How to make a Data driven decision?

Concepts such as Big Data and Artificial Intelligence are crying out for attention in (trade) media. But what is their true value? The answer: Better decisions based on hard facts, often made in real time. This is also an important competitive weapon for insurers. The world of Formula 1 offers valuable lessons. Above all, it shows that this transformation does not stop with the implementation of a piece of technology - just like Formula 1 drivers, managers sometimes have to set aside their gut instinct.

During training sessions and races, the several hundred sensors on a Formula 1 car pump an impressive amount of data through to the team. That data is of vital importance in a world where the differences, especially between the top teams, have become extremely small. Insiders have known for years that this data (can) make the difference between winning and losing. So, what can insurers learn from this?

Data as the Norm

Matt Cadieux, the CIO of Red Bull Racing indicated in an interview that no less than 30,000 major and minor changes are made to a car per season, to optimize its performance1. These changes are based on insights from (measurement) data.

Former world champion, Sebastian Vettel, was even somewhat mournful about it. According to him, telemetry is starting to play too dominant a role in sport. "If, in the past, someone was faster on a corner because he ran his own line, he kept his lead throughout the entire race weekend. Now everything is so transparent that the engineer is able to propose a better line2." The machine is then, on the basis of the available data, better able to master the corners than the driver himself.

Like it or not, the growing role of data in decision-making is an irreversible development that is becoming increasingly clear to an ever larger audience. An amazing sign of the times was the victory of Max Verstappen on the circuit of the Brazilian Interlagos in Autumn 2019. Afterwards, the Red Bull Racing team boss pushed Senior Strategy Engineer, Hannah Schmitz, on stage with Verstappen for the traditional champagne moment. She was derisively portrayed by the Dutch commentator of Ziggo Sport as the "Data Girl"3. The reality was that, as Senior Strategy Engineer, Hannah Schmitz had played a crucial role in the victory by advising an extra tire stop during a safety car situation at the end of the race. 

This seemed like a daring move; the stop forced Verstappen to hand over the lead to Mercedes driver, Lewis Hamilton. The Dutchman was able to catch up with the six-time world champion in the final laps on fresher tires.

Added value for insurers

This is no different in business. Marketers used to determine the pricing strategy of a product or service using their gut feeling but are gradually being outsmarted by algorithms. These algorithms feed on data from a multitude of sources, internal and external, and are able to predict which strategy produces the best results. Prices can then change from minute to minute and customised solutions can be tailored to client, without the involvement of human thought or action.

Insurers

Insurers are also increasingly taking (real- time) decisions based on data, but this appears to be quite a struggle for many. Consultancy BCG writes in a (recent) paper4 that the insurance market is mainly about pilots and experiments, but that it is not yet possible to consistently retool the decision-making process.

Algorithm is Key

"The only way to realize the full potential of data science is to use it to systematically shape the what and the how of decision making, redefining roles and reshaping organizational cultures." Incidentally, this not only provides greater customer satisfaction, but also more profit: 'Insurers that use data in a systematic way have found that its promise isn't a mirage. They've improved their profit margins by between 200 and 400 basis points. "

An example, according to BCG, is the monitoring of the Net Present Value (NPV) of a customer. With the right data you can measure this per-customer relationship and start targeting it in a more focused way. The data provide insight into, among other things, the terms and the embedded value of their policies and the potential for cross- selling. With that insight, insurers can make better decisions about issues such as, allocation of capital and (customized) actions in the field of sales, marketing and customer experience.

Think Big, Act Small

But, it’s not all that simple - because it’s about (much) more than introducing a few new applications. It is a major change process. This and more is evident, from an analysis in the KPMG magazine, Compact, of the transformation of insurance company, VIVAT5. The authors, a team of KPMG and VIVAT professionals, state that it is a combination of thinking big on the one hand and making progress (in small controlled steps) on the other, because you simply cannot expect an organization to suddenly become entirely data-driven overnight. 

