Good Essay On This Example Was Concerned With The Data Stored By The Retail Loyalty Cards And Never Processed (Explainingcomputers, 2012).

Type of paper: Essay

Topic: Information, Big Data, Vehicles, Youtube, Computers, Technology, Company, Processing

Pages: 5

Words: 1375

Published: 2020/10/03

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Management Information System

Assignment#
According to Mark Hurd, why does his typical customer's IT budget increase by two to three percent per year (even when his typical customer does not do any innovation)? (5 points)
Apparently, no application is used solely in its original mode during different statitions of its utilization. All applications undergo numerous changes in their structure, mainly due to the variety of gadgets used today – users are no longer limited to system-based applications, and can choose from smartphones, tabs, and other devices, hence applications’ adjustments to the new operational systems. Coulds are widely used for the purpose of communication, circulation and publishing information nowadays, and have become modern databases in their structure and utilization principles.
According to Hurd (2012), there are two main issues with applications, which lead to pric increase: 1) the applications CIO’s are trying to update are 23 years old, on average. This means they are no longer compatible with modern gadgets, and need to be updated, which, in its turn, requires investments; and 2) the customers’ deman towards data has been drastically increasing lately, which, again, leads to price increase.
According to the video, forty three percent of workforces in USA will retire in the next 10 years. Why does this matter, according to Mark Hurd?
According to Hurd, the retired forty three percent of the U.S. work forces are bound to be replaced by another forty three percent of work forces, thus, the informational gap is bound to occur between these forces, since the data stored by the future worked will differ drastically from that stored by the retired work force.
The video illustrate two examples where big data are collected but not utilized. Describe the two examples.

This example described the occurrence in the healthcare system, where the records filming surgeries in the hospitals get deleted after a fortnight (ExplainingComputers, 2012).

What are two main components of Hadoop? (5 points)

Hadoop Distributed File System (HDFS) with a cluster storage with high bandwidth is the first component of Hadoop, and a data processing framework named MapReduce, based on Google technology which distributes data set between multiple servers is the second component. In the latter case, the summary of the allocated data is stored in each server.

Are there any ways for small companies (who cannot afford internal big data) to use big data tools? How?

Since the cloud does not require for the data to be downloaded for utilization (ExplainingComputers, 2012), it can serve as a solution for small companies which cannot afford a huge internal data infrastructure. For instance, Amazon frequently posts healthcare and government-related information in its clould hosting public data sets.
Also, quantum computing, as the tool using quantum mechanics for data processing and storage and excelling in unstructured information processing, can be used for big data processing further.

What are three key differences in the big data movement when it is compared with analytics?

The principle of three V’s is often used to characterize big data: Volume, Velocity and Variety. Having been put at the first place, volume is the component which is the biggest challenge and the greatest opportunity at the same time, since traditional computing databases are yet unscalable to allow the organizations store and process the data of high magntitude when it comes to declaring that big data has the potential to assist the companies in more proper resource allocation. As far as potential is concerned, velocity can also be an issue, since the rate at which data flows into many companies and organizations is impossible to be handled by the current IT systems (ExplainingComputers, 2012). As the users also require the data to be streamed to them and processed during the timeframe minimized as much as possible, providing such velocity can pose a great challenge, as well.
As far as variety is concerned, the types of data to be processed have become very diverse, since the traditional types of documents and files the data centers had to deal with in the past have had the data of other multimedia types added to them – namely, audio and video files, compact data, photographs and even 3D models. Since these types of files are still quite innovative and unstructured in terms of their categorizing and processing, traditional computing techniques can no longer serve for these purposes.
According to the case, it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. How many filing cabinets' worth of text are 2.5 petabytes equivalent to?
As one petabyte equals to one quadrillion bytes, and this, in its turn, equals to around 20 million filing cabinets worth of text, a simple calculation gets the answer on this question:
2.5 petabytes multiplied by 20 filing cabinets = 50 filing cabinets worth of text is what Walmart collects from its customer transactions every hour.

