How analytics will drive the future

• Posted by Vincent Granville on February 8, 2014 at 10:30am
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Guest blog submitted by Marty Ellingsworth (martyells@aol.com), President of Verisk Innovative Analytics, a division of Verisk Analytics (www.verisk.com). 

Whether you’re steering an enterprise, championing an analytics program, driving a venture capital-funded data-modeling product or piloting your own consulting practice, there is no way you have not become aware of the promise of big data and bigger analytics.
Companies often deal with an opaque marketplace where riskiness is rated less accurately in some organizations than others. That said, some businesses, afraid of taking on new risks, stick to their own market niches and avoid new ones – preventing their abilities to be competitive in the larger marketplace. Others are blazing trails using newer technologies, data sources, modeling approaches and electronic connectivity to better manage risks in an increasingly mobile world. The following framework helps identify how companies are structured to compete on analytics.

Spectrum of Predictive Analytics Capabilities

Now we’re at the beginning of a long rally race of analytic improvements – from newer, better data to smarter, faster algorithms. Enhancements will include broader, more scalable platforms and will access unique sensors – spectra, spatial, temporal – and micro/macro levels of structured and unstructured data. All that will help generate insights into individualized, massively personalized and localized information while getting even more power out of grouped predictive parameters.
For example, if you have operated a car or other moving object, you undoubtedly have been assessed for your risk of loss as an operator of that vehicle and, in some manner, likely have been insured. In the past, that insurance-based risk assessment has blended wide bands of information on a few historically available generic characteristics to achieve a general-purpose estimate of prospective loss risk estimates.
In the future, that historical benchmark will be segmented into ever-more granular and accurate assessments. Those will then again be reinvented, recombined and refined to enhance the data-driven process, culminating in an adaptive analytic that adjusts expectations to the level of risk in each operating scenario encountered or intuited.
In the world of big data and bigger analytics, insurers will come to view vehicles as instrumented platforms and operators as real-time learners whose risk may change over time. Operators may drive safer and make smarter decisions about moving between locations, or they may permit distractions into their cockpit (such as texting, talking, smoking, eating and so on). How you drive, when you drive, where you drive and how much you drive are all becoming part of the context in your individual risk profile.
Figure 1: Companies vary widely in their abilities to create and use predictive information. There are seven stages of development for predictive analytic capabilities, and each has a level of investment and an expected return. The companies with the most mature capabilities will have invested in all seven stages shown in the illustration and, depending on individual jurisdictional restrictions, will have deployed analytic models to serve their customers and compete for others.
Some businesses apply detailed telemetry, routing algorithms, real-time weather and traffic alerts and driver/crew pairing models to manage more effectively the logistics of moving people, packages and pallets. Similarly, individual consumers make daily choices to move themselves, their passengers and their belongings along the same roadways and flyways and use all sorts of new navigation and alerting applications and devices to do so. (I’ve seen a mobile tablet computer go from a plane to a car to a sofa all in the hands of the same individual within one morning.)
Peering into the future, if a submersible helicopter car becomes commonplace, we’d have a truly three-dimensional driving experience. And on those journeys, we might need to dodge Amazon’s Octocopter self-driving delivery micro bots along the way.
In the ubiquity of an instrumented world, such a trend is unstoppable. Our challenge will be how we will use analytics to interact with decision-making. If consumers continue to make their own decisions, it’s a certainty that marketing analytics, advertising effectiveness and brand campaigning will merge into mobile and content messaging more than ever before. The next frontier will involve the layered sensing of the temporal and spatial context surrounding the customer.
Decisions that address emotional desires of customers resonate in the behavioral economics that underpin our financial world. The closer we can come to a customer’s desires, the better – and better still to be able to influence demand by making customers aware of opportunities they did not know exist. That holds true for business-to-business decisions as well.
The paradigm shift transitions from company-centric to customer-centric and from “we always have done ‘IT’ this way” to real time. That shift must be the focus of top management, which needs to take the offense and drive resource allocation for innovation and productivity to a customer-focused, real-time strategy. Executives who embrace such a process of optimization that both considers maximizing enterprise performance while minimizing risks will effectively revitalize every decision opportunity in marketing, production, distribution, logistics, operations, servicing and sales. And they will find there is no finish line when generating more shareholder value – only a continual cycle of improvement and a corporate culture of data-driven, sustainable excellence.