Academic journal article Journal of Management Information and Decision Sciences

Big Data Analytics the Next Big Learning Opportunity

Academic journal article Journal of Management Information and Decision Sciences

Big Data Analytics the Next Big Learning Opportunity

Article excerpt


It is astounding that about 90% of the world's data has been generated in just the last 2 years (SINTEF, 2013). Companies like Facebook, Google, Twitter, and Amazon collect data from interactions and activities by its users. Added to that is yet another new phenomenon known as IoT (Internet of Things), that generates a deluge of data from sensors on equipment and appliances that is even much bigger than what humans create. This ever-increasing collection of data, known often as Big Data, will however, only be useful if it can be analyzed. Analysis will provide useful insights into business problems, and perhaps even make suggestions as to when and where future problems will occur (predictive analytics) so that the problems can be avoided or at least mitigated. Most of the world's big organizations such as Apple, GE, Walmart, Exxon and Samsung have global operations (factories, warehouses, transporters, and customers) and serve several customers with a wide variety of products and services. The complexity of such vast and highly connected networks is hard to unravel, and makes it very difficult for humans to find where and why problems occur.

Data analytics allows businesses to examine large data sets to respond to existing needs in the respective industry of operation. With data being produced continuously by humans and machines, the sheer amount of data available is much more massive than at any time in the past (Hardgrave, 2013). Business analytics is useful for a business to examine patterns and trends in large data sets. Examining the data helps a business generate models for future predictions of patterns and trends. Thus, businesses seek to recruit and hire individuals who understand how to handle large data sets to drive business decision making. This is evident by the increasing demand in the job market for people who have data analytic skills.

Smigala (2014) reports by 2015, 4.4 million jobs will be offered globally to address the needs for big data analysis. Business Schools across the world recognize the need to infuse big data education in the curriculum; however, understanding how to integrate data analysis appropriately in the curriculum presents a challenge. Business Management Information systems courses encourages students to learn how to create and enter data, access data, and generate business reports that can support business decision-making; however, courses offering a specific focus on using data analytics at the undergraduate level seems to be a course educational institutions must examine and integrate in teaching and learning practices.

The purpose of this paper is to briefly describe the nature of big data, highlight its importance in the business world, and make the case for incorporating big data analytics as an essential tool in business and incorporate these tools in university curricula. eCampus News (Barmer, 2014) demonstrates a lack of implementation of data analytic programs in undergraduate curriculums. This research focuses on how undergraduate business schools may help students in higher education gain the big data and data analytics skills and experience necessary to fill the current employment gap of trained professionals in the field.

The Nature of Big Data

Big data is not just a massive database. Traditional databases store structured data in a table format as shown in Figure 1. In this example, all Employee data records are stored as rows in a table with 6 columns for each Employee, hence, the data is referred to as "structured". Structured data can be easily processed by modern database management systems such Oracle, DB2 and SQL Server and using languages such as SQL.

Big data, however, is often unstructured. The data source can be generated by humans or by machines. Examples of human generated unstructured data include Microsoft Word documents, "text" messages from cellphones, posts in Facebook and Twitter, or web pages of companies or individuals. …

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