How did MIT Media Lab estimate Macy's sales on Black Friday? (5 points)

In order to estimate Macy’s sales on Black Friday, MIT Media Lab used the location data from mobile phones, which showed how many people were in Macy’s parking lot (McAfee&Brynjolfsson, 2012). Thus, the estimated number of people who visited Macy’s was known even before Macy’s counted it.
Erik and Lynn Wu's prediction about housing-price changes in metropolitan areas across the United States proved more accurate than the official one from the National Association of Realtors. What data did they use?
Without any detailed knowledge on the subject of housing market, Erik and Lynn Wu used the web commonly available search data for their research to predict the housing market prices, which eventually turned out to be more accurate than the National Association of Realtors’ report based on a complex analysis of the slowly changing historical data about housing market prices (McAfee&Brynjolfsson, 2012).
How could researchers at the Johns Hopkins School of Medicine predict surges in flu-related emergency room visits a week before warnings came from the Centers of Disease Control?
Google Flu Trends provides a free and commonly available data (McAfee & Brynjolfsson, 2012), containing the aggregation of the relevant search data related to surges in flu-related emergency room, which was used by the researchers at the Johns Hopkins School of Medicine to predict the visits even before the Centers of Disease control announced their warnings.

Provide evidence that using big data intelligently will improve business performance.

According to the data received from the interviews conducted by by the researchers of Wharton and MIT with 330 executives across North America about their technology and organizational management through annual and independent resources , more data-driven companies have better financial (6% more profit) and operational (5% more production) performance results than their competitors.
One major U.S airline company serves as a real-life evidence of these findings. As the airline noticed that at least 10 minutes gap in the actual and estimated arrival time of its planes always took place, with the one-third of plane arrivals having a 5-minute gap on constant basis, it turned to PASSUR Aerospace for help. PASSUR Aerospace provided them with their service RightETA, which calculated arrival times based on data about weather, flight schedules, etc., and it helped to eliminate the gaps, resulting to the overall improvement of the airline’s performance.

PASSUR developed a service RightETA. What does ETA stand for?

ETA stands for Estimated Time of Arrival. RightETA service was provided by PASSUR Aerospace to one major U.S. airline in order to eliminate constant gaps between the planes arrivals .

How was Sears able to reduce the cycle time to generate personalized promotions from 8 weeks to one week?

Since the customer data needed for promotion and greater value generation was fragmented with data warehouses of different brands and took eight weeks to be collected , Seard Holding turned to technological practices of Big Data and set up a Hadoop cluster, which comprised of a group of servers coordinated by Hadoop framework. Afterwards, the data from different brands’ warehouses was collected and analyzed for promotions directly in a Hadoop cluster, which reduced the time of data collection and analysis from eight to one week.

What does "HIPPO" stand for?

HIPPO stands for Highest Paid Person’s Opinion . This concept illustrates the theory that the important decision making process needs to be carried out by the people occupying the highest seats in the organizations only, since their experience and intuition can be counted on with more reliability, opposed to the collected data. Thus, the decision making process, being one of the most crucial steps in any business, is more likely to yield greater results.

What are two techniques that executives can employ if they are interested in leading a big data transition?

Analysing the data .
Analysing the data, and, questioning the data in particular, leads to more questions, which, in its turn, leads to more specific and detailed answers, and allows one to reach the point when they are satisfied with the data to full extent.

Illustrate at least two barriers to the success of big data implementation.

The lack of effective leadership can take the Big Data implementation back . Effective leadership is crucial for any type of business, since a great leader must be creative, original, capable of spotting good ideas and opportunities and turning them into plans, and run his business on the basis of proper duties and information distribution.
Company’s culture is another barrier for Big Data implementation, since the lack of understanding of the corporate ethics make the implementation excecutives be at different pages, while they must be on the same.

The speaker challenges three conventional assumptions. What are they?

Machines can be reliable .
It is not wise to rely on hardware solely, since machine implies failure. Even though it may happen quite rarely with one machine, as the number of machine grows, so does the chance of hardware tofail. Depending on software rather than on hardware solely can eliminate the irreversible consequences from the machine failure, and save quite a lot of funds.

Machines have identities .

Different machines are created to serve different purposes. Considering the abovementioned assumption that machines can be reliable, which is, as has been mentioned, incorrect; hardware needs to be acknowledged as potentially unreliable commodity rather than individuals.

A data set can fit on a single machine .

With the modern data sets, especially in the fields of science, approaching and exceeding hundreds of terrabytes, these data cannot be fit on a single machine, especially considering the fact that machines can potentially fail. Thus, it is not possible and advisable for the data sets to be put on the same machine.

List at least three names of regular enterprises that use Hadoop.

