Better Data Part I: Be specific
The insurance industry collects vast amounts of data as part of its business operating model, yet progress in the area of data governance has been slow. We examine the tangible benefits of improving data management, showing that an enterprise-wide data strategy drives operational efficiencies, meets regulatory commitments and allows insurance firms to make data-driven business decisions based on holistic and consolidated views of information.
Earlier this month, GFT was a sponsor for Guidewire Connections, an annual conference which brings together insurance companies and service providers to discuss some of the industry trends in regards to technology (and of course, the Guidewire platform). One of the key topics that came up over and over again was the importance of data. This is hardly a surprise; it seems, nowadays, that you can’t have a conversation about the future of any company without discussing data. In some ways, the widespread attention to data is a great thing for companies and for consumers; the potential benefits of better understanding your customers and their needs are obvious. However, there’s also a danger to the ubiquity of ‘data’; as more people talk about the ways in which they can harness data, the term is beginning to lose meaning, going the way of corporate buzzword fluff with “leverage” and “synergies.”*
What seems to be missing from the conversation is specifics – what data should you be collecting? How should you be qualifying and auditing this data? Which processes will be put in place to ensure data quality and regulatory compliance? Why is this data valuable, and what insights can actually be gained?
In order to answer some of these questions, we’ve decided to write a multi-part mini-series which attempts to look at these specifics through a variety of real-world lenses. As we move through the series, we’ll transition from industries that are just beginning to really see the value of data through to more developed markets where data governance is reaching maturity.
Insurance and data
To begin, let’s look at the insurance industry. From its start, data has been the basis for the insurance industry. From assessing the risk that a ship will sink on its voyage to the New World, to the development of actuarial science to predict the mortality associated with your life style and habits, insurers have found a business advantage in being at the forefront of incorporating data into their decisions and management of their business. The exponential increase in the availability of data from a seemingly infinite ecosystem of sources in recent years has made the job of the insurance data practitioner a challenging one, as the imperative to mine knowledge and insights from this data and to use these insights to transform their business accelerates.
Additionally, this is a market that arguably stands to benefit more from intelligent data than most; insurance companies are in a unique position, in that their assets span multiple asset classes and liabilities do not typically follow any simple, industry-defined model. An insurance company’s assets are their investments, which take the form not only of bonds, but also of alternative investments, real estate, derivatives, loans, Corporate Partnerships, Structured Equity and Leveraged Leases. Their liabilities are in the form of policy payouts, which are also highly variable and depend on a number of company and client-specific factors. Therefore, even the most foundational task of matching assets to liabilities to ensure that you have a sustainable business model relies on intensive collection and analysis of data for insurance companies.
Yet, investment in data management has been slow within this industry. This also isn’t particularly surprising. Investing in data can seem like a trip to the dentist; you know it’s necessary and ultimately good for you, but you also know it’ll take longer than you like and cost more than you want to spend (0 minutes and 0 dollars, respectively). The process of setting up sustainable data governance – of creating data taxonomies, consolidating warehouses, implementing enforcement controls – is tedious, to put it nicely. The financial services industry has been forced to start taking formalised steps in this area as regulatory legislation like BCBS239 has mandated data-intensive reports. While the insurance industry also has regulatory pressures on their assets, their liabilities are not subject to the same laws. As a result, enterprise-wide data governance has only just started to develop.
Benefits of strong data governance
To make the case for moving data from the abstract world of “strategic innovation as a competitive advantage”** to an actionable business case, the authors, bring to bear their extensive experience working in Business, Data and Application Strategy in the Life and Property and Casualty sectors to put together this (non-exhaustive) list of benefits to better data governance: (read on here)
* The authors acknowledge their own guilt here, and vow to use these words with less impunity.
** The authors, again, are sure we’ve used this phrase a number of times!