging the data they already have, making it AI ready and then applying AI solutions like Fraud detection and mitigation.
Then put in place the overall strategy that includes long-term leveraging of AI across the whole enterprise. But remember it is a technology and not a solution.
at last allow businesses to see the computer age in their productivity statistics, freeing them from the shadow of Robert Solow, a Nobel-prizewinning economist, who in 1987 observed that investment in information technology appeared to do little to make companies more efficient. Just as electricity enabled the assembly line in the 19th century, since machines no longer had to be grouped around a central steam engine, data-analytics companies promise to usher in the assembly lines of the digital economy, distributing data-crunching capacity where it is needed.
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