Business Intelligence Using Big Data for Growing Opportunities

Question: Discuss about the Business Intelligence Using Big Data for Growing Opportunities. Answer: Introduction Big Data is one of the fastest growing opportunities that are generating huge revenue of multibillion dollar (Wang et al. 2014). From different studies it had been found that the use case requires special combination of hardware and software services to have an effective effect of the technology (DeLyser and Sui 2013). The big data is the newly evolved technology and it is highly customable according to the needs of the industry. The report discusses about the analysis and application of big data in the health care industry. For the analysis the difference between online and offline big data is analyzed and different strategies adopted for selecting the right application that would suit the health care industry. The outcomes that are expected by the health care industry and the technologies that are involved in the big data solutions are also discussed. Lastly the impact of big data on the organization is discussed in the report. Difference between online and Offline Data Big data is the technology and initiative that includes the data that is diverse in nature or changing at a rapid rate. Big data is related with the creation of data, retrieval, analysis and storage which is notable in terms of velocity, variety and volume (Chen et al. 2014). The new technologies developed had made it possible to take in value from Big data e.g: the clicks of the user can be tracked in the ecommerce websites to identify the behavior of the user and improve the service of the website, pricing and stocks. The big data technologies can be of two types: Online Big data, and Offline Big data Online Big data Offline Big data Online Big Data systems offer operational competency for real-time and interactive workloads. Data is ingested and stored with no lag in time over online database servers (Sagiroglu and Sinanc 2013). Examples of online big data applications include real time advertising server, social networking news feeds, analytics tools and Customer Relationship Management tools. Offline Big Data systems include analytical capabilities for retrospective, sophisticated analyses that can load most of the data. Hadoop is an example of an Offline Big Data technology. Strategy to select right Big Data application The big data service is required to be chosen according to the need of the organization i.e. the according to the amount of data generated. The data can be used efficiently once the needs of the business organization are clearly understood (George et al. 2014). So it is essential to have a proper analysis on the current business process and select the right application best fitted for the business. The chosen application must be scalable and it should be capable of handling many types of data (Groves et al. 2013). The data of an organization goes on increasing day by day and thus the chosen application should give the option of further expansion to the storage space for organization (Letouz 2013). It should be noted that the performance of the application should not hamper after expanding the database. The application chosen should be open to handle a wide variety of data whether structured or unstructured in format. The security provide by the application should also be noted, the m ost of the products available in the market provides security in their own way but it is essential that the application must provide security from end to end. The data should be protected during its usage via online application and analysis. From the analysis it is found that the application of Hadoop would be best for the healthcare industry. Listed desired outcome from Big Data Solution Information investigation in health care is a blend of clinical development and innovation together. As the hospital industry is consistently creating a lot of information in various structures, it is verging on difficult to deal with this information over delicate or printed copy positions (Wu et al. 2014). According to the present trend there is a requirement to digitization the expanding information. Driven by obligatory prerequisites, the present era favors "Information Analytics". The Hadoop system underpins an extensive variety of social insurance capacities to enhance administrations and tackle issues in medicinal services segment (Murdoch and Detsky 2013). It is best suited for handling terabytes and petabytes of information, as a consequence of which, information examination gets to be less demanding. The analysis of the data in the healthcare industry can be utilized to bring the measures up such as: General Health: By breaking down the pattern of the disease of the patients and analyzing the records of sickness flare-ups, general health issues can be enhanced with examination approach (George et al. 2014). Extensive measure of information can decide needs, offer required administrations and foresee and keep the future emergencies to advantage the populace. Electronic Medical Record or EMR: The EMR consists of the standard data that are related with the health condition of the patients and the data can be evaluated using the approach of data analytic that helps in predicting the risk associated with the patients health and provide him effective treatment. Analysis of the patient record: the patient records can be analyzed with advanced analytical methods that would help the patient to identify the disease and the lifestyle the patient should follow. Genomic Analytics: This approach can be used to include the Genomic analysis as a regular process in the medical care system (Groves et al. 2013). Fraud Analysis: The insurance claim that is fraud can be analyzed and thus the fraud case can be curtailed down. The abuse, waste and fraud of the insurance can also be reduced. Safety monitoring: The large volume of fast data in the hospitals can be analyzed in real time and thus increasing the safety of the data and reducing harmful even prediction. Discussion on Technologies used in Big data solutions The topic Big data is vast and consists of new technology and trends and is constantly evolving at a rapid rate. Some of the technologies related with the big data are discussed below: Column-oriented databases- the row oriented database is the best suited for the online transactions because the update speed of the database in this method is much higher. This