Keeping up with Big Data Designing an Introductory Data Analytics Class.
Universities need to keep up with the demand of the business world when it comes to Big Data. The exponential increase in data has put additional dem ands on academia to meet the big gap in education. Business demand for Big Data has surpassed 1.9 mill ion positions in 2015. Big Data, Business Intelligence, Data Analytics, and Data Mining are the four main branches of data analysis. These areas intertwine, overlap, and clearly depend on each other. T his study endeavors to examine the concepts, tools, and techniques of these topics through an introduct ory class in Big Data. After serious efforts and ex amination, this study found Data Analytics to be th most suitable topic. One main reason for making this choice included the need to teach the students how to ask questions when they manipulate large amounts of data. The other reason is the availabili ty of PowerPivot and DAX as an Add In to MS Excel. Most students are familiar with MS Excel, sinc e it is readily available to them. The tools, the t echniques, the built in functions, PivotTables, and DA X, as a formula language, will allow students to ex periment with a million rows of data resulting in a rich and rewarding learning environment. The class will also cover the other areas of Big Data and the ir relationships. Students should become Big Data literate by the time they finish the class successf ully. Descriptors: Big Data, Data Analytics, MS Excel, Po werPivot, PivotTables, DAX, Class Design, Introductory, Learning, Business. Introduction and Problem Statement The exponential increase in data size is not a hidd en fact in our daily activities. We can hardly meas ure the amount of data we capture, process and store ev e y day. "The idea of data creating business value is not new; however, the effective use of data is beco ming the basis of competition" (Insights, 2014) Th e author added that businesses have always wanted to derive some insights in order to make more informed, real time, factual, and smarter decisions. Big Data is hitting organizations from all directio ns, internally and externally. Data is not limited to m achine data but also found as unstructured, online and on mobile as well. The Insights article added that st istical data (historical) and predictive (forwa d thinking) are needed to make helpful decisions. One of the problems that has been noticed, according t o the Insight article, is that we are able to capture and store massive amount of data, but we still lac k the technical capacity to aggregate and analyze unpredi ctable volumes of data. Marr (2016) has summarized some intriguing facts wi th a full invitation for us to examine them closely. His intention is clearly to make us realize tha t Big Data is not only a problem but also a great o ppor2015 ASCUE Proceedings 33 tunity, if we decide to take it seriously as educat ors and business people. Here are some facts that m igh help some of the hesitant business educators to thi nk over and see why a class related to Big Data wit h data analytics is not only an option but a must hav e: Everything we do has a digital trace for us to anal yze and use. Every two days we create as much data as we did fro m the beginning of our civilization until 2003. "Over 90% of all the data in the world was created in the past 2 years." "The total amount of data being captured and stored by industry doubles every 1.2 years." It will take 15 years to watch all the uploaded vid eos to YouTube every day. In the Big Data field, there were 1.9 million IT jo bs created in the US by the year 2015. Knowing that every job needs 3 new jobs created outside of IT to support the one in Big Data. This will result in 6 million new jobs caused by Big Data. By the year 2020, the value of the Hadoop (the open source for big data technologies market) will soar from $2 billion in 2013 to $50 billion. The above facts and findings do not require any add itional proof in order to decide our need to offer additional classes in Big Data in the business curriculum. If we don't react accordingly, students will have to i nvest in additional trainings, seminars, and online classes to c a ch up with other peers in a very competitive mark et. The problem requires an immediate solution. It is clear th t we need to incorporate the concepts and appli cations of Big Data in our academic curricula. Why Teach Big Data Related Class? These days when accountants were responsible for cr unching historical data are behind us (Meyer, 2016) The world expects CPAs and accounting studen ts to manipulate a much larger scale and volume of data, that is, Big Data. Meyer also quoted Wenge r, an assistant professor at the University of Miss issippi, who stated that data exploded in all sort of businesses. Future business students need to posse ss the ability to sift through massive amount of data by using different techniques and tools in order to evaluate data effectively. Students need "basic