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08-31-2018 Blog: Analysis Versus Analytics

Analysis Versus Analytics

August 31, 2018 | Amit Ranjan

With the continuing prevalence of Big Data and the current proliferation of data sciences and insurtech, there’s significant uncertainty about what it all means and what it can enable. The insurance industry continues to seek and refine the promise of Big Data and modern insurance technology. Nevertheless, within that promise, the fundamental difference between analysis and analytics is often unclear.

In simple terms, the differences are these:

  • Analysis connotes a finite process of examination, conducted on a discrete set of data.
  • Analytics connotes an ongoing, methodological approach to extracting advantageous information and perspective from ever-changing and ever-expanding sets of data.

From such definitions, then, it behooves insurance consultants to help re/insurers practice analytics, rather than analysis, unless two conditions prevail:

  1. Those insurance consultants want there to be an actual end to their end-to-end offerings.
  2. Their clients’ target operating models are fixed and static, rather than adaptable and dynamic.

More Than Two Things Are Certain

An old axiom says only two things are certain: death and taxes. It’s pretty safe to say “change” should be on that list too. In any case, change becomes more apparent as its pace accelerates. And so it is that insurance consultants — especially those who practice data sciences and contribute to the insurtech movement — need to be ever-responsive to change, and ever-vigilant to the changing nature of data assets encompassing risk management.

As well, industry consultants must constantly develop their understanding of analytical methods by which they can support re/insurers to extract meaningful, actionable drops of information from the growing ocean of disparate data sources.

Clearly, the legacy environment of rigid technology, combined with one-dimensional information and analysis has come and gone.

Science Meets Art

Today, well-prepared insurance consultants will blend data sciences and insurtech — plus increasingly, blockchain frameworks— with a form of artistic creativity in working with re/insurers and their distribution channels to manage, model, analyze, and share volumes of data.

They’ll unite modern analytical techniques with intelligent technologies (machine learning, robotic process automation) to harmonize re/insurer data, processes, transactions, and platforms. They’ll develop and leverage insurtech tools to collaborate with re/insurers and experiment with a variety of predictive or prescriptive models. They’ll create credible “what-if” and “to-be” business scenarios — then personalize algorithms and analytic approaches, while reviewing the validity of analytically-driven metrics related to insurance markets, products, and operations. They’ll team with re/insurers to constantly assess the insights drawn from advanced analytics, to ensure the optimization of workflows and operating models.

Most importantly, knowledgeable insurance consultants, in conjunction with their forward-looking re/insurer clients, will recognize the relentless pace of change and the stimulus of analytics, insurtech, and blockchain to support change management. No business can afford the risk of assuming the insights or processes based on today’s market and operational data will be reliable in the weeks and months ahead. And every re/insurer’s operations should be informed by dynamic analytical discipline, versus static or one-dimensional analysis.

Increasingly, the formidable outcomes derived from data sciences propelled by progressive technologies allow re/insurers to be operationally adaptable in managing change and proactive in driving growth.

Learn more about Xceedance capabilities in insurance data sciences and analytics.

Amit Ranjan is EVP, global service delivery at Xceedance.