How Insurers Derive Value from Big Data and Analytics
For insurers to stay ahead, learning how to derive value from big data and analytics is key
Few technologies have transcended the insurance industry like big data and analytics. Big data and analytics offer a major competitive advantage for insurers, and insurers who are slow to adopt big data technologies forfeit their chance to stand out amongst the competition.
Big data and analytics offer a multitude of benefits for insurers. According to Property & Casualty 360, “Analytics can be applied across the insurance value chain to generate highly relevant real-time insights and spark decisive actions. Insurers must access and act on the wealth of new data available for better, faster and more informed decision-making at all levels of the enterprise. To support adoption and usage, organizations must also integrate analytics with decision engines to apply contextual factors and deliver actionable insights to the front lines.”
While the benefits are nearly endless, insurers need more than just a big data environment to derive valuable business insights. Insurers need an end-to-end big data platform designed to handle not one, but rather multiple steps from data acquisition and preparation to data analysis and operationalization.
Validate Data Quality
Deriving value from big data starts with ensuring data quality. Ensuring data quality can be an arduous task. Insurers need to stay diligent and consistent with their data accuracy efforts in order to chart a path forward that leads to eliminating potentially egregious errors. Therefore, insurers need a standardized, auditable, and automated end-to-end data quality framework to remain vigilant and catch errors, which can be done with a proper big data platform.
To eliminate data errors, the big data platform adopted by insurers should perform data quality, balancing and reconciliation, file monitoring, and process validation on both big data and traditional data environments. The platform should easily process high volumes of data and the results from data profiling, consistency, conformity, completeness, timeliness and reconciliation checks should be output into dynamic reports and case management workflows. Once insurers have quality data, the next step is understanding that data.
Institute a Data Governance Framework
Data can be a significant asset for insurers, but it can also be a liability. In today’s world, data is being amassed, disseminated, and deployed at a record pace. That’s why it’s more critical than ever for insurers to implement a data governance framework in order to understand their data from a business context and quantify the quality of their data.
In order for insurers to understand their data, the platform they choose should deliver an all-inclusive view of their data landscape to combat the increasingly complex demands around regulations and compliance, and the shifting tides of business policies and business alignment. The solution should allow insurers to easily define, track, and manage all aspects of their data assets, enabling collaboration, knowledge-sharing, and user empowerment through transparency across the enterprise. The next step now is for insurers to derive insights from their data.
Analyze the Data
Once insurers have quality data and a data governance framework they can then analyze the data to make faster, smarter business decisions. To analyze, the big data platform should apply machine learning algorithms with intuitive drag-and drop functionality on a visual canvas blended with data preparation and operationalization capabilities. This allows insurers to conduct ad-hoc analysis, segmentation, classification, regression, recommendation, and forecasting across many nodes for faster execution.
With an end-to-end big data solution, insurers eliminate the need for multiple tools and are given an all-inclusive view of their data landscape. This allows insurers to easily utilize sophisticated analytical algorithms without coding experience in order to jump start analytics projects.
To learn more about deriving value from big data, download this data sheet.Download the Data Sheet