Big Data Research
Big data has bigger and more convoluted data sets that originate from a variety of new data sources. The data sets tend to be so big to the point where traditional data processing software unable to handle them. Big data can also be defined by three v’s, volume, velocity, and variety. Big data has high volumes of unstructured data which sometimes can range up to hundreds of petabytes. The velocity of big data is the speed of generating data which is usually generated in real-time. Variety stands for the many types of data which are available, with big data generating new unstructured data types or a combination of unstructured and semi structured data types like text, audio, etc. Big data is important since online services like Facebook, Youtube, etc. generate so much user data. The increasing implementation of IoT (Internet of Things) also generates high volumes of data such as usage patterns and product performance. With the popularity of online services and IoT implementation, it puts a task at hand for businesses to create value from the vast amounts of data being generated. It relates to topics that we’ve covered in MIS 2502 since big data deals with unstructured, semi-structured data. The data is difficult to understand since there might be no data model at all, no formal data model, or any pre-defined organization. The course also brought up how 70-80% of an organizations data is unstructured, so it becomes important to create value from the high volumes of data being available. Companies such as Procter & Gamble and Netflix use big data to predict customer demand. They make predictive models for new products and services by noting main attributes of past and current products or services and compare the relationship between attributes and the commercial success of what they sell. Handling big data is an issue since organizations can fall behind with the growing volume of data and the storage of it. Storing is one part of it, but cleaning the data is the also important since it creates value in it. Cleaning big data takes lots of time and effort through organizing and curating relevant information. However, that time and effort is worth it since it allows for effective analysis.
Citations:
“What Is Big Data?” Oracle. N.p., n.d. Web. 07 Dec. 2019.
Whishworks. “Understanding the 3 Vs of Big Data – Volume, Velocity and Variety.” Big Data Consultants & Mulesoft Systems Integration Services. N.p., 8 Sept. 2017. Web. 07 Dec. 2019.