What is Big Data Analytics For The Insurance Industry?

Big data analytics in the insurance industry is the process of examining or inspecting large amount of insurance data or data sets, that contain a variety of insurance data types.

What is Big Data Analytics in Insurance Used For?

Big data analytics is insurance is used to reveal hidden patterns, correlations that are unknown, trends in the insurance market, preferences of insured customers, and other important insurance business information. The findings can lead to new revenue opportunities that were not seen before, better marketing to specific insurance markets, operational efficiency improvements, and the most important... Help you gain an advantage over your insurance firm competitors

What is the goal of big data analytics for insurance agencies?

The main goal of big data analytics for insurance agencies is to help insurance companies make better decisions in general. Data analytic brokers, aka data scientists, aka predictive modelers, help your insurance firm analyze big volumes of data including transaction data and other forms of data that may have been overlooked by typical BI (business intelligence) programs. This type of data could include anything from internet clickstream data, web server logs, and social media activity/content, to text from emails, call detail records, and machine data captured from the internet sensors. Some think big data is the same as unstructured data or semi-structured data, and that statement is partially true in some cases, but full-service data analytic companies also include transaction and structured data to be a main component of a successful big data analytics application.

How is big data analyzed?

Big data is analyzed with high tech software tools that are specifically used in conjunction with an advanced analytic discipline such as: data mining, predictive analytics, statistical analytics, and test analytics. Data visualization tools and business intelligence software also play their own roles in the analytic process., but unstructured and semi-structure data may not work well in data warehouses based upon relational databases. Also, data warehouses sometimes cannot handle the demands of big data that is updated frequently. For example, tracking real-time data about the performance of gas or oil pipeline mobile applications. Due to this fact, many companies are turning to a newer version of technology for big data that includes Hadoop and other related tools like MapReduce, YARN, Hive, Spark, Pig and NoSQL databases. These technologies are the core of the open source framework that processes diverse or large data sets across disorganized systems. In few cases, NoSQL and Hadoop cluster systems are used as a form of landing pad or staging area for big data before it ever even gets input into a data warehouse, usually presented in a form that is more conductive to relation structures. Big data vendors are now pushing for the concept of a Hadoop storage repository that is used as a storage like facility top store organizations streams of raw data until they are needed.  With this in mind, subsets of data would be able to be filtered for analytics in analytical databases and data warehouses, or it could be directly analyzed in Hadoop using stream processing software, batch query tools and SQL on Hadoop technologies that run ad hoc queries originally written in SQL. However, big data also has the potential to cause some confusion insurance companies who lack internal analytics skills. Also, there can be high costs associated with hiring expert big data analytic professionals who cater to insurance brokers/firms. The large amounts of data and information that's usually involved, and the variety of it, can cause data headaches, including consistency issues and data quality. Furthermore, integrating data warehouses and Hadoop systems can be challenging. Although, many vendors now offer software connectors between relational databases and Hadoop, as well as a list of other data integration tools that have big data abilities.

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