The New Frontier- Data Analytics

Assignment 1: The New Frontier: Data Analytics

Strayer College

Info System Decision-Making

CIS 500

Assignment 1: The New Frontier: Data Analytics

Data Analytics Overview:

Data Analytics is the process of analyzing copious amounts of information or big data, collected from various sources through software programs and equipment designed for its purposes. This data are examined for patterns, within a specific guideline to establish trends in business sales. With the use of mathematics and statistics, this process gives a clear view of product demands, patterns of customer loyalty, operational needs and fraudulent activities. The method is utilized in various industries such as finance and banking, sports, and even service-based companies, for its unique outcomes to use for improving performance.

Over the years, the business industry used these systems as a source for marketing research. The information helped large retail firms identify sale trends and helped design more effective advertising campaigns to increase consumer activity. Although the process was deemed useful, the steps to create a single report was somewhat tedious and time-consuming. The method includes cleaning and organizing the information collected, deleting any duplications, then analyzing the data of customer responses to categories valuable to the firm. That information was then used to design sales events, provide discounts, or any activity believed to make the company more appealing to the public.

Advantages and Disadvantages of Analytics:

The advantage to using the data analytic process is the evolution of the system. Today, with more companies specializing in providing data analytics as a service, the time of delivering data has decreased dramatically. With more internet based businesses opening and the decrease of storefront sales, Real-time information is crucial to a company’s sustainability. The process now uses day-to=day operational data the overall goal is to achieve efficiency, quality, and accountability with short-term results. Now part of any businesses basic operations and the rapid changes in the industry, data analytics “is now rapidly becoming a mandate for virtually all organizations (Sanders, 2016, pp 2).”

Development of innovative technologies with data analytic capabilities is another advantage towards more efficient business practices. POS, GPS, and RFID are all intelligence sources used to assist in data collections. Big box companies such as Walmart, Kmart, Costco, and Sam’s Clubs and other supply chain operations, use these devices, to track in-store and online purchases, confirm inventory on hand, detect theft, eliminate waste, reduce cost, and deliveries to and from the facilities. It also connects customers through social media by monitoring online customer activities. One search for a product and the IP address links the history of the user to that business. This technique can appear as a disadvantage to the consumer.

Another disadvantage of this type of method is vulnerability; to both to the company and the consumer. Everyday data are extracted for research purposes through various resources, providing retailers certain advantages for building the company brand. The information included in the data analysis is a generated profile of a customer including their most vital information. Each transaction performed leaves a digital footprint to track the consumer’s behaviors including likes, financial patterns, favorable colors, styles and even shopping frequency. With an increase of security breaches over the last few years, security has become one more opportunity that puts the consumer at risk of identity theft and businesses at risk of monetary loss.

Challenges for Implementing Data Analytics:

The fundamental obstacles that business management may find challenging is staying up to date with social media. Even the most advanced software has a tough time to distinguish between true and false. It takes the skill and experience of analysis to identify any data that does not appear to have the substance the company is looking for to support its venture. As the amount of unstructured data becomes more of a challenge, the higher the demand for optimization through platforms such as social media and e-commerce will require a system with more complexity, faster speed and an advance scaled programming for businesses to compete in a global marketplace and protect the integrity of the company.

Companies also face the challenge of data accuracy. A significant issue with securing business and customer information is many of the consumers patronize establishments via internet or marketplace; making it harder to decipher real customers from scammers. The innovation of technology leaves businesses scrambling to keep information secure against hackers and predators. In 2015, the Fraud Reduction and Data Analytics Act was created by the Committee on Homeland Security and Governmental Affairs; to improve federal agency financial and administrative controls and procedures to assess and mitigate fraud risks, and to improve federal agencies’ development and use of data analytics for identifying, preventing, and responding to fraud, including improper payments (Fraud Reduction and Data Analytics Act of 2015, 2016).

Transforming Customer Response and Satisfaction:

The transformation of the way data analytics uses the information gathered has changed the way companies do business. The trend of social media began as one website allowed subscribers the ability to share bits and pieces of information about their selves with friends who also subscribed to the service. As the social media world grew, the capabilities of these platforms began to explore the way people communicated through social websites, making it a pool of opportunities for anyone who had something to share with the public. Businesses discovered a way to use social media communication to guide the way they do business and interact with its customers.

Trends from using Data Analytics:

With the platform of social media becoming more of a channel for expressing emotions. The shift from data analytics collecting unstructured information to a broader spectrum of information. At one point, it was probably likely that the info geared around people who lived in a city-based area, middle class, and young (baby boomers), as they were more likely to embrace the effects of the internet and the introduction of social media.

Today, these specific criteria are not as clearly distinguished because the internet is no longer considered a trend and is now a way of life. People of all age groups use social media as an outlet to interact with a public following while expressing their views on society. The unstructured information collected is no longer just using the traditional descriptions once analyzed the effects of an advertising campaign. It is used to monitor how the campaign is performing in real-time. The company can use this information to recalibrate its performance for a more profitable outcome.

Considering the vast spread of information across social media; the capabilities will expand by examining the effects of outside factors like world news and politics on sales. Noticing the impact on the stock exchange, but on a smaller scale, the consumer behavior plays a huge role in fluctuation of deals on items such as groceries, clothing, and household items. Recently, it has appeared that the effect of the landfall of Hurricane Irma to the Florida coast. Sales for specific things like fuel, camping supplies, bread and nonperishable foods became high demand. Companies can better prepare their stores if they were able to use the analytical information like demographics and weather timelines to prevent product shortages.


Fraud Reduction and Data Analytics Act of 2015: report of the Committee on Homeland Security and Governmental Affairs, United States Senate, to accompany S. 2133, to improve federal agency financial and administrative controls and procedures to assess and mitigate fraud risks, and to improve federal agencies’ development and use of data analytics for the purpose of identifying, preventing, and responding to fraud, including improper payments. (2016).

Kte’pi, B. M. (2016). Data analytics (DA). Salem Press Encyclopedia of Science,

Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. Hoboken, New Jersey: Wiley.

Ng, R. T. (2013). Perspectives on Business Intelligence. San Rafael, Calif. [1537 Fourth Street, San Rafael, CA 94901 USA]: Morgan & Claypool Publishers.

Purchase, J. (2015). Fundamental Mind Shifts for the Future of Data Analytics. Business Intelligence Journal20(2), 26-32.

Sanders, N. R. (2016). How to Use Big Data to Drive Your Supply Chain. California Management Review58(3), 26.

Turban, E. (2013). Information Technology for Management: Advancing Sustainable, Profitable Business Growth, 9th Edition. [Strayer University Bookshelf]. Retrieved from

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