Projects
MIS 2502 Data Analytics: Big Data
Big Data is a term that has been attracting a lot of buzz in recent years. As technology has advanced, the amount of data being collected has grown, as most everything we do leaves a digital trace that can be collected and analyzed. This data can be structured (usually numerical and easily analyzed because of existing data model) or unstructured (no predefined data model – examples include text or pictures). Unstructured data has created many problems, as there is no clear defined way of examining it, however tools such as text mining, predictive analytics, natural language processing, and sentiment analysis are being utilized to examine all these complex data forms. Big data has provided great opportunities for businesses to understand their customers better and optimize their processes. Outside the business realm, big data is also being used to detect fraudulent transactions and make advancements in cancer treatments. In MIS 2502, we have been discussing advanced data analytics which are being used in real life to predict, explain, and generally gain better insight from trends found in Big Data. Decision trees can be used to predictfuture outcomes based off characteristics from that data set, clustering can identify segments that show meaningful patterns, and association rule mining helps predict future events based on previous data. All of these tools are used in the analysis of Big Data and have real life value. Netflix is a great example of a firm using Big Data to their advantage. Not only do they have access to insights from massive amounts of data on their millions of subscribers, but they have invested in the tools and processes they use to comb through the data as they have grown. Netflix truly understands the value that Big Data can bring to an organization and their customers. They have become able to understand each customer well enough to predict what content they will enjoy based off of previous content they have watched. Using Big Data analytics, Netflix has been able to discover that “a typical Netflix customer will lose interest in 60 to 90 seconds when choosing something to watch,” (Marr). This can help them even in recommending better content, as the information of what titles you pass over with no interest can be used to make better predictions in the future. Big Data is increasingly strong and has the power to drive innovation and success in the future.
Bibliography
- “Analytics.” IBM Analytics, www.ibm.com/analytics/hadoop/big-data-analytics.
- Marr, Bernard. “Big Data Explained in Less Than 2 Minutes – To Absolutely Anyone.” LinkedIn, 23 Mar. 2015, www.linkedin.com/pulse/big-data-explained-less-than-2-minutes-absolutely-anyone-bernard-marr/.
- Marr, Bernard. “Netflix Used Big Data To Identify The Movies That Are Too Scary To Finish.” Forbes, Forbes Magazine, 18 Apr. 2018, www.forbes.com/sites/bernardmarr/2018/04/18/netflix-used-big-data-to-identify-the-movies-that-are-too-scary-to-finish/#3c94cb513990.
- “What Is Big Data?” Villanova University , Bisk, www.villanovau.com/resources/bi/what-is-big-data/#.Wt35Xy-ZPVo.
- “What Is Big Data Analytics? – Definition from WhatIs.com.” SearchBusinessAnalytics, searchbusinessanalytics.techtarget.com/definition/big-data-analytics.