The Role and Influence of Big Data in Organisations - Essay Example

The Role and Influence of Big Data in Organisations Introduction Big data Big data is a catch phrase that has been extensively used to describe large and complex volume of data that cannot be processed and evaluated based on the available traditional data analysis methods. Handling of big data by corporations leads to a number of challenges, which include their capture, duration, storage, search and even analysis (Kuiler, 2014). Traditionally, large sets of data are a common phenomenon in specific government or private sector departments tasked with the analysis of big set of information. For example, the centre for bioinformatics information or the genomics library, the department of meteorology and the connectomics services are considered as examples of big data sets. The growth of data sets into the big data category is attributed to increased ubiquitous data gathering activities by different sources and stored within one central point (Brown, Court & Willmott, 2013). Big data analytics Big data analytics on the other hand is a process of collecting, organizing and evaluation of big sets of information in order to make specific discovery common in the data collected. Data analytics is used in different fields including biological research, business and meteorology and its assist in making conclusive information based on reliable data sets. In business, big data analytics is essential in the process of understanding specific information from a given market environment which will guide present and future strategic business decisions (Kuiler, 2014). The analysis of big data is done by qualified data analysts who sought to identify the specifics in the data that can be of use to the organization or business. Business across the globe attempt to device ways of ensuring that they improve the performance of their organisations based on the set of information they derive from databases. What make this set of data unique are its structure and the level of market representation that it has at any given point in time. The manner in which an organization will employ this set of data has the potential to open success doors to business in massive ways (Brown, Court & Willmott, 2013). The presence of this set of data does not make it conclusive, but the way in which business make sense out of it. Making sense out of big data can be done through different data analysis techniques that provide ground for the understanding of this set of information and using it for various business related purposes. Businesses have adopted a trend, which points towards the desire to dig out hidden threat, or patterns which are essential for improving their performance and the quality of their products in the market. Why big data? The fuzz about big data within the business circle has increased considerably since 2012 with outsiders wondering the potential that it holds for the success of the businesses. Through big data, business has an opportunity to make radical changes within its operational and management structure that is essential for its success in the 21st century. Companies that incorporates latest technological and data analytics discovery have the opportunity to capture essential insights into the behaviour of the market (Goes, 2014). The appropriate use of dig data has proved to be an essential differentiating factor of a business that is responsive to emerging information from its competitors that do not. according to a report by the Vanson Bourne, the rate of using big data by businesses is yet to pick up though the trends points to great future for business that embrace big data analytics. In this paper, the influence and challenges facing organisations that use big data in their operations will be discussed (Waller & Fawcett, 2013). Organizational use of big data Advanced and simple analytics of big data has significant impacts on the success of businesses if they are used to develop sound business models. By using the information from the big data; for example, businesses can develop statistical models that can predict the future behaviour of consumers based on the past patterns. This is based on the scoring model that is derived from predictive analysis of the information found from the big databases. The predictive analysis of big data has four benefits to business and presents avenues that when explored, can improve the performance of businesses in the face of stiff competition. Prevention and remediation of financial fraud With the growth of global business entities, data mining and selling has increased among unethical business executives across the globe. For example, three Chinese citizens were recently charged for digging into private databases of large multinationals based in the United States. This demonstrates the level of cyber fraud that provides leeway for the initiation of financial fraud and corruption that can affect the performance of business and give their competitors the leeway to control the market. The new approaches developed by criminals, which they have successfully used to defraud companies points to the need to safeguard financial and strategic information of businesses (Gilenson, 2014). Big data provides businesses with an opportunity to shift to powerful and secure analytics to save their massive volumes of information and uncover hidden patterns, trends and activities that can be deemed suspicious in the face of competition. Through the analytics of big data, fraud detection has been enhanced through the process of analysing specific trends in the transactions and orders that are fit for further review. In most instance, increase in information and data stored within the traditional data storage approaches increases the loopholes for fraud (Goes, 2014). For example, SAS, a leading manufacturer in consumer electronics faced the challenge of stopping massive losses while using its previous data infrastructure. To eliminate the losses and increase the effectiveness of its operation, the company needed to install a new data management approach that could assist in the process of fighting fraud. In its previous infrastructure, the company had a number of servers but only dedicated a