Amazon, Yahoo and LinkedIn are the three regular enterprises that use Hadoop.
Amazon hosts many healthcare and government-related public data sets and uses Hadoop for massive storing and processing unstructured data . Yahoo also stores much information for its online information services .
Finally, LinkedIn uses it for the storage of a one billion’s worth of personalized recommendations every week. Hadoop is effective and desirable to use when it comes to large data sets distribution, storage and processing, since Hadoop performs it across groups or clusters of server computers, while the traditional large scale computing only relies on the hardware with high tolerance .

The speaker talks about where data come from. List at least two sources of data

1)The users who are extensively using innovative technology such as cloud were named as the first source of data. The more users acquire access to modern technologies, the more data is consequently produced.
2) Storage devices are the second source of data. The cost of storage devices is drastically decreasing, and it has become easier and more affordable as a source of data and the option to store data .

Can Hadoop serve data in real time? Is Hadoop a competing product with DB software, such as Oracle?

Hadoop a batch data processing system, which does not allow users to interact directly with the Hadoop clusters and, thus, does not serve data in real time .What Hadoop actually does is that it absorbs data from different sources and processes them, with the data being loaded in interactive databases afterwards. For instance, it is capable of generating indexes for interactive search boxes which complete the words or sentences when a user types the text in.

References

Daoliang Li, Y. L. (n.d.). Computer and Computing Technologies in Agriculture .
ExplainingComputers. (2012, June 16). Explaining Big Data. Retrieved from Youtube.com: http://www.youtube.com/watch?v=7D1CQ_LOizA
Hurd, M. (2012, September 30). Oracle Big Data and Innovation: President Mark Hurd. Retrieved from Youtube.com: https://www.youtube.com/watch?v=F6YGZZeG_2M&feature=related
IV, O. E. (n.d.). smallbusiness.chron.com. Retrieved from smallbusiness.chron.com: http://smallbusiness.chron.com/reduce-bullwhip-effect-3908.html
Jorgwel. (2011, August 28). Hadoop and Big Data 2/6 Processing Petabytes. Retrieved from Youtube.com: http://www.youtube.com/watch?v=xQOKOl6lKJM&feature=relmfu
Jorgwel. (2011, August 28). Hadoop and Big Data 5/6 Ferrari vs Freight Train. Retrieved from Youtube: http://www.youtube.com/watch?v=-QdCABPyu1k&feature=relmfu
Jorgwell. (2011, August 28). Hadoop and Big Data 1/6 Challenging Old Assumptions. Retrieved from Youtube.com: http://www.youtube.com/watch?v=y8DRKd4SKWo
McAfee, A., & Brynjolfsson, E. (2012, October). Big Data: The Management Revolution. Retrieved from Harvard Business Review: https://hbr.org/2012/10/big-data-the-management-revolution/ar

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WePapers. (2020, October, 03) Good Essay On This Example Was Concerned With The Data Stored By The Retail Loyalty Cards And Never Processed (Explainingcomputers, 2012).. Retrieved May 13, 2021, from https://www.wepapers.com/samples/good-essay-on-this-example-was-concerned-with-the-data-stored-by-the-retail-loyalty-cards-and-never-processed-explainingcomputers-2012/
"Good Essay On This Example Was Concerned With The Data Stored By The Retail Loyalty Cards And Never Processed (Explainingcomputers, 2012).." WePapers, 03 Oct. 2020, https://www.wepapers.com/samples/good-essay-on-this-example-was-concerned-with-the-data-stored-by-the-retail-loyalty-cards-and-never-processed-explainingcomputers-2012/. Accessed 13 May 2021.
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"Good Essay On This Example Was Concerned With The Data Stored By The Retail Loyalty Cards And Never Processed (Explainingcomputers, 2012).." WePapers, Oct 03, 2020. Accessed May 13, 2021. https://www.wepapers.com/samples/good-essay-on-this-example-was-concerned-with-the-data-stored-by-the-retail-loyalty-cards-and-never-processed-explainingcomputers-2012/
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Good Essay On This Example Was Concerned With The Data Stored By The Retail Loyalty Cards And Never Processed (Explainingcomputers, 2012).. Free Essay Examples - WePapers.com. https://www.wepapers.com/samples/good-essay-on-this-example-was-concerned-with-the-data-stored-by-the-retail-loyalty-cards-and-never-processed-explainingcomputers-2012/. Published Oct 03, 2020. Accessed May 13, 2021.
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