The Use Of Data Analytics Tools For An Organization

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

Big data and its definition has changed over the years. In a 2011 research project by MGI and Mckinsey’s Business’ defined big data as

Businesses today have access to significantly more data than any other time in history; however, most businesses are not capturing or using the data effectively. A report by the Aberdeen Group, “The Executive’s Guide to Effective Analytics,” indicates that “44 percent of executives are dissatisfied with the analytic capabilities available to them today, and that they often make critical decisions based on inaccurate or inadequate data” (Forbes, 2014). Luckily, CEO’s are beginning to recognize the need for analytics and more and more businesses are making a shift towards a data-driven business culture.

Can big data analytics be used by management accountants to provide a better understanding of a business’s position and outlook?

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

Every organization, be it a booming corporation, a start up non-profit, or even a national football league team, is comprised of a plethora of data. Although data has always been important to an organization, now more than ever it has become a critical part of their performance. With continuously advancing technology becoming available for companies to use, the amount of data accessible can seem almost endless. Figuring out how to manage this data, along with what to do with it can be a daunting challenge. This is where data analytics comes in. By simple definition, data analytics is the science of using the raw data collected to come to conclusions to make, hopefully, successful business decisions. There are many different facets of data analytics, and each facet can be uniquely important to an organization’s needs. Most data collected can be divided into one of three subgroups that each build upon the previous: descriptive, predictive, and prescriptive.

Big data is not as new as many people believe it to be. It is actually a concept that has been around for almost a century. It is just the “same old data marketers have always used, and it’s not all that big, and it’s something we should be embracing, not fearing” (Arthur). In 1944, Fremont Rider “predicted that the amount of data in the world would increase exponentially” (Hopp). Rider was right on target with his prediction seventy years ago. Data has grown much greater than he probably could have ever imagined back then.

In this paper, the author will attempt to define data analytics in general and provide a brief overview of the evolution of utilizing data analytics in business. Next, analyze the main advantages and disadvantages of using data analytics within the industry or company that you have chosen. Determine the fundamental obstacles or challenges that business management in general must overcome in order to implement data analytics. Next, suggest a strategy that business management could use to overcome the obstacles or challenges in question. Provide a rationale. Analyze the overall manner in which data analytics transformed the industry or company you selected with regard to customer responsiveness and satisfaction. Speculate on the trend of using data analytics for the chosen industry or company in the next ten (10) years. Next, determine at least one (1) additional type of data that one could collect by using data analytics. Provide a rationale for the outcome.

analytics beyond commerce to healthcare strategies, hiring processes, and personal life (Bayan, 2015; Germann, Lilien, & Rangaswamy 2012; Tavana, Kennedy, & Joglekar 1996). Regardless of the industry, analytics have legitimacy absent context and provide insight to the working of a structure. Organizations could, and often dp argue, that awareness of the power of analytics is enough; however, for analytics to be of the greatest support, their merit and worth is not in the capture and analyzation of data but in the application of it within a specific context. Analytics provide insight that can be

Through this paper, we will attempt to understand what constitutes ‘Big Data’. We will explore some of its sources and discuss some of the barriers faced by organizations looking to benefit from this phenomenon. We will also examine the various management tools and statistical techniques that can be used to extract information from big data.

Understanding what big data means is really simple.” It is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it” (Big Data Analytics).Big data is being produced by everyone and every day that finding ways way to manage this data is becoming a challenge. It arrives from multiple sources or touch points such as websites, social media or apps on smart phones at a high velocity, volume and variety. “All kinds of technologies or approaches including mobile devices, remote sensing technologies, software logs, wireless sensor networks, social media etc. are used by organizations to collect big data. (issue, 2013)” Now that the meaning of ‘big data’ is clear, it’s important to know that this information is useless unless it’s processed properly with the right tools. To extract meaningful value from big data companies spends fortune; it requires optimal processing power, analytics capabilities and skills.

The importance of analytics in big and small companies could not be overestimated. Every organization uses big data in order to manage their processes and improve its business. The term of "analytics" is commonly defined as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive predictive models" (Davenport & Harris, 2007). Analytics appears to be a useful tool, which helps to gain necessary information and provide extensive support to all departments in the enterprise. Nowadays analytical innovations play a big role on the market as they help an organization to gain a competitive advantage and become a market leader. According to the researcher Svetlana Sicular, analytics helps the companies to organize big data and divide all the information into relevant and irrelevant. In such a way organization receive an opportunity to process this data and deliver necessary information to a customer faster and more efficient. Furthermore, new tools and applications help to build up a reliable channel of communication between clients and a support team (Wagner, 2016). That is why almost all the companies are constantly trying to improve their analytical methods in order to attract more customers and investors to their business. The examples of such organization as Netflix Inc., Harrah 's Entertainment and Amazon show that analytics could help an enterprise to develop in its field and gain a strong

Analytics is defined, according to online-behavior.com, as the “process of obtaining an optimal or realistic decision based on existing data.” [1]. Davenport and Harris (2007), defined analytics, as the “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” [2]. An article titled Outsmart the Competition by Jackie Zack in Teradata Online magazine states that “analytics can help an organization optimize their business processes to make them effective as possible.” [3]. It is a proven fact that proper use of analytics can lead an organizations to success, providing them with that distinctive advantage over their competitors. In

The proliferation of data, process and system integration technologies, combined with the rapid advances made in analytics, Big Data, customer management and supply chain applications are power catalysts of disruptive change in enterprise IT. Given the fact that many legacy, 3rd party and previously disparate, disconnected systems are for the first time being integrated together, the amount of data available for analysis and decision making has never been greater. Add to this the torrent of data being generated daily through an enterprise's sales cycles, social networks subscribed to, and customer interactions, and the amount of data available can becoming quickly overwhelming. All of these dynamics taken together form the area of analytics and enterprise software called Big Data. As tempting it is for the analytically-minded to dive into these terabytes and explore for insights and previously-unknown associations in the data, to get the most value from the investments in BigData, analytics, and enterprise applications, governance-based frameworks need to be defined that align these systems to specific strategic objectives (McKendrick, 2012). The advent of Hadoop, H-Base, MapReduce and other data analysis and aggregation platforms and applications only become relevant in the context of strategic goals and their accomplishment (Rogers, 2011). That is why more

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include analytics, prescriptive, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelling, web analytics, sales force sizing and optimization, price and promotion modelling, predictive science, credit risk analysis, and fraud analytics. [1]