Other parts of this series:
Most insurers are pretty aware of the opportunities made possible by big data and have made initial investments in this area. However, we are also seeing them take important steps to address key challenges and make good use of these important investments.
We hear it. We read it. We know it: We are observing that the value of companies is now more and more “tied” to the amount of relevant data they can manage. The more data and better quality data, the greater the opportunity to properly assess risk up and across sales opportunities and the more value companies can generate from this data. The “big data revolution” is playing a major role in the digital transformation of the insurance business, which is, by its nature, a “risk to value” business. Data-driven products. Connected cars. Connected homes. Connected lifestyle. The resulting customer insight means value, but it also means huge amounts of data to manage.
For a deep dive into big data opportunities for insurers, take a look at our blog: Steps for insurers to pursue better data management and measurement.
Studies of big data have concluded that insurers should keep their eyes wide open and look after the quality of the data they manage. To keep competitive in a digital economy, they should trust nothing but the data.
Big data means different challenges
We know Formula One cars require different driving skills and maintenance than our good old family car. And the same is true for insurers.
By driving the “big data car” that uses predictive modeling, an insurer can more accurately assess a customer, highlight ongoing anomalies and propose a policy that better fits the customer’s budget and needs. This can become a real growth and value opportunity.
The ability to analyze in real-time, dynamic data coming from different channels—like telemetry, GPS, wearable devices, social media and weather forecasts—and matching it to customer portfolio data, represents a dream for any insurer.
But to get there, we might need some additional understanding and training around big data management. Here is one of the lessons insurers will need to learn. We’ll look at the other two lessons in the next post on this topic.
Preparing the team: Data governance
Data governance is a set of controls and monitoring processes every company should live by to maintain the availability, integrity and security of data assets. This is based on two pillars: Data Quality and Roles and Responsibilities:
How can data quality improve insurers’ performances?
Data quality measures the adequacy of data to support business intelligence and organizational decisions. It covers accuracy and consistency (data integrity) of the data characteristics, from definitions and lifecycle workflow to business rules and relations, as well as different media storage (data warehouse, data mart).
Data quality is a key metric for insurers. High quality data provides appropriate consistency, a crucial characteristic for high quality statistical calculations and actuarial models, through which insurers improve their business and results analysis, and respond to regulatory requirements.
In a big data context, data quality acquires particular importance, as it represents the principal prerequisite for a high value analysis and use of data. Unfortunately, most insurers are not giving as much importance to data quality assessment methods and frameworks as they should.
Who is responsible for data and what are the roles?
Big data management efficiency stems from both strong data governance and concise definitions around data guardians and their roles and responsibilities.
Data governance is not the only lesson insurers will need to learn. In the next post, we’ll consider two other lessons.
For more information, view our presentation: A New Approach to Data Management in the Digital Era