The New Frontier: Data Analytics

The New Frontier: Data Analytics

Mariam E. Richardson

CIS 500

April 22, 2017

Professor: Dr. Irwin Otto

Definition and Evolution of Data Analytics

According to DeKroon and Karp, there are several definitions for data analytics. Some sources define data analytics as “as the process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making” (DeKroon & Karp, 2013). Others describe data analytics as “an analytical process by which insights are extracted from operational, financial, and other forms of electronic data internal or external to the organization” (Dekroon & Karp, 2013). Simply put, data analytics is the science of probing unprocessed data for the purpose of drawing conclusions about that information. The process of data analytics is used to assist companies in making better informed business decisions based off information that is already known. Over the years since its inception, data analytics has grown into a process known as big data analytics. With big data analytics, companies can analyze “a mix of structured, semi-structured and unstructured data in search of valuable business information and insights” (Rouse, n.d.). Organizations can examine a variety of vast data set types to discover useful information including customer preferences, market trends, hidden patterns and unknown correlations. The information obtained can be used to guide the company to improved customer service, effective marketing, new revenue opportunities and competitive advantages (Rouse, n.d.).

Since 1990, business intelligence (BI) has been in use, yet the process of data analytics was not incorporated until the late 2000’s. Initially the concept of business intelligence and analytics (BI&A) 1.0 was used to collect structured data using a variety of methods and store in commercial relational database management systems (RDBMS). Once it was realized that the internet could offer benefits that allowed businesses to interact directly with consumers for the purpose of gathering data BI&A 2.0 was introduced. The most current version, BI&A 3.0 is built on mobile and sensor-based content. BI&A 3.0 is evolving because Smartphones and tablets are being used more and more to conduct daily activities and operations. In a short amount of time, data analysis has developed quickly and it must continue to do so to keep up with the ever-changing advancements in technology (Chen, Chiang, & Storey, 2012).

Advantages and Disadvantages

        Several e-commerce companies such as Amazon and eBay use data analytics in improve their business opportunities and operations.  The Seattle-based e-commerce giant Amazon.com has used leveraged data since its inception in 1995 by Jeff Bezos. A key building block for the organization has been their ability to use analyzed data to see potential innovations and patents. Amazon started out primarily as a book retailer that used extensive algorithms to provide recommendations to its customers. The concept of data analytics has grown within the company until Amazon has actually patented a way to use data analysis to determine what consumers are going to buy in advance and ship it to them before they order. This concept is called anticipatory shipping (Marr, 2014). Although this advancement in data analysis is fascinating, it does have its advantages and disadvantages. Some advantages to anticipatory shipping are that the company knows in advance what consumers want and will be able to ensure that it is in stock and consumers will get their products quicker. A major disadvantage is that if the company ships the product before it is ordered, they run the risk that the customer may not want or be able to afford it. They also run the risk that the consumer will not pay for the product, which would cause the company to lose money.