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?

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?

Provide the references in your responses.

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

INclude references.

Write a response to the below two aritcles in 150 words and include your opinion about the article and dont just paraphrase the article:

Article 1:

The 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.

The second article looks at “Not just big data, but wide data.” 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).

The 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.

References:

Frankel. S (2015).  DataScientists Don’t Scale. Retrieved from; https://hbr.org/2015/05/data-scientists-dont-scale

Yeomans, 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.

Article 2:

Big data is a term that describes the large volume of data   both structured and unstructured  that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Volume. Organizations 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’ve been a problem – but new technologies (such as Hadoop) have eased the burden.

Velocity. Data 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.

Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

       Why is big data is so important?

The importance of big data doesn’t 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:

• Determining root causes of failures, issues and defects in near-real time.
• Generating coupons at the point of sale based on the customer’s buying habits.
• Recalculating entire risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your organization.

Business impacts:

Across 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 – whose time is considered the most valuable in most organizations – report being frequently interrupted by irrelevant incoming data.

Reference: https://www.sas.com/en_us/insights/big-data/what-is-big-data.html

                  https://www.avanade.com/~/media/asset/point-of-view/big-data-executive-summary-final-seov.pdf