(1) With the stern competition among companies today, success relies on gaining business competitive advantages. This is the main objective of big data and data analytics. Though both seeks to utilize data in gaining edge over the other three characteristics differentiates big data from data analytics and these are volume, velocity and variety. Big data is aware and gives importance to the fact that the volume of data gathered by companies from different sources is doubling every day, so being able to predict these data means providing mechanisms that are capable of processing this amount of data. Further, big data is also aware the data moves fast, so data needed must be captured in a timely manner to be able to make good use of it, and not analyzing the data when it is already considered history. This is important because with correct and reliable data, predictions are more reliable, and decision making is improved. Big data is also aware of the various possible sources of data in today’s era from smartphones, computers to social media, thus it makes mechanisms that enables the collection and integration of such to form one relevant information. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(2) Because one petabyte is equivalent to about 20 million filing cabinets, Walmart will be needing 50 million filing cabinets in a day since the company is estimated to have 2.5 petabytes of data daily. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(3) Location data from mobile phones of people was used by MIT Media lab to calculate the sales of Macy’s during Black Friday’s. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(4) Erik and Lynn Wu collected data based from virtual real-time search instead of waiting for actual historical data to to come up with better, more accurate and timely predictions of the housing pricings in various Metropolitan Areas in the United States. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(5) To provide predictions on urges in flu-related emergency room visits that are at least one week earlier than that of the Center of Disease Control, researchers from the John Hopkins school of Medicine utilized Google Flu Trends. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(6) Big data can be utilized to determine and identify gaps in the current business processes and looking for ways to address such gaps. An airline company, for example, was able to improve their employees production rate thereby improving its business performance through gap determination, analysis and then providing the specific actions. Specifically, through the utilization of big data, the company was able to determine the gap between their flights giving idle but compensated time for their employees. Because of this, they hired PASSUR a known company to create a program that could give a more accurate ETA of their aircrafts to reduce the idle time of their employees and increase company performance.
(7) ETA is the shortcut for the expected time of arrival of an aircraft. (http://en.mimi.hu)
(8) Sears company found out they it took them a lot of time and effort to create personalized promotions for their clients. Upon further investigation, they realized that the major cause of such delays is due to their numerous data formats and data warehouses their office use. They made the big data move when they came up with one data center where all their data are integrated into one. This of course led to a significant decrease in the amount of time analyzing each clients data leading to the timely generation of personalized promotions and of course increased business performance.
(9) Hippo is short for highest-paid persons opinion.
(10) Two techniques that can be used by data executives wanting to shift to the big data revolution are to start relying and performing analysis of data. Decisions must be supported by not only the front as to what the data says by also trying to dig deeper on the sources and reliabity of data. On the contrary, they can completely ignore what data says and based their decisions of intuitions. (McAffee, Andrew and Erik Brynjolfsson, 2012)
(11) There are several possible barriers that leads to the unsuccessful utilization of big data. Two of which can be leadership and decision making. Leadership plays an important role in big data implementation. Companies using big data are not successful because of the volume of data they hold but because they have a leadership team that knows how to set goals and works on how to achieve them. Visualization is important part of the paradigm shift.(Olavsrud, 2013)
Decision-making is another important factor that could lead to an unsuccessful shifting. Data and its analysis is not enough for a company to be successful. It is also important to have people around who knows how to utilized these data and create solutions to recognized gaps.
(12) 6pm.com is an online company that provides its clients with a wide variety of products and operates like Walmart. Currently, it has a large collection of products, offers large discounts, and accepts payments on major credit cards. It offers shipment not only to the US but also on some selected locations. Transactions are through their website.
For them to be able to improve their services through the utilization of big data, they must start integrating their data collections. They have millions of costumers around the globe who accesses their site on different mediums – that is either through their phones, computers or tablets as long as they are capable of connecting to the internet. They must utilize data collected from these to come up with possible shopping lists for each customer at any given time based on the trends or records she have and if possible provide them with personalized promos. With this, there is a bigger tendency of the client getting more items from the company because she knows that her loyalty is being appreciated. Further, big data could also be used to predict the volume of inventory required. This way, the company can order more for those items who are predicted to have more clients and less for those who have few buyers to prevent static inventories of the company. Another thing that can be done by the company based on big data collected is to improve their partnership with other companies. For example, they can partner with smartphone companies to increase their sale. Data on mobile transactions can be analyzed and put to good use here. If data says for example that there are a few clients who uses Samsung for their transactions then Samsung can partner with 6pm and put up a promo for utilizing Samsung phones and getting discounts with the company.
(13) That machines are reliable, have identities and that that data can fit in a single machine are three of the most common assumptions the people have. It is natural that machines ebb in performance over the years but good software does not and if data you’re dealing with is already in petabytes then this certainly does not fit in a single machine even if this machine is a supercomputer.
(14) Mailtrust, Autodesk and Fox are three of the today’s regular enterprises that uses big data.
(15) Data can either come from unstructured data or data directly from the users, from existing databases, from simulations and scientific experiments and simulations of from data generated by other systems.
(16) Hadoop is like a collector of data thus it cannot provide real-time data. It has some common features with databases but I cannot be categorized as a competing product of databases since it is not capable of reading and writing data thus making it a commodity software. In addition, if databases work on structured data, hadoop is capable of supporting both structured and unstructured data and is capable of handling at least three times than that of regular databases.
References:
http://en.mimi.hu (2013). ETA Page. Retrieved from http://en.mimi.hu/aviation/eta.html
Jorgwel (2011 August 28). Hadoop and Big Data 1/6 Challenging Old Assumptions [Video file]. Retrieved from http://www.youtube.com/watch?v=y8DRKd4SKWo
Jorgwel (2011 August 28). Hadoop and Big Data 2/6 Processing Petabytes [Video file]. Retrieved from http://www.youtube.com/watch?v=xQOKOl6lKJM&feature=relmfu
Jorgwel (2011 August 28). Hadoop and Big Data 5/6 Ferrari vs Freight Train [Video file]. Retrieved from http://www.youtube.com/watch?v=-QdCABPyu1k&feature=relmfu
McAffee, Andrew and Brynjolfsson, Erik (2012). Big Data: the Management Revolution. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/23074865
Olavsrud, Thor (2013 April 9). 4 Barriers Stand Between You and Big Data Insight. Retrieved from http://www.cio.com/article/731503/4_Barriers_Stand_Between_You_and_Big_Data_Insight