exp osure to Big Data and data analytics" by adding thi s class to the accounting information systems degree curriculum. It is general belief that all students in business, especially information systems ones, need a deep understanding of Big Data concepts, techniques, and tools. Deciding on the Best Approach to Offer an Introduct ory Data Analytics class It is clear there are some differences in the areas we hear about everyday concerning Big Data. However, if we probe closely, a person could notice, reg a dless of the name of the area that they all inter twine, overlap, and clearly depend on each other. "The dif f rence between Big Data Business Intelligence (BI) is synonymous to fishing in the sea versus fis hing in the lake. Your target is the same but the t ools are decided by the scale" (Mohanasundaram, 2015) I n addition to Big Data and BI, there are two fields related to Big Data. These are data analytics and d ata mining. Obviously, it is very hard to come up with a quick conclusion concerning which approach i s best for your students. Having said that, we need to act to find a solution. There are factors we hav e to consider such as cost, size, and support. Junk (2015) discussed the lack of boundaries between all the areas of Big Data. To help us navigate the complexity of business data concepts, he discussed the most common terms in this field and their relationship: 2015 ASCUE Proceedings 34 1. Business Intelligence. Junk stated that BI is the b roadest category that encompasses the three other areas according to how the business world use s them these days. BI is based on decisionmaking and concentrates on the generation, aggregat ion, nalysis, and visualization of data. It is not only about the data and the tools but also abou t the policy and procedures that support all the activities that convert the data into actionable re sults. 2. Analytics. Junk stated if BI is about making decisi ons, then Analytics is about asking questions. Here you can break down the data, create an assessm ent over time, and compare one trend to another, just to name a few activities. You can com pare sales from this month to other periods. Data Analytics is about opening a wide door to be i nqu sitive .Business today needs to use both historical and predictive data. 3. Big Data. Junk stated this typically refers to the incredible volumes of data from internal and external sources. In addition to volume, data usually is completely raw and in many cases unstructured. Usually businesses use Key Performance Indic ators (KPIs) as the main key to turn their questions into answers. Junk added that "Big Data i s he library you visit when the information to answer your questions isn't readily at hand. And like a real library it allows you to look for answers to questions you didn't even know you had." 4. Data Mining. Finding an answer to a question you ne ver thought about is what data mining is (Junk) Data mining allows users to sift through lo ts f data to find unrecognized trends or patterns among the noise. Further, data mining works c losely with Data Analytics. The difference is analytics is about measuring data while data mining is about sifting through data. No matter which class would be taught, it should co ver the four areas above conceptually. However, it would be impossible to cover the four areas in depth. For th e sake of this paper, the decision was made, with m ultiple reasons in mind, to teach a Data Analytic class. Reaso ns t teach the class include affordability, portab ility, and more importantly the transferability of the gained skilled and knowledge by students to the business w orld. Regardless, the main reason is to prepare our student s for the real world in order to enhance their chan e to compete effectively in the job market. Given that a limited budget is an issue in most aca demic institutions, there was a need to find the mo st affordable tools without jeopardizing the learning outcomes. I n the process of deciding on which techniques and t ools are the best options for this class, there were a few t o consider. This study looked at these areas: 1. SAS sells proprietary software for data analysis an d management, BI. It is robust data analytics and comes with machine learning, statistics, Econ, fore casting and others. SAS can work with Hadoop and R (Hall, n. d. discussed below. The problem with SA S clearly is the cost associated with licensing the software for the academic area. It is not cost effe ctiv at all, where the only price I was able to fi nd at their website direct was a whooping tag price of $9 200.00 for an individual license. In addition to th e prohibitive price, educators need to worry about th e learning curve students must go through to be eff ective in using the software, in addition to the lear ning of the tool for data analysis and predication. SAS is 2015 ASCUE Proceedings 35 very powerful and sophisticated software; however, it is more suitable for high end data analysis for big corporations. 2