In one of the recent blog I published What Insurance companies could do to save others from eating their lunch, I have stated the importance of good data management as one of the essential component for business growth. Big data fits right into that alley.
What is big-data?
In ideal scenario, big-data definition change from case to case. But, to summarize Wikipedia does a good job: In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, storage, search, sharing, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”
Why is it important for insurance and why should they care?
Insurance companies have been gathering data (both structured and unstructured) for years now. So, the current big-data landscape fits their bill well. Insurance companies could use big-data to analyze their data and learn great deal of insights to service customers better and help differentiate them from competitors. Here are a few used cases that should motivate insurers from embracing big-data in their arsenal.
1. Â Â Â Â Do linkage analysis of structured and unstructured data: Insurance companies have been collecting data for eons. This data is placed in either structured or unstructured form. Before the age of sophisticated analytical tools, it was nearly impossible to comb the data for any further insights considering the amount of effort and cost vs expected outcome. Thanks to big-data, a lot of tools have emerged that are well capable of doing that task with minimum resource requirement and promise great outcomes. So, it should be taken as an opportunity for insurance companies to look deep in their data silos and process them to find meaningful Â correlations and insights to further help business.
2. Â Â Â Â Use public data from social web and scoop for prospect signals: Another big area that has been unleashed by sophisticated big-data tool is capturing social-web and searching it for any meaningful keyword, and use it to understand the insurance landscape. For example, consider looking for keywords that are utilized to describe oneâs business and see how much you lead that space. There are many other used cases that are super critical to insurance and could be solved by big-data tools.
3. Â Â Â Â Use data from social web to spy on competition: This is another powerful used case being used by many companies to better understand their competition, their brand perception and their social media footprint. It is done by sniffing on public web activity for competition and further analyzes the findings to learn more about competition. Real-time nature of the data makes it all the more interesting keeping information current and real-time.
4. Â Â Â Â Sniffing and processing all the product interfaces for insights: This is another big area harnessed by big-data tools. Due to superior analytical skills, big-data tools could also help in providing real-time insights from data collected from all the product interfaces. Whether it is verbal(call-center logs, queries etc.) or non-verbal data(logs, activity report, market conditions etc.). Once an appropriate model-framework to consume that data is build, big-data tools could get to job and start real-time analysis of customers, sales and provide invaluable actionable insights.
5. Â Â Â Â Big-data for data driven innovation: I have been a strong advocate for data driven innovation. Data driven innovation is innovating using the power of data. Once appropriate modules are identified that could advocate innovations, their information could be then processed and monitored for any correlations with business critical KPIs. Once a direct link is established, tweaking the process and monitoring its impact on the system and quickly help in understanding the areas for improvement. So, this module could be used to create innovation and promote lean build-measure-learn loops for faster learning and deployment. This will drastically reduce the execution cycle for testing innovations.
I am certain that there are numerous other areas, in which insurance could pitch in. Feel free to share your thoughts in comments.