single one to the detection and prevention of fraud (Hsinchun, Chiang & Storey, 2012). The company thus faced limitations in its desire to compute its resource and improve the performance of its backup system. However, the transition from the departmental disconnected data analytics system to the use of enterprise scale data handling approach sealed the security loopholes that were available in the company. The adoption of this big data approach enabled SAS to connect its grid computing, the manufacturer’s fraud and the IT team, a process that provided room for effective collaboration and fraud detection. This example demonstrates that the adoption of big data analytics approaches enables businesses to enhance the process of detecting and eliminating fraud, thus preventing business losses (Waller & Fawcett, 2013). Risk calculation on large loan portfolios The management of consumer home loan portfolios is an essential practice for financial institutions whose desire to protect their investments and eliminate default loopholes remains paramount. In 2008, lack of proper approach towards the assessment of risks available in providing substandard loans led to a credit crunch due a massive credit defaults. A number of organisations were closed due to improper business practices and the failure to develop safeguards while issuing subprime mortgages to people with unknown credit history. There is need to develop sound models that can be run against the massive data volumes and access essential credit information on potential market (Goes, 2014). Most institutions developed approaches that enabled them to aptly capture market information but lacked adequate storage facility for such massive information. This created a number of risks and time wastage due to prolonged processing period to financial institutions that led to increased losses. High performance analytics based on big data is essential for financial institutions to increase their speed of information collection and advancing of loans. The companies have also increased their ability to make a conclusive assessment of the risks involved in such consumer mortgage portfolios (Hsinchun, Chiang & Storey, 2012). Within 84 seconds, a financial institution can be able to generate information on the dynamics of the targeted market and make decision on whether to proceed with issuing the loan portfolio or not. apart from evaluating the risks involved within a short period of time, the adoption of big data in the management of loan portfolios also provides opportunities for business to massively save money that would have otherwise been used for traditional and time consuming data mining (Gilenson, 2014). Big data and high value marketing The level of marketing approaches they develop and the sound abilities of such approaches to respond to real market issues and information influence the performance of business. Among financial institutions, the need to reduce fee-income related losses has created a strong competitive frontier where performance is only determined by the nature of information one organization possesses. A marketing campaign that targets millions of potential customers leads to a massive increase in information and data collected which can be redundant unless well organized (Brown, Court & Willmott, 2013). The maximization of customer lifetime value from a large set of customer information can only be done if the information is efficiently organized and use to meet the needs for which it was collected. The adoption of high performance big data analytics enhances information collection, analysis and use in the process of database marketing. With adequate information access and use, the productivity of marketing teams is increased and the impact is felt by the growth in market share controlled (Hsinchun, Chiang & Storey, 2012). Platform requirement for big data Before the adoption of high-speed big data analytics, businesses must make changes in their computing systems in order to develop platforms that the new approach can operate on. Three key platforms are critical in the adoption of big data analytics in all types of organisations that handles massive data set from the market (Capriotti, 2014). Linear scalability The development of linear scalability platform enables a business to analyse massive sets of data at one specific time with a speed that is more improved and reliable. The adoption of big data analytics should not force business systems to adapt to the new data, but instead to expand its analysis through innovative approach to data mining. With scalability, multidimensional analysis can be performed on the data to the ‘nth’ degree, which allows the business to adopt a wide variety of dimensions in fine-tuning its use of information. Linear scalability enables businesses to overcome challenges associated with localized analytics such as identifying the business drivers within the local or global business environment (Goes, 2014). Low latency The development of low latency data platforms enables a business to develop improve and speedy access to information, which also increases the process of decision-making. By reducing the time between data identification and data availability, operational analytics of small and large business can be realized. Low latency provides platforms for businesses to continuously exploit data feeds such as the trickle feeds, which reduce the process of decision making in business transactions. In flight decision making within the organization is also improved significantly with the development of low latency platform. Such decisions include the development of marketing campaign budgets and the adoption of online marketing platform to reach to the global market (Bottles, Begoli & Worley, 2014). In-database analytics This is the final platform for businesses to develop before embracing big data analytics in the formulation of strategic marketing and management decisions. In this platform, the performance of big data analytics approach is determined by the ability to integrate massive and granular sets of data within the business operations systems. In-database analytics includes the use of multichannel attribution analysis, which can be employed in the evaluation of the impact