method may lack in performing the query if there in an increase in the volume of the data and the data becomes unstructured (Fan et al. 2014). But in case of column oriented database system the data are stored in the database keeping focus on the columns and thus works on huge volume of data without reducing the execution time. On the other hand it is much slower in updating the database. Schema-less databases, or NoSQL databases- Several types of database like key value store, document store that have main focus on the storage and recovery of large amount of data falls in this category (Wu et al. 2014). The performance of the database is increased by solving the restrictions such as consistency of reading and writing, scalability and distribution that are associated with the traditional database systems. MapReduce- This technology uses programming approach and is used to execute among thousands of database servers. The Map Reduce process consists of two tasks. The map task converts the input data into different types of value or key pairs. The reduce task combines the tuples and reduces the output of the map. Storage Technologies- There is an increase in the growth of the volume of the data and thus an effective storage technique is needed to handle the vast datasets (Minelli et al. 2012). There is an evolution in the data storage applying the technique of compression and virtualization of data. Sky tree- It is a data analytic and machine learning platform that have focus on handling Big data. It is very useful because it uses machine learning platform which is essential to explore the massive volumes of data and manual exploration or automatic exploration process is much expensive if used to handle large volume of data. Hadoop is an open source platform and works on the Map reduce technology. Hadoop is efficient to work using multiple data sources and process large scale of data (Lohr 2012). It is flexible to work with different application where the data are constantly changing such as social media, traffic sensor data etc. SQL like bridge can be used in hadoop to make BI applications query in the Hadoop cluster (Fan and Bifet 2013). Hadoop requires a high level knowledge of the developer for the implementation of MapReduce technology. The websites can explore and use the user information to provide real time response to the user such as recommendation, personalization and taking decision (Riggins and Wamba 2015). Several web analysis tools work on Hadoop and this technique is called Wibidata. Business impact of Big Data Deploying Hadoop in the health care industry would have a positive impact on the business like Optimized customer service and treatment- using the patient data available from different hospitals and policlinics the patients can be benefited and the healthcare organization can plan faster to provide the appropriate treatment (Letouz 2013). Save money through accurate data- the data analyzed would be accurate and thus can avoid mistakes, delays and miscalculations and the here the application of Hadoop can improve the accuracy and quality of the data (Fan and Bifet 2013). Better treatment based on analysis of data- The application of Hadoop would facilitate research into specific topics where a huge volume of data is available. Improve position by providing quality and services- the current position of the health care organization can be seen and it would increase the treatment speed and success rate of each treatment and also help in researching on a typical disease (Chen and Zhang 2014). Optimization of the insurance through treatment analysis- The client information regarding insurances can be analyzed for providing treatments of medical institutions and thus most effective treatment and use the data to save money can be applied in the treatment process. Organizational impact of Big Data Hadoop have a large potential to contribute in different areas of the Health care industry. At the moment there are some good initiatives, but this is not good enough to keep up with the demand of the healthcare services and the rising costs (Chen and Zhang 2014). Relative improvements can be: Electronic health records (EMR/ HER, or EPD) which serves the patient. Structuring data and information for service optimization. Accurate information about the patients can reduce mistakes Cost optimization through efficiency of new e-health system services Increased customer satisfaction Analysis of big data sets for RD purposes Utilizing huge information is both a mechanical and vital issue. Other than cost adequacy the social insurance division needs to accomplish enhancements in blending information from numerous sources. Gaining knowledge from their information would change the way they interface with the patients, contenders and the business sector through information driven basic leadership (Fan and Bifet 2013). However the healthcare industry would not accomplish the critical worth accessible from Hadoop without radical changes in controls and framework wide incentives. Accomplishing those progressions would be troublesome yet the potential prize is great to the point that the medicinal services officials and the strategy creators ought not disregard these open doors (Riggins and Wamba 2015). Huge commitments can be normal from information mining and investigation. Conclusion The report states the application of Big data in the health care environment. The big data have opened an attractive job opportunity and it acts as a technology driver and increase in investment in the service. But it is a new technology and is still in the development stage and has rumors that the business which adopted the technology earlier has faced lots of problem adopting the technology. The difference of online and offline data is discussed in the report and the right application is chosen for the application of big data in the health care industry. The technologies available in Hadoop that can be utilized in the health care industry are also discussed in the report. The impact of the application on the health care industry and the management of the process that would help the organization are also discussed in the report. References Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data.Information Sciences,275, pp.314-347. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact.MIS quarterly,36(4), pp.1165-1188. Chen, M., Mao, S. and Liu, Y., 2014. 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