Expert Commentary

Whetting the Appetite for Analytics—A Recipe for Success

A good analytic model implementation or good analytic strategy is like your favorite meal. Thinking through such a pleasurable experience might help put you in an open frame of mind.


Big Data and Predictive Analytics
June 2016

Close your eyes and think of one of your favorite meals. Take in all your senses and imagine why it's so high on your list. What does it look like? What's in it? How does it make you feel before you eat it, during the meal, and after the meal? What's the main ingredient? What other ingredients make it stand out? Do you like the ingredients? Who would you share it with (their own portion, of course)? To whom would you recommend it? Is this a special occasion meal or would you make it a regular staple?

The Appetite for Analytics

Think about what goes into making a good meal.

  • The ingredients need to be familiar to the diner. Tofu just doesn't work for some people. Crickets (note Andrew Zimmer)? There are degrees of familiarity within our habitual walls.
  • The ingredients need to blend together and complement each other. Balance of flavors is critical. Some of these combinations may seem unusual to the average person.
  • The meal should be served the way the diner is used to having it served—corn on the cob or cut off the cob and served as kernels?
  • Should there be changes to traditional dishes (Rueben sandwich with hummus or Hatch chilies)?
  • Are the ingredients and preparation considering a healthy lifestyle? Are they vegan or gluten free?
  • Is the food preparer a food lover who is passionate enough to please those who will be eating what is prepared? Is the preparer open to substitution based on allergies, geography, phobias, religious beliefs, etc.?

Chef de Cuisine

Meal preparation professionals know these requirements from training and experience. Most of them probably burned a loaf of bread, made a soggy batch of rice, or undercooked a nice cut of meat at some point. But they also go through several career steps, gaining insights from mentors and coworkers along the way.

Similarly, analytic leaders, including C-Suite management, who are championing an analytics culture, have to be aware but often don't get an analytics mentor/student experience. Perhaps some in management came from a quantitative background. But more than likely, leaders probably haven't had the complete experience of conceiving the meal, creating the recipe, assembling the ingredients, or doing the meal preparation. Without assistance or experience, the diner might just shuffle the food around the plate before ultimately pushing it aside. There may be poor Yelp or Urbanspoon reviews. The worst would be an unpopular and then closed restaurant or unanswered dinner invitations by friends and relatives.

Recipe for Success

The planning and recipe is a blueprint for success of creating anticipation of needs being met, a pleasing experience during the meal, and the resultant primal comfort in having had the experience. It's even better if the diners express their wish for an opportunity for a return visit. The mere recall should make their mouths water.

Like the perfect meal experience, the analytic experience can be thought of in the same light for your organization. Everything is prepared for and used to fit the ultimate business purpose—to make better decisions, make them faster, and make them at less cost.

  • Was the strategy well communicated from the beginning? (Was it taste-tested in advance?)
  • Is the process driven by relevant data that is consistently available, and does it make intuitive sense for the purpose (just like the ingredients of a recipe)?
  • Is the strategy and tool "owned" by the users of the process? (Did they get to decide what's served and with which side dishes?)
  • Is it understood by the affected downstream entities who are incorporating the processes? (Is there transparency as to how the meal was prepared (no secret crickets)?)
  • Do all stakeholders have clear and well-communicated returns on investments to users along the process chain? (Are the ingredients healthy and nutritious?)
  • Are the purpose and scope clearly defined by management? (The chef loves cooking as much as eating.)
  • Are the analytics complementary to the overall business strategy? (Does white wine pair with prime rib?)

Humalogy™

Humalogy the word used to describe the blending of humans and technology. (See http://www.fpov.com/humalogy-overview/.) Most processes and actions involve some human effort that is aided by technology. The trick is figuring out how much human effort versus technology effort needs to be used to maximize the efficiency of the action.

This is becoming a real challenge for insurers as they strive to interject analytics into the decision process. One school of thought is to bring new levels of skill to positions, such as underwriters, claim adjustors, or even in-house agents. Insurers are gearing up to train their current staff to be data scientists, whereby the person is expected to understand how to sort through complex arrays of data and indications as they make decisions they're used to making based on experience and traditional risk characteristics, particularly in the medium to large commercial lines accounts.

New studies indicate underwriters will be expected to rely more on data-driven insights and less on traditional underwriting theory, consuming data through analytics to be more efficient and providing bandwidth to enhance their roles. What underwriter wouldn't want to fill the role of sales executive, decision scientist, customer advocate, and innovator? Do such skill sets exist in our underwriting teams? If not, what is the cost and ramp-up investment to get them there? Do they want to fill this new style of underwriter? Will the next generation of underwriter arrive at insurers' doorsteps with all of these skills in hand? Does the recipe for more edible analytics support this trend?

Chief Cook and Bottle Washer?

These new underwriting skills or needs sound much like those of a data scientist. In 2016, some studies indicated the job position of data scientist was the best job to have. The pay is lucrative, and good data scientists could write their own ticket to nearly any company where they might want to work. Being the most coveted, the role has a pay scale to match. The requirements of a data scientist include being adept at understanding data, versed in the nuances of analytical techniques used, and a master at data visualization to communicate what the data is telling them. All this needs to be paired with deep insurance domain expertise for such skills to fit the demands of underwriter along with the data scientist role.

How do we strike a balance? Can a work colleague be successful or be proficient in all the skills of the maître d', chef, waitperson, sous chef, wine steward, dessert waiter, dish washer, and cashier? One could argue this type of person is more of a unicorn—able to juggle both technical and human skills.

Citizen Data Scientist

Maybe the future of underwriting and the use of analytic tools won't be such a revolution with skill requirements being as technical as some think. With new machine learning and your computer, most of us can be self-taught data scientists. Almost anyone can find the complex interactions of variables and the relevant predictive variables (and combinations) using Natural Language Processing (NLP) that is now being rolled out.

NLP connects human communication to the computer using voice synthesizing, voice recognition, and past interactions to understand what the user is seeking to understand. Therefore, unlike a data scientist having to be versed in using analytic tools such as SQL, R, or SAS, the problem can be communicated and understood by the computer and then set on its way to provide various inputs based on what the data says. Skeptics of the potential reality of these technologies should ask Wilson (of IBM).

Food for Thought

My mouth waters every time I sit down to write or edit this paper. It waters not only because I love good food, but also because you can call me late, but you cannot call me late for dinner. I just follow my nose and my gut when challenged with providing advice on implementing analytics into different parts of the organization.

There's a lot to think about and several decisions to make as a company to develop a data-driven culture. Thinking through the entire meal preparation process can help. You can rest assured, if you don't deeply consider all facets of the journey, it will eat your lunch.


Opinions expressed in Expert Commentary articles are those of the author and are not necessarily held by the author's employer or IRMI. Expert Commentary articles and other IRMI Online content do not purport to provide legal, accounting, or other professional advice or opinion. If such advice is needed, consult with your attorney, accountant, or other qualified adviser.

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