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?
\nProvide the references in your responses.
\nYour post should be 300 words long (25 points). Respond to at least two other postings (25 points).
\nINclude references.
\nWrite a response to the below two aritcles in 150 words and include your opinion about the article and dont just paraphrase the article:
\nArticle 1:
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\nThe two articles tackle the topic of big data and its impact on the businesses. The first article looks and the unsafe situations in which most organizations find themselves when they have invested all their resources in acquiring software, tools and data scientists and yet they do not see any returns from these investments. Mostly, this because getting benefits out of big data by using manual process is impossible because of the normal limitations of human abilities; they at times get tired and can make mistakes. Therefore, the there is need to fulfill the demands of big data through the use of artificial intelligence (Frankel. 2015). It goes ahead to show where artificial intelligence is able to interact with clients and also have effective communication channels that are customized to meet the needs of specific users. For example, one of the examples given is the Artificial Intelligence driven engagement model.
\nThe second article looks at \u201cNot just big data, but wide data.\u201d It looks at some of the basic features of big data machine. The main components are explained; they include regularization, feature extraction, and cross validation. Regularization deals with the tuning of the model so as to ensure that there is an optimal mix between the conservation model and the flexible mode. Feature extraction refers to the identification of important parameters that are used to deal with machine learning problems. Lastly, it talks about cross validation which is the process that is used to test the efficacy of the model to test data sets (Yeomans, 2015).
\nThe two articles highlighted the use of big data and the techniques used in the same. They also showed the different situations which the techniques can give the expected results. They also focused on encouraging users not to go blindly instead, the articles pointed out some precautions needed to earn the benefits of big data.
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\nReferences:
\nFrankel. S (2015).\u00a0 DataScientists Don\u2019t Scale. Retrieved from; https:\/\/hbr.org\/2015\/05\/data-scientists-dont-scale
\nYeomans, M (2015). WhatEvery Manager Should Know About Machine Learning. By JULY 07, 2015. Retrieved from; https:\/\/hbr.org\/2015\/07\/what-every-manager-should-know-about-machine-learning.
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\nArticle 2:
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\nBig data is a term that describes the large volume of data \u00a0 both structured and unstructured \u00a0that inundates a business on a day-to-day basis.\u00a0But it\u2019s not the amount of data that\u2019s important. It\u2019s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
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\nVolume.\u00a0Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would\u2019ve been a problem \u2013 but new technologies (such as Hadoop) have eased the burden.
\nVelocity.\u00a0Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
\nVariety.\u00a0Data comes in all types of formats \u2013 from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
\n\u00a0 \u00a0 \u00a0 \u00a0Why is big data is so important?
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\nThe importance of big data doesn\u2019t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
\nDetermining root causes of failures, issues and defects in near-real time.
\nGenerating coupons at the point of sale based on the customer\u2019s buying habits.
\nRecalculating entire risk portfolios in minutes.
\nDetecting fraudulent behavior before it affects your organization.
\nBusiness impacts:
\nAcross industries, regions and companies large and small, executives report the exponential growth in data and ability to access to critical information is creating very real business challenges. More than half of business and IT executives, 56 percent, report they feel overwhelmed by the amount of data their company manages. Many report they are often delayed in making important decisions as a result of too much information. Surprisingly, 62 percent of C-level respondents \u2013 whose time is considered the most valuable in most organizations \u2013 report being frequently interrupted by irrelevant incoming data.
\nReference:\u00a0https:\/\/www.sas.com\/en_us\/insights\/big-data\/what-is-big-data.html
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0https:\/\/www.avanade.com\/~\/media\/asset\/point-of-view\/big-data-executive-summary-final-seov.pdf
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