Benefits and Barriers of Big Data Analysis in Organizations

Summary

The case provides an analysis of the benefits and snags associated with the use of big data in organizations. It achieves this through an analysis of organizations like Google, AutoZone, Hertz, Vestas and the Real Time Crime Center who have in the past and currently utilize big data in company based decision making. It outlines the data sources for each company after which it dives into a look at how the companies utilized the data, the results and the benefits accrued through its use.

For every company, it also provides a look at some of the business intelligence tools that collectively enhance the efficiency of big data in decision making. Nevertheless, the case also addresses some of the limitations and the conditions in which big data should be integrated into the operations of any company.

The organizations described in the case collects social media, search, sensor and location data. Specifically, the Real Time Crime Center in New York collects crime data which is a collection of crime records and criminal information. The data is obtained from varied databases enabled by the NYPD’s search system. Vestas, a global wind energy company, collects both location and sensor data from global weather systems and its existing turbines to enhance its turbine placements and generate better understanding of grid patterns.

Other companies like AutoZone and Sears collects customer feedback data. AutoZone utilizes the data in adjusting product and inventory prices while Sears uses it to smoothen promotions, merchandising and selling of products. Similarly, Hertz, a car-rental giant also generates its big data based on customer feedback. Google on the other hand collects its big data based on the queries of its users.

The Real Time Crime Center in New York utilizes an intelligent search system capable of deriving criminal information and crime records from varied databases accurately. Vestas also uses the InfoSphereBigInsights, capable of analyzing and visualizing big data in the analysis of the collected weather and location data. Another system described in the case is the software from NuoDB which AutoZone uses in the analysis of its collected big data from varied databases. The system utilizes cloud based services in its analysis.

Another business intelligent system described in the case although in much detail is a system which was developed by Google to determine the number of people who had been infected by influenza. The system utilized an algorithm which was capable of utilizing web searches to calculate the number of infected people by relating flu related queries to location information.

The Real Time Crime Center in New York needed to facilitate access and ensure accuracy in crime records ("Does Big Data Bring Big Rewards?", 2015). As a result, the center can now access criminal information, crime records and visualize the information within short time spans. Vestas on the other hand needed to increase the accuracy of turbine placements hence the need for big data analysis. Consequently, it widened its wind library and can now analyze and manage locations and weather data more precisely.

For Sears, the need to analyze and maintain big data was the desire to get closer to its consumers. As a result, it can now personalize marketing campaigns to individual consumers as a way of maintaining their loyalty. However, it was not able to gain a competitive edge in the market but possessed the best cost structure in the industry.

Vestas needed to enhance the accuracy of turbine placements and could not without the analysis of vast location and global weather data. Through the collection and analysis of big data, it was able to increase the accuracy with which it laid the turbines. Similarly, through the use of big data, AutoZone was able to effectively manage product prices as well as making inventory adjustments for some of its whooping 5000 stores ("Does Big Data Bring Big Rewards?", 2015).

Also, for the Real Time Crime Center in New York, decisions pertaining to the correlation between criminal activities and individuals were highly enhanced through the analysis of the big crime data collected from varied databases in the public and private domain. Consequently, crime information can be relayed to crime scenes within very limited time spans.

All organizations should analyze big data as it is currently the ultimate tool for better decision making. However, precaution should be taken as there are varied risks and limitations associated with its utilization. First, the company wishing to analyze big data ought to establish business goals for the new information system to ensure that it collects and analyzes the correct information it needs. Else, the organization might end up analyzing the wrong data leading to wrong results as in the case of Google’s influenza infection results discussed in the case.

In a nutshell, Big Data analysis is currently the ultimate solution for data driven decision making and as apparent in the case study, it is the perfect tool for attaining better performance for organizations whose operations involve the flow of vast volumes of data (Hare, 2014). Also evident from the case is its ability of enhancing decision making when integrated with other Business intelligent systems and I would thus recommend the use of big data analytics as a way of enhancing business decisions. However, caution should be taken to ensure that the data collected is accurate and that there exist predefined goals for the integration of the data into the respective systems.