.....“If you cannot describe the difference between a taste of ice cream and a taste of gelato, it is obvious that you have never experienced tasting gelato.”
-Celent in Digital Insurer
So Ellingsworth uses this analogy to describe the difference between actuarial and data scientist roles.
"Productizing and monetizing insights into actions across the enterprise are the dividing lines between data science and traditional IT and actuarial organizations.
Organizing for success is taking a wandering path. Companies are learning how to learn, how to ask better questions, and how to source the mix of build and buy levers to use in different AI and data science strategies and deployments. Everyone is finding new appetite for more data, more compute, more impact, and more recursive segmentation and feedback loops for more accuracy with faster cycle times and smaller expenses.
Inevitably we resolve that these career paths are separable with distinct changes in how and where knowledge, skills, and abilities are applied. Actuarial - slower, regulated, embedded. Data science - faster, nimble, scalable.
Both are on the dessert menu, but best not served together. Essential and regulated tasks are not often the shiny, new, and sexy projects requiring bleeding edge skills, powerhouse computation, and new sources of exotic and even streaming data."
One issue impacts both professions. Inaccessible data.
The data hidden in data silos inherited over M&A history and often resulting in 15, 20 and more technology stacks. Data frozen in icebergs tantalisingly close and mostly hidden from view.
Data hidden in the volumes of unstructured data that wash up on the shores of document management systems, email inboxes and mail rooms. Medical reports, free-form text in web-forms, correspondence, PI litigation packs.
For the most part never surfaced and never analysed. Some enterprises rush into AI projects without even addressing this crucial matter of sunk data; sunk and never analysed.
It's a similar issue faced by police and tax investigators. Experienced professionals like fraud investigators with a nose for suspicious behaviour know the data is somewhere and if they could just find it and then analyse it- group, join, find patterns, test hypotheses.
Police, tax and insurers have found an answer in 360Retrieve- searches, finds and exposes the evidence hidden in all that unstructured and semi-structured data. Joins it with the well analysed structured data.
Lets all the professionals in these investigative organisations find the truths hidden away in years and years of data. Even the real-time data in today's insurance claims forms. The free-form text again in which many a deception hides away from inquisitive minds.
No more as many an insurer has addressed these serious issues with 360Retrieve.
And that still leaves the matter of gelato and ice-cream. Well worth going to the full article from Marty Ellingsworth - just follow the link below.
The list of impactful technologies is extensive and growing. Here is an active list: computer vision, natural language processing, speech analytics, text analytics, geospatial frameworks, machine learning, telematics, end-to-end experiences, IoT, digital distribution, AI, household and living situation models, customer behavior scoring, knowledge graphs, social network information, etc.
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