Big Data Analytics
Management information system (MIS) is a computer based system which enables managers to conduct their operations in an efficient manner. MIS is usually used in a form of software that managers use to make decisions in the various organizations they work for. MIS enables managers to critically analyze the problems facing their organizations and enable them to generate solutions to those problems through the application of various software solutions. MIS has become very essential today. Organizations are going out of their way to invest in information systems because of the great benefits that these organizations are bound to reap from these investments. In a world where technology has evolved, it becomes inevitable for the organizations to adopt new technological solutions.
In today’s world technology is vital in enabling the company to implement its corporate strategies (Laudon & Laudon, 2012, p. 12). With this interdependence, many organizations have entered the market of software development in order to supply businesses with solutions to improve their performance. These firms have developed software that ranges from accounting, graphic designing, and procurement to data analysis. Palantir Technologies is one such companies which develops data analytics software. The use of big data analytics will result in enhanced operational efficiency.
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A firm in the health industry is among the many types of businesses that can benefit from data analytics software. The Guardian Life Insurance Company of America, for example, could reap benefits if it adopts the data analytics software developed by Palantir Technologies. The Guardian Life Insurance Company offers health disability, dental, and life insurance (Wright & Smith, 2004). Like any insurance company, the object of insurance has to be valued in terms of efficiencies in order to determine the amount of premiums to be paid. The process of valuation is one that involves analysis of various sets of data used to spot the insurance trends. The function is conducted by the company on a daily basis, and in bulk. Therefore, big data analytics software might be very useful for this insurance company since it compiles sets of data from the industry such as trends and the likelihood of default among others in providing mathematical solutions.
One of the benefits that the insurance company can reap from the software developed by Palantir Technologies is the reduction of costs of its operations. The Palantir Metropolis technology is a type of predictive analytics software that can help in detecting inefficiencies by the insurer. The part of satisfying claims by an insurance company proves to be the hardest part of all its processes. With the large amount of data involved, big data analytics is a critical component for the firm. Settling claims is faced with the risk of paying out for a fraud claim which reduces efficiency. This predictive analytics software might help the insurance company analysts to get insights faster to enhance operational efficiency. The predictive analytics software, thus, draws information from these databases and correlates them with given patterns and, as a result, the insurer can flag inefficiencies (Spann, 2014).
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The predictive analytics software might also be very helpful in enabling managers of the insurance firms to make decisions regarding ordering and prioritizing the operations of the firm. The claims triage, for example, is a decision tree management that enables the insurance firm’s managers to determine areas where there is wastage and to come up with solutions since the software pinpoints the exact places of inefficiency. The predictive analytics software plays the role of data consolidation since it brings out what may not be easily detectable by the analysts if they were using manual methods (Floudas & Pardalos, 2008).
The data analytics software might also enable the insurance company to improve its performance especially in aspects such as customer support and claim settlement. The proper utilization of data available to the insurers enables them to pay out claims in an efficient manner and ensures that the customer is reinstated and the process is finished within a reasonable period of time. On the other hand, the software ensures that the insurer does not suffer any losses arising from fraudulent claims. Therefore, the software also enables the insurance company to make profits and enhance efficiency by collecting information about clients faster and by enhancing the process of settlement.
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The Palantir metropolis software is used to connect to various sets of data from public, proprietary or commercial sources. Once connected to these sets of data, it is programmed to discover the trends presented in the data, the relationships, and anomalies arising from the data. The software is, therefore, suitable for integrating, managing and performing analysis on the quantitative sets of data.
The insurance companies highly depend on information and statistics in making the important decisions. Such aspects as life expectancy, diseases prevalent in the population, the likelihood of occurrence of accidents or even deaths, and general community health are important in determining premiums charged. This information is usually made available to the members of the public by the entities concerned (Czernicki, 2010). As a result, the Palantir metropolis software can be used to connect to such sources of information and conduct predictive analysis. The insurance provider will then make crucial decisions such as the amount of premiums to charge. Other sets of data that make public, such as lists of individuals who engage in insurance claims fraud, can be used to enable the insurance providers to avoid incurring losses. Therefore, the business functionality of the Palantir technologies matches the big data analytics needs of The Guardian Life Insurance Company of America.
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The methodology for implementing the Palantir metropolis technology will take place in different stages (Vreede, 2004).
The first stage is to examine the scope of the business requirements that is mainly the definition of the solution to the problems faced by the company. The software to be used is identified and the second phase of training the insurance providers is rolled out. The training is expected to be carried out in 2015, the first year of implementation. At this stage, the data mining functionality of the software is introduced to the insurer. This way the insurer will be able to gather different sets of data that they might need in their operations. Next, the software is configured with the systems of the insurer.
The data integration functionality of the software will be introduced in 2016 so that the insurer will be able to integrate different sets of data to draw relations between them. The following procedure is to conduct an integrated system test in order to determine the effectiveness of the software.
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The last stage of the implementation of the software will take place in 2017 implying that the insurer will accept the software and apply it in their operations. At this point, the data analysis functionality of the software is introduced to the insurer. This way they will be able to predict the future based on the data mined and integrated with the software. Palantir will afterwards seek feedback from the insurer on the effectiveness of the software.
The introduction of big data analytics software is important for both business health insurance providers and for analytics software technology firms. Firms that adopt this technology will enhance their profitability by enhancing their responsiveness to changing market trends, needs of their consumers and by enhancing their operational effectiveness. By adopting a structured framework of implementation, they will assess the software in order to determine its effectiveness. In the long run the adoption of such software will result in enhanced operational efficiency in the organizations that adopt it.