Data-driven Decisions

The evidence for this comes from, among other things, an example where it turned out that, with data analysis, it was possible, with a high degree of accuracy, to pinpoint fraud cases in an insurance portfolio. The analysis revealed no less than 150 clear cut cases, but this also resulted in a much higher workload for the departments involved, so VIVAT decided to start with only a small selection. The hope was that these small successes would whet the appetite of those involved, so that enthusiasm for this kind of data-driven approach would gradually grow.

The success of such transformations is therefore almost always embedded in the human factor. At VIVAT it appears that they still have to overcome their initial apprehension. Even though it is clear that the use of data yields meaningful results, it is still far from being a given. "Business managers are still in doubt about whether the data algorithms are in use are effective, even if they have been proven to be so. (...) “Effort is still needed to make them trust the data and their outcomes."

Better Information = Better Decisions

It is a very human response to react with some suspicion to such developments, certainly if one's own function changes - or is threatened as a result. Decisions are increasingly the result of data analysis and people play only a marginal role in this, or, at least, play a very different role; a different reality arises in decision- making.

Just as Sebastian Vettel experienced, first hand, the way in which systems are influencing the motor racing profession (a change he apparently finds difficult to come to terms with), so top managers will also have to become more humble and bow to the superior decision-making of data-fed systems. This will, of course, take some adjustment, because they are used to their experience and intuition playing an important role. 

Gut feeling

If you look at the cold, hard facts, you will see an unequal battle between man and machine in this regard, mainly because the machine can handle a much greater complexity. Moreover, the human approach is fundamentally flawed. Take, for example, "group think" in teams and other forms of bias, and you can find a wealth of scientific research that calls into question just how good human decisions actually are. This research6 shows that most of our decisions are not even made consciously but are made by the subconscious mind. It is therefore very simple: The machine makes better decisions than humans.

Daniel Kahneman draws this very conclusion in his book, Thinking Fast and Slow. He concludes that in uncertain and unpredictable domains, the human being loses out. 7 We must therefore convince managers not to always trust their own judgement, and to be somewhat humbled by what machines can do. Andrew McAfee, one of the authors of The Second Machine Age, pointed this out years ago in an article with the headline: Big Data’s Biggest Challenge? Convincing People NOT to Trust Their Judgment. 

In Short, he Message

Therein lies an important message for insurance managers. They shouldn’t allow themselves to be driven to distraction by all sorts of new technological hypes, but, instead, should investigate how they can arrive at better decisions at all levels of the organization and in every conceivable role. They will also have to acknowledge that even the best manager must, occasionally, put human intuition and experience aside and follow the machine - just as Formula 1 drivers must occasionally recognize their superior in the form of a Data Girl... 

1 https://netsotech.com/assets/documents/simplivity_caseStudy_redbull.pdf

2 https://www.spiegel.de/plus/sebastian-vettel-wuenscht-sich-fuer-seinen-ferrari-ein-schaltgetriebe-a- 00000000-0002-0001-0000-000163724175

3 https://autobahn.eu/artikel/55434/datameisje-van-red-bull-is-superstrateeg-die-race-voor-max- verstappen-won 

4 https://www.bcg.com/publications/2018/rewiring-decision-making-insurance-data-science.aspx 

5 https://www.compact.nl/articles/is-data-the-new-oil-for-insurers-like-vivat 

6 https://www.sciencedaily.com/releases/2008/04/080414145705.htm

7 On an impressive number of sites: “The longevity of cancer patients, the length of hospital stays, the diagnosis of cardiac disease, and the susceptibility of babies to sudden infant death syndrome; economic measures such as the prospects of success for new businesses, the evaluation of credit risks by banks, and the future career satisfaction of workers; questions of interest to government agencies, including assessments of the suitability of foster parents, the odds of recidivism among juvenile offenders, and the likelihood of other forms of violent behavior; and miscellaneous outcomes such as the evaluation of scientific presentations, the winners of football games, and the future prices of Bordeaux wine.