Big Data Analytics is the termused to describe data sets that are large enough causing challenges when common infrastructure and tools are used in collecting, managing, and processing them (Sinha 103). Big Data Analytics concept examines large amounts of data in order to derive hidden correlations and patterns as well as other meaningful insights. The technologyused in big companies these days provide for quick data analysis and instant processing of information. The process was less effective and slower when conventional business intelligence solutions were usedfew years ago. Organizations have realized that if every data streaming into the business is captured, Big Data Analytics concept will helpin deriving significant value(Sinha 105).Big Data Analytics is generally transforming businesses by enhancing efficiency and speed. Although businesses could gather data and unearth information to make future decisions few years ago, Big Data Analytics these daysallows businesses to identify and derive significant insights for making immediate decisions. The speed, efficiency and the ability to remain agile givesbusinessescompetitive advantage they did not enjoy before.
In 2012, the America International Group (AIG) launched a team to oversee transformation into Big Data Analytics (Marr 54). The management recruited professionals outside the insurance industry who could challenge the conventional approach to decision making. The science team utilized Big Data Analytics in harnessing data and applied it in identifying new opportunities in the insurance industry. Using models and analytics technology, it wasable to make smart business moves that led to efficient business operations, high profits, and, happier customers. Cloud-based, Hadoop, and other Big Data technology resulted in significant cost reduction especially in storing large amount of data. Besides, the ability to identify efficient ways of carrying out business resulted in large cost reduction. The science team relied on Big Data Analytics technology in analyzing new data sources and makingimmediate decisions based on what was learned (Tanner 93). Big Data Analytics allowed the team to examine customer satisfaction and needs. By gauging the needs and satisfaction, the team was able to create new products that met the customer’s changing needs.
Big Data Analytics enabled the AIG science team to improve the outcomes by discovering more accurate intervention measures such as conducting special investigations and reviewing claims more accurately. This was an excellent example of fully embedding Data Analytics into the business(Tanner 99). The result was low cost, better predictions, and customer satisfaction. The mandate of the science team went beyond identifying insights to supporting learning processes and change across the whole organization. Since AIG relied more on brokers and agents to carry out business, the science team used Big Data Analytics in assessing relationships and priorities based on value, potential, volume, and overall effectiveness. Data Analytics technology allowed the science team to accurately predict the efficiency of every broker. On a daily basis, aggregated performance analytics were presented to the sales manager in a user-friendly format (Marr 61). The analytics helped the sales manager to make decisions on how to effectively manage the network of brokers and intermediaries.
Preventing fraudulent claims was a major focus for the AIG science team. They developed analytics models and proprietary tools for identifying fraudulent claims. The analytics tools identified twice as many fraud cases than experts could do. Big Data Analytics technology provided short-term, medium-term, and long-term solutions that potentially changed the scope and model of AIG business. Unlike other insurance companies, AIG was in a position to assessthe damages from accident claims using photograph analytics as well as measuring risk assessments with telematics and sensors. Therefore, Big Data Analytics enabled the AIG science team to make transformative, immediate and long-term evidence-based decisions(Tanner 101). High performance analytics allowed the science team to perform tasks that were previously difficult because the volumes of data were too big. The timely insights allowed the company to uncover numerous growth opportunities in the insurance industry. The ability to refine Big Data enabled the organization to effectively manage brokers and agents.
In conclusion, Big Data Analytics constantly enabled AIG to improve the decision-making process, operating efficiency as well as to reduce cost (Marr 70). The science team remained proactive in creating new opportunities and new products using Big Data Analytics. It constantly reconsidered the company’s business model and the business’s activity footprint using Big Data Analytics technology. In order to remain competitive, AIG relied on agile and quick decisions that were made using Big Data Analytics. Although Big Data Analytics potentially createdvalue for AIG, several factors were required to fulfill this goal; strong support from the management, tools, infrastructure to support big data analytics, and a series of activities to promote development and application of Big Data Analytics (Sinha 108). The activities included continuous supply of competent professionals within the science team and the efforts to promote collaboration between the team members. If the management of AIG continues paying attention to these factors, it stands a chance of reaping the benefits of Big Data Analytics in its decision-making, enhancing efficiency, and reducing operating cost in the foreseeable future.
Works Cited
Marr, Bernard. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary. , 2016. Print.
Sinha, Sudhi. Making Big Data Work for Your Business: A Guide to Effective Big Data Analytics. Birmingham, U.K: Impackt Pub, 2014. Print.
Tanner, John F. Analytics and Dynamic Customer Strategy: Big Profits from Big Data. , 2014. Print.