of credit sales on the daily operations of the business. The customer churn database can be used in the evaluation and prediction of customer’s behaviour basing the conclusions on information such as payment patterns, impacts of friends and peers among others (Gilenson, 2014). Criticism of big data paradigm shift Despite the benefits of big data approach to business, critics have poured cold water on its relevance in the operation of businesses and the attainment of competitive advantage. Either two groups of critics have emerged who believe that in one way or the other, big data is not a right approach or the way it is used is not right. First, lack of information on the source of the empirical microprocessor used in the development of typical networks that form the big data has raised a number of questions. The microprocessor properties of the big data analytics are based on assumptions that analysts like Snijders and Reips continue to doubt (Grossman & Siegel, 2014). The pressures by companies to implement this front of information and data storage have continued despite lack of technological advancement and knowledge on the part of employees. To overcome the challenges associated with lack of employee skills, an article in the Harvard business review posited that big data must be complemented by big judgment. The analysis of information in big data is also influenced by information that existed in the past and the present, which may not hold absolute control over the progress in the future (Grossman & Siegel, 2014). Biological and health sciences use data that are based on pure experimentation and that can be verified through various approaches to confirm or refute the hypothesis posted. Today, biological and health researchers have agreed that information derived from big databases are complementary if no hypothesis is available to be tested (PR, 2014). The inability to test the hypothesis of the biological and health data derived from the large volumes data makes their applications limited. Individuals who advocate for privacy have also raised concern over continued adoption of big data due to the threats it presents to privacy issues. Critics have also argued that the analysis of big data sets is shallow when compared to the thorough approaches given to small sets of data (Bottles, Begoli & Worley, 2014). Conclusion The discovery of big data has provided significant opportunities for business to enhance their data collection, storage and security of the information. However, traditional architectures and infrastructure used by organisations cannot adequately support the development of big data unless significant changes are implemented. As it is today, information technology experts are facing significant challenges due to the massive requests for data and off reports from customers and suppliers (Bottles, Begoli & Worley, 2014). However, the opportunities presented by big data analytics has enabled businesses to implement decision with speed due to the haste in getting information and answers to strategic information. The data stored within the servers are large and the question of timely availability to policy and decision makers remains the prime concern. Going through millions of information from various sources presents a number of challenges to managers and information technology experts. Lack of adequate technical expertise also makes the benefits of big data analytics slim, as organizational employees are less equipped to address the emerging challenges and issues in this emerging trend (Bottles, Begoli & Worley, 2014). Recommendations The success of big data analytics to businesses can only be realized if structures, architectures and infrastructures are developed. The dimension of big data analytics makes it more challenging than anticipated and this makes it essential for business to prepare ground for its implementation. First, information technology employees in the organization must be trained and prepared to handle the dynamic data environment. This will eliminate time wastage that has hampered its implementation in certain quarters and organisations. SAS is a better platform for acquiring information on how to make big data analytics a success despite the high number of challenges (Goes, 2014). The adoption of big data analytics approach by organisations can only succeed if a comprehensive and considerate approach is adopted. Such an approach should factor in a number of issues which are aimed at making the new approach acceptable across the organization. It must be guided by the desire to create customer centric outcomes that are aimed at increasing the business value. This can only be achieved through initiating the analytics with the customer outcome analytics (Bottles, Begoli & Worley, 2014). The big data blueprint should also cover the entire enterprise and connect different departments and business entities. Through a common enterprise, challenges affecting the operations of the business will be identified from a common ground and this enables the development of a more harmonized response approach (Goes, 2014). The development of big data analytics platform must also begin with the currently available data to create coherence among the users. This is because the users will be able to identify the primary information stored and differentiate it from other data forms within the system. Business priorities must also be synchronized and skills investment done by the organization to enhance the application of this new form of data analytics in the organization. Finally, approaches must exist that allow for measuring the outcomes of the new system and gauging its success in the face of the emerging issues associated with the system. References Bottles, K., Begoli, E., & Worley, B 2014, Understanding the Pros and Cons of Big Data Analytics, Physician Executive, 40(4), 6-12. Brown, B., Court, D., & Willmott, P 2013, Mobilizing your C-suite for big-data analytics, McKinney Quarterly, (4), 76-87. Capriotti, R. J 2014, Big Data Bringing Big Changes to Accounting, Pennsylvania CPA Journal, 85(2), 1-3. Gilenson, S 2014, Why It Big Data Needs Its Own Analytics? Siliconindia, 17(8), 34-35. Goes, P. 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