¶ … new technologies have given birth to data analysis from the IT backrooms, and have increased the possibilities of utilizing the use of data-driven results into every aspect of an organization. However, much as improvements in hardware and software have made the advent of big data usage possible, the only consideration is not technology. It is important for organizations to take very holistic approaches to integrate big data into every aspect of their business procedures, their daily operations, and strategies. Big data brings both challenges and opportunities to every business. To be able to obtain value for big data, it is important to carry out timely analysis of the data, and the result must be such that can influence important business decisions and bring about positive changes. Having the appropriate combination of people, technology, and processes determine the overall effectiveness. Key processes, roles, and functions are optimized by analytics. It can be built upon to aggregate both external and internal data. It makes it possible for organizations to manage large volumes of data, meet all stakeholder-reporting demands, develop important market advantages, improve controls, manage risks, and, eventually improve the performance of the organization by turning relevant information into intelligence. Analytics are able to identify inventive opportunities in major processes, roles, and functions. It creates the needed catalysts for change and innovation -- and helps develop new frontiers for both the business and its customers by challenging the status quo. Sophisticated procedures allow organizations to find out the root causes, carry out the analysis of micro segments of the markets, alter processes, and predict accurately about upcoming events or the propensity of customers to engage, churn, or buy (EY, 2014).
Claims Analytics, Optimization, and Fraud Detection
Analytics and big data can play very important roles in reducing the ever-increasing rate of fraud. In a study carried out by FICO in 2013, over 35% of respondents predicted that about 5-10% of the total claim is represented by fraud. 31% claimed that the cost could be as high as 20%. As these percentages keep increasing, a large number of insurers predict a sharp increase in losses due to fraud. Organizations should device means to discover suspicious claims, improve the effectiveness of investigations and prosecutions. They should also facilitate fast visualization and reporting to enhance continuing antifraud efforts. Analytics and big data can be a part of every fraud background to gain fast supplementary insights.
Customer Retention
Carriers can exploit maintenance efforts by signifying the next viable offer by gaining more knowledge of customers, their needs, and their tendency to churn. Analytics and big data go a long way to help decide whether a customer should be shown a new product or if the customer poses a retention risk to the company. The identification can be carried out while a call-center agent interacts with the policyholder.
Up-sell and Cross-sell
Carriers can advice the right policyholder on the aright action to take at any given time in order to utilize up-sell, cross-sell, loyalty and strategic lifetime value profitability. It can improve on analytics to enhance customer service deliveries, give solutions to service issues, understand the policyholder's motivation better, and improve customer satisfaction (IBM, 2013).
Issues Concerning Big Data Collection
Volume of Data
The data volume measures the amount of data an organization has at its disposal. The organization does not need to own every data it makes use of so long as they are easily accessible. As the volume data increases, different data records lose values about type, age, richness, quantity and other factors.
Data Velocity
Data velocity is responsible for measuring the speed of the data creation, aggregation and streaming. The richness and speed of data utilized in different business dealings have been rapidly increased by e-commerce (for instance, web-site clicks). Managing data velocity goes far beyond a mere bandwidth issue; it is equally an issue of ingest (extract transform-load).
Transport and Storage Issues
Each time we invent a new storage medium, the quantity of data exploded beyond such capacity. The only difference with the recent explosions-which can be traced to the advent of the social media-is that no new storage medium was invented. Additionally, everyone and everything creates data. A good example is electronic devices. So scientists, journalists, writers and other professionals are not the only ones creating data. Most disk technologies today come with a storage limit of 4 terabytes per disk. This means that about 25, 000 disks will be required for every 1 Exabyte. It will be hard to attach the required number of disks on a single computer, even if the whole data can be successfully processed with a single computer.
Management Issues
With big data, management will always be...
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