Assignment2 15 hrs

The New Frontier

October 16, 2017

Professor

Introduction

There are numerous companies in different industries that use data analytics. Some of these companies are AIG, GE Software, American Express, eBay, and AT&T. By exploring how American Express uses IBM big data analytics, the advantages and disadvantages of data analytics can be discovered. Additionally, the fundamental obstacles or challenges that must be overcome to implement data analytics can be identified. Once they are identified, business management can implement strategies to overcome the fundamental obstacles and challenges. Further, looking at how data analytics has transformed the industry in customer responsiveness and satisfaction can help determine how data analytics can be used in the future and what new types of data can be collected.

Data Analytics Defined

What is data analytics? According to Melissa Zgola (2015), “data analytics is the science of collecting, organizing, and analyzing very large sets of data in order to identify patterns and draw conclusions”. Margret Rouse (2016) has a similar definition describing data analytics as “the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software”. Based on these two definitions, it can be said that data analytics is basically collecting data and sorting through it to determine ways to use the information to enhance the business. Some of the ways that data analytics can assist with boosting business performance is by increasing revenues, optimizing marketing campaigns and customer service efforts, allowing them to gain a competitive edge over rivals, increasing response to emerging marketing trends and improving operational efficiency.

The four types of big data analytics that truly aid businesses are prescriptive, predictive, diagnostic and descriptive. Prescriptive analysis is the most valuable type of analysis that reveals what type of actions should be taken. Predictive analysis uses scenarios to predict what might happen or predictive forecasts. Diagnostic analysis is a look at past performance for the purpose of determining what happened and why. The result of this is an analytic dashboard. A descriptive analysis shows what is happening now according to incoming data and mined using a real-time dashboard and/or email reports (Ingram Micro Advisor, 2015).

Advantages

The use of big data analytics has its advantages in several areas. As mentioned earlier, data analytics can boost business performance by increasing revenues, optimizing marketing campaigns and customer service efforts, allowing them to gain a competitive edge over rivals, increasing response to emerging marketing trends and improving operational efficiency. A few advantages of IBM data analytics have been identified as low operating cost, fast speed, reliability through redundancy and scalability (Ghosh, 2017). American Express specifically uses big data and machine learning algorithms for fraud prevention and has enabled American Express to detect more fraudulent transactions and save millions of dollars. Another advantage that American Express has found using data analytics is that they can use the vast data flows to develop apps that connect cardholders to services and products (Marr, 2016). American Express has used predictive analytics to identify customers that may be considering defecting from American Expresses’ products and services more quickly and effectively (Cameron, 2013).

Disadvantages

As with most things, data analytics presents so disadvantages as well. One disadvantage of big data analytics is the vast amount of data. The amount of data processed can be overwhelming if it is not processed correctly. Another disadvantage is privacy issues associated with data collection from and about consumers. Some of the disadvantages of IBM data analytics are communication problems, ambivalence between security and ease of access, higher costs for bigger products, and lesser reach of product offering (Ghosh, 2017). Some disadvantages that American Express encountered with predictive analytics are setting up and programing the system, finding employees skilled in big data analytics, and getting the staff to respect information found by a machine (Cameron, 2013)

Fundamental Obstacles and Challenges

American Express faced numerous fundamental obstacles and challenges while implementing data analytics. According to Ash Gupta, American Express’ President of Global Credit Risk and Information Management, three challenges that were encountered were the significant organizational adaptation and cultural transformation necessary to new and immature technologies, recruitment of employees with skills in Big Data solutions, and determining how to market using the information learned from the data (Bean, 2016). Simon Taranto, American Express’ marketing development manager for Global Corporate Payments experienced different challenges with the implementation of predictive analytics. Some of the challenges that Taranto encountered were “disparate data sources, scale, project design, measurement of attrition, identifying the right variables, validating the 30-40 test models, ensuring the project sticks to core business objectives, and balancing the speed of deployment with the ability to execute on information generated” (Cameron, 2013).

Way Transformed Industry

Data analytics have revolutionized the credit card industry. It has made it possible for the company to better evaluate the consumers and to identify those who are high risk. The data received makes it possible for the company to reach out to at-risk clients or individuals who may be defecting from services and products more quickly. Another way data analytics has changed the industry is making it possible to prevent fraud and save the industry billions.

Future Trends and Data Type

The use of data analytics is going to increase over the next 10 years within the credit card industry. More companies are going to implement so that they can make more informed decisions, lower risks and prevent fraud. Currently data is collected via the internet, from smartphones, with surveys and using feedback from customers. In the future, a new type of data that will probably be collected will be information from smart watches and fit bits. The will allow the company to track what types of places the person visits and be able to recommend more places for them to go. This can already be done with credit card purchases to a point, but can only track where the credit cards are used and the items that are purchased on them.

References

Bean, R. (2016, April 27). Inside American Express' big data journey. Retrieved from https://www.forbes.com/sites/ciocentral/2016/04/27/inside-american-express-big-data-journey/#7ddb3c1e3d89

Cameron, N. (2013, April 11). How predictive analytics is tackling customer attrition at American Express - CMO Australia. Retrieved from https://www.cmo.com.au/article/458724/how_predictive_analytics_tackling_customer_attrition_american_express/

Ghosh, A. (2017, August 12). Advantages & Disadvantages of Using IBM Big Data Analytics On Cloud. Retrieved from https://thecustomizewindows.com/2017/08/advantages-disadvantages-ibm-big-data-analytics-cloud/#Advantages

Ingram Micro Advisor. (2015, March 24). Four Types of Big Data Analytics and Examples of Their Use. Retrieved from http://www.ingrammicroadvisor.com/data-center/four-types-of-big-data-analytics-and-examples-of-their-use

Marr, B. (2016, January 13). American Express Charges into the World of Big Data. Retrieved from http://data-informed.com/american-express-charges-into-world-big-data/

Rouse, M. (2016, December 30). What is data analytics (DA)? - Definition from WhatIs.com. Retrieved from http://searchdatamanagement.techtarget.com/definition/data-analytics

Zgola, M. (2015, August 4). A Deep Dive into Data Analytics – Capella University. Retrieved from https://www.capella.edu/blogs/cublog/what-is-data-analytics/