Research at least two articles on the topic of big data and its business impacts

Question

Research at least two articles on the topic of big data and its business impacts. Write a brief synthesis and summary of the two articles. How are the topics of the two articles related? What information was relevant and why?

Your post should be 300 words long (25 points). Respond to at least two other postings (25 points).

Provide the references in your responses.

http://www.ijcsmc.com/docs/papers/April2014/V3I4201480.pdf

https://www.cgi.com/sites/default/files/white-papers/big-data-and-business-process-workflow.pdf

Explanation / Answer

Solution:-

Articlereference:-

1. Data Scientists Don’t Scale. By Stuart Frankel MAY 22, 2015

https://hbr.org/2015/05/data-scientists-dont-scale

2. What Every Manager Should Know About Machine Learning. By Mike Yeomans JULY 07, 2015

https://hbr.org/2015/07/what-every-manager-should-know-about-machine-learning

The first article talks about the precarious situations many organizations find themselves in wherein they have invested huge amount of resources in acquiring the tools, softwares and highly paid “data scientist”, but the return on investment is not yet visible. This is primarily because getting insights out of big data through a manual process is next to impossible because of the inherent limitations of human abilities. The need of the hour is for solutions to scale to fulfill the demands of the big data and this is possible through artificial intelligence.

Examples are shown wherein artificial intelligence is able to interact with consumers at scale to have an effective brand communication channel customized to the needs of the individual customers. Specific instances are that of automated AI driven engagement models wherein portfolio summary and performance explanations of the fund are detailed out in the financial services industry or the sales person is given actionable insights to improve his performance.

In the second article, the basic facets of machine learning are explained. The main components include:- feature extraction, regularization and cross validation. Feature extraction delas with identification of important parameters used for machine learning problem. Regularization basically deals with tuning the model to ensure that it is an optimal mix between a flexible and conservative model. Cross validation is the process of testing the efficacy of the model on the basis of a test data set.

Both the articles highlighted nuances of big data and techniques accompanying the same. It also illustrated the situations in which the techniques give the desired results and necessary precautions needed to get the most out of big data.

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