Big Data Today
Big Data is the buzzword used to describe the exponential growth and availability of data, the more data there is the more accurate our analyses can become. The higher the accuracy of an analysis the more confidence will present during decision making processes. Combining big data with high-powered analytics can provide opportunities for cost reduction, time reduction, new product development, offering optimization, and overall smarter business decision making. It is then possible to send tailored recommendations to mobile devices of customers who are in the area of the offering, use clickstream analysis and data mining to detect fraudulent behavior, generate coupons at the point of sale based on a customer’s purchase history, as well as to optimize routes for many thousands of package delivery vehicles live from the road.
The three main components of the concept, according to SAS, are volume, velocity, and variety. The increase in data volume is contributed by unstructured data streaming in from social media, increasing amounts of data collection for sensor and machine-to-machine data, and decreasing storage costs that allow for unprecedented collection space. With this comes the struggle of finding relevant information in the ever-increasing amount of data obtained. Furthermore, data is streaming at record speeds and still has to be dealt with in timely fashions, reacting quickly enough to interpret this new speed of data acquisition is becoming a newfound challenge for many organizations. Managing, merging, and governing different varieties of data is still a challenge as well, today more than ever, organizations fight to keep up with merging structured, numeric data from traditional databases with unstructured text documents, financial transactions, and other multimedia information.
This concept relates to almost every main topic we covered in MIS 2502, with greater volumes of information, there is simply more predictions, analyses, and inferences that can be made. The greater the volume of information collected, the accurate predictive analysis can be. Association mining becomes more advanced and companies have more capabilities in associating one product in a transaction with a purchase from other transactions. As big data grows, so does the ability to track consumer behavior and tailor to the most profitable customers for a certain product offering. The more advanced an organization’s predictive analytics become, typically the greater its competitive advantage will be.
An example of this concept in action is how delivery service UPS uses big data to optimize delivery routes, the company stores more than 16 petabytes of data. Much of this big data comes from the telematics sensors they have in over 46,000 of its vehicles. Information is typically collected from each UPS truck about speed, direction, and braking. With this, the company can monitor daily performance of drivers/trucks, and more importantly, to optimize the redesign of a driver’s route structure. Their goal is to eventually use this online mapping data to configure a driver’s drop-offs in real time.
Works Cited
“Big Data.” IBM – What Is – United States. N.p., n.d. Web. 17 Apr. 2015.
“A Very Short History Of Big Data.” Forbes. Forbes Magazine, n.d. Web. 17 Apr. 2015.
“What Is Big Data?” What Is Big Data? N.p., n.d. Web. 17 Apr. 2015.