Data Analytics - Assignment Example

Data Analytics s TABLE OF CONTENTS Introduction……………………………………………………………………………… 2 Applications of data analytics…………………………………………………………… 3 Advantages and disadvantages of data analytics……………………………………… 4 Data Analytics With Regards To Customer Satisfaction……………………………… 5 Conclusion……………………………………………………………………………… 6 References……………………………………………………………………………… 7 Abstract The business environment has proved to be highly competitive in recent times due to a continuous advancement in technology. The urgency for efficiency and precision in business dealings has prompted businesses to employ technology in all areas of operation to be on par with the market demands. Organizations are usually inundated with data, receiving terabytes and petabytes of it; received from various departments of the operation. From operational to transactional business systems, from management and scanning facilities; from outbound and inbound customer contact points and so on: information received in businesses is so immense that it requires equivalent ways of dealing with it. The explosion of data is not a new phenomenon; it extends back to the 1970s and today companies have to deal with big volumes of data. For instance, Wal-Mart is believed to handle over a million transactions of customers every hour and those transactions, estimated to be worth over 2.5 petabytes of data are imported into their databases. Further, Facebook handles over 250 million worth of photo uploads and a further 900 million objects from over 800 million active users every day. This is a huge data to analyze. Introduction Among these advancements of technology that businesses have employed in their operations is data analytics. Data analytics is the only criterion that such big volumes of data can be handled effectively. Data analytics is the science through which raw data is examined with the purpose of drawing important inferences from that information (Duan & Xiong, 2015). Data analytics is employed in many organizations allow the companies and organizations to make better and informed business decisions, and are used to prove or disprove certain assertions and theories. It usually focuses on the inferences, deriving a conclusion based solely on the information in question. Analytics is the realization or discovery and communication of some important patterns of raw data. Analytics usually relies on a simultaneous application of computer programming, operational research and statistics to help quantify performance of data. It should be noted that data analytics employs extensive computer programming and therefore the algorithms and software used in analytics are the most current methods in computer sciences, mathematics and statistics. Applications of Data Analytics Data analytics is usually extensively employed in areas of business like enterprise decision-making, market optimization, price and promotion modeling, credit risk analysis, store-keeping unit optimization and many others (Duan & Xiong, 2015). This study will take the example of General Electric as the case study, to discuss the benefits and the disadvantages of data analytic. General Electric Co. (GE) is well known for venturing in what is known as the big swings, meaning that it does not hold back in investing in new markets that it deems to have tremendous potential. GE, which is a manufacturer of jet engines, power plants and locomotives made the decision to enter in the data analytics and Big Data, and believed that data analytics is an ideal venture. The Connecticut-based industrial investment in data analytic meant that the torrent of all digital data that emanate from sensors and other digital devices that have been planted in GE’s jet engines, hospital MRI equipment, trains and turbines will be received in a central place. Harnessing such information and data would enable GE to help customers to identify maintenance-related problems before they actually occur as well as improving efficiency in fuel consumption and other improvements in operations that could give birth to trillions of dollars in savings (Davenport, 2015). And as the vice president of the company said, it was about making machines more efficient and intelligent as well as disseminating data to the right people at the right time. Advantages and disadvantages of Data Analytics Some of the advantages data analytics are discussed in this section. First, there is a massive cost reduction in business operations (Davenport, 2015). Big data technologies like the Hadoop and cloud-based analytics are critical in providing significant cost advantages. One may argue that comparing the traditional ways of data architecture and the data analytics is not fair, due to their variations in their functionality, comparing them in cost-wise presents the real picture of the order-in-magnitude improvements. Companies do not primarily invest in big data technologies to replace the old data architectures, actually, the augment them. Rather than the mere reason of processing and storage of data, data analytics look to augment the old data architectures. The long-term goal of augmentation of the two is the reduction of costs incurred. Secondly, data analytics has always attempted to improve on decision-making; businesses are looking for faster and more efficient ways of making decisions with big data and this has been realized by the used of analytics (Davenport, 2015). With the speed of Hadoop and in-memory analytics, GE can boast to have increased the speed of making decisions. Apart from GE, Caesars, which is a leading gaming company has long embraced analytics and it has begun using big data analytics for faster decisions. It has a program from which it derives its data about its customers. GE has used data analytics in improving its services for the customers like ways of enhancing efficient consumption of fuel for the jets and the locomotives. However, the most important advantage of data analytics is the propensity to bring up new products and services to customers. This has been the same for GE as new strategies for improvement of customer services has been pit in place. However, data analytics has got some challenges that all businesses employing it must deal with. For instance, lack of comprehensive approach to big data is a challenge that must be addressed well (The 4 Biggest Problems with Big Data, 2012). Moreover, getting the right information to the decision makers should be a priority to avoid companies from sinking in the humongous amount of information. Businesses do not have effective ways of turning big data into effective big insights and GE has also struggled on this front. Data Analytics With Regards To Customer Satisfaction With the ever increasing market competition, GE has prioritized customers in the utilization of data analytics and this can be proved by its decision to plant sensors in the engines of the jets and any other product that comes from the factory so that reception of information is made easier. As it has already been noted in this paper, this would be essential in helping customers with vital information on to curb mechanical problems of the machines even before they occur, as well as improving the efficiency of the machines for the sake of the customers. The CEO of GE announced in 2012 that the company would invest a whopping $1 billion to its analytics in the following four years which would make the company one of the biggest investors in the venture. With this substantial amount of investment, it represents only a small amount of down payment for GE’s $40 trillion opportunity by the year 2030. Using what it calls a rather conservative 1% saving on its customers, who include the aviation, healthcare, rail, oil and gas sectors, the estimates from an industrial internet for theses sectors are well over $ 300 billion in the next decade and a half. Taking the case of aviation alone, a unit percentage in fuel efficiency would be equivalent to $2 billion a year put into airline coffers (The 4 Biggest Problems with Big Data, 2012). There are many types of data that can be collected using data analytics, and it was mentioned in the introduction, data analytics is usually extensively employed in areas of business like enterprise decision-making, market optimization, price and promotion modeling, credit risk analysis, store-keeping unit optimization and many others. At this point market optimization would be a worthwhile area for the application of data analytics (CIO, 2013). This is because GE needs to gain the competitive advantage ahead of its competitors and data analytics is perfect for making this endeavor come to fruition. Researchers have found that automating the delivery of information and especially for processes that are primarily manually-intensive such as financial reporting can help the business maintain a business-as-usual environment and add a new dimension of competitiveness in the market (CIO, 2013). Conclusion The urgency for efficiency and precision in business dealings has prompted businesses to employ technology in all areas of operation to be on par with the market demands. From operational to transactional business systems, from management and scanning facilities; from outbound and inbound customer contact points and so on: information received in businesses is so immense that it requires equivalent ways of dealing with it. Data analytics is the only answer to this problem and therefore companies must purpose to invest heavily in data analytics if they were to maintain high level of market competitiveness and improve efficiency and precision in their dealings. References CIO,. (2013). Using data analytics to achieve competitive advantage. Retrieved 13 April 2015, from http://www.cio.com.au/article/457558/using_data_analytics_achieve_competitive_advant age/ Davenport, T. (2015). Three big benefits of big data analytics. Sas. doi:2015 Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal Of Management Analytics, 2(1), 1-21. doi:10.1080/23270012.2015.1020891 The 4 Biggest Problems with Big Data. (2012). Spotfire. Retrieved from http://spotfire.tibco.com/blog/?p=10941 Read More