Advanced analytics comprises of three elements or components. In fact, I think of it as an equation and it reads like this: Advanced Analytics= Techniques x Technology x Talent2. I square the Talent component, because if you get the talent right you have the ability to have an exponential impact on your bottom line compared to your competition
Let’s break down the equation and look first at the first component, Techniques.
Machine learning techniques are being developed today to learn complex decision making rules that traditionally took humans many years or decades to master through trial and error. These applications will eventually take on or supplement roles held by difficult to fill highly-trained individuals with complex job functions in claims management and even underwriting.
Our second T is Technology.
You need more than the ability to process millions of rows of data. You need the ability to link these millions of rows of data and resolve to a single entity whether it’s a person, business, vehicle, address or any other thing. Then, you need the ability to summarize these millions of rows of data into 1,000s of attributes.
Because attributes are core to predictive models, they “transform” the raw data into a form that can be efficiently analyzed (modeled). And you need a platform that can generate these attributes not only offline for model development but also on the fly to score quotes, renewals or claims
Finally, on to Talent2.
As I mentioned, I see this as the most important of the three Ts. Any business capturing large amounts of data will benefit immensely from employing data scientists.
I think it’s the most important component of the equation and requires two key inputs: firstly experience, and second, innovative thinking.
With the right experience set and an innovative mind set the output of your analytics can be exponential. Because just hiring lots of smart people won’t do much for your business. And companies who are natural innovators, who can challenge the data scientist, provide adequate tools and deliver other big data capabilities will attract the best talent.
In addition to the three Ts, data influences your analytics outcome. Today, data comes from many different sources with varying degrees of quality. For advanced analytics, data must be accurately and efficiently cleansed, standardised, linked to other related data, and fused together into a single holistic view of every risk. This results in decreasing content volume, but increasing content quality. Because the more you know about a risk the better you’ll be able to manage it
The potential of advanced analytics is enormous, and will continue to grow as the breadth and depth of data increases, as technology develops and the experience of data scientists grows further still. The only limit to this potential, beyond the compliance requirements, is the imagination of the data scientist.