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While this weeks topic highlighted the uncertainty of Big Data, the author identified the following as areas for future research.  Pick one of the following for your Research paper.:

· Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.

· The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.

· New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.

· More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.

· Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.

NO PLAGARISM please

Your paper should meet the following requirements:

• Be approximately 3-4 pages in length, not including the required cover page and reference page.

• Follow APA guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.

• Support your response with the readings from the course and at least five peer-reviewed articles or scholarly journals to support your positions, claims, and observations.

• Be clear with well-written, concise, using excellent grammar and style techniques. You are being graded in part on the quality of your writing. Source:-

https://s3.amazonaws.com/blackboard.learn.xythos.prod/5a31b16bb2c48/6322124?response-cache-control=private%2C%20max-age%3D21600&response-content-disposition=inline%3B%20filename%2A%3DUTF-8%27%27Uncertainty%2520in%2520big%2520data%2520analytics.pdf&response-content-type=application%2Fpdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200525T000000Z&X-Amz-SignedHeaders=host&X-Amz-Expires=21600&X-Amz-Credential=AKIAIL7WQYDOOHAZJGWQ%2F20200525%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=b4b6eca4f4ce96216878dc3c6f8a0cce9257ccc2b628ff63edd96d8ed219f9c9

As large volumes of raw and complex data, Big data enables programmers to take better decisions and optimize business processes by understanding customer behavior, latest trends, and changing patterns. An enterprise can collect, store, and analyze these large datasets in a number of ways. An enterprise can even use robust big data tools to store, access, and manage the structured and unstructured data collected from various sources in a faster and more efficient way. But no business can utilize and leverage big data optimally without addressing a number of challenges.

Big data challenges are numerous: Big data projects have become a normal part of doing business — but that doesn't mean that big data is easy. The most obvious challenge associated with big data is simply storing and analyzing all that information. In its  Digital Universe report , IDC estimates that the amount of information stored in the world's IT systems is doubling about every two years. By 2020, the total amount will be enough to fill a stack of tablets that reaches from the earth to the moon 6.6 times. And enterprises have responsibility or liability for about 85 percent of that information. Much of that  data is unstructured , meaning that it doesn't reside in a database. Documents, photos, audio, videos and other unstructured data can be difficult to search and analyze.

In order for organizations to capitalize on the opportunities offered by big data, they are going to have to do some things differently. And that sort of change can be tremendously difficult for large organizations.