Abstract

This paper explores three books and four published articles that report on results from research conducted online (internet) and off-line (non-internet) of the effect of big data on the network infrastructure of which the common internet platform is based. Furthermore, it explores the hyper-active relationship between the common user and the business entities that provide user content. The articles vary in their exploration of the common good from IBM’s Understanding Big Data which describes big data as “applying to information that can’t be processed or analyzed using traditional processes or tools.” (Zikopolous, Eaton, DeRoos, Deutsch & Lapis, 2013). Which delivers a shorthanded description of an otherwise complicated topic more clearly defined in Too Big To Ignore as “Everything is data. There’s even data about data, hence the term Metadata.” (Simon, 2013) This paper is broken down into chapter sections while interjecting additional research materials within the sections.

Keywords: data, big data, business data, communications data, understanding data

 

Too Big To Ignore: The Business Case for Big Data

            In The Dream Machine, J.C.R. Licklider is described as “the All-American Boy- Tall, blond and good looking, (essentially) good at everything he tried (Waldrop, 2001, p. 1). In 1962, the man heretofore described as he requested as “Lick” through a series of memos at M.I.T. discussed what he coined as a “Galactic Network” concept.  It was through his memos that he described “a globally interconnected set of computers through which everyone could use to access data and programs” (p. 23). Over fifty years later, his memos have expanded to an exchange in information that even the most robust thinker couldn’t have expected. The rampant use of the network we now call “the internet” has been accelerated so quickly that the data that these TCP/IP rotations create has become a large pool from which data can be pulled and categorized. Those categorized data segments are what the mass populace describes as “Big Data.”

Chapter 1: Data 101 and the Data Deluge

            In chapter 1 Phil Simon (2013) describes the “evolution between the enterprise data and the arrival of the data deluge” (p. 29). How that statement translates to the personal interpreter can differ in many cases, however, the basic way in which big data is viewed at the business level occurs via structured segments and augmented by means of three categories: structured, semi-structured, and unstructured. While each of these categories enables the data analyst to view the data from an opulent position, it also stands to reason that the holder of such data carries the keys to their own customer base and potential customer bases across the board.

The overall structure of how that data is sorted and catalogued leads to a more important set of rules which include: how that data is shared, to whom that data is shared, the vested interest in the consumer of protecting their data, and how the security measures of the company can lead to spillage.

How we use big data can still be a challenge to some people but a few local communities have discovered a way of using smartphones to gather useful information. The results yielded a report in potholes that recorded 155,333 bumps whereas before the technology was shared potholes were left largely unreported. Now not all bumps were potholes but the data that was collected was far greater than that without (p. 7).

The Wall Street Journal has also recently reported progressive data that has been found to assist Human Resources personnel in finding better employees via analytical tools that gauge the longevity of an individual hire (p.9).

Chapter 2: Demystifying Big Data

            Chapter 2 attempts to ease the technological aspects of big data towards a more non-technical conversation. This conversation steers closer to another three-tiered approach by defining the data deluge into categories of:

1.)    Volume

2.)    Variety

3.)    Velocity

With the three categories of data deluge set into place we can look at the data through the lens of the depth of the data ranges and the increasing speed of data. Why and how that would be effective can most aptly be defined by Gartner employee, Douglas Laney, who first wrote of the defining sets in 2001 (Simon, p. 49). However, the basis for how the categories are separated creates a bit of an issue with other companies who, instead of acknowledging Laney’s definition, decided to define their own categories. These categories differ now from company to company with the single display of solidarity, essentially they are all built on the three categories that Laney described.

Other topics in this chapter include the ways in which big data can define or re-define your agenda in the marketplace. For example, Netflix Corporation attempted to create a branch of its company for DVD delivery called Qwikster (Woo, 2011). Although the idea for separation would seem beneficial to the overall growth of the company, it was soon discovered that through social media channels the change would not be accepted by the common customer. The charge was therefore revoked.

Chapter 3: The Elements of Persuasion: Big Data Techniques

      The elements of Chapter 3 deal exclusively with the ways that individual fields and sub-fields in big data are examined. In other words, how techniques like statistical methods, data visualization, automation, semantics, and predictive analytics are achieved using both sophisticated tools and largely uncomplicated procedures that can gather and sort data quickly.

The subtext “big overview” (Simon, p. 79) details the three key points that big data delivers to business:

1.)    A better way to understand the past

2.)    A better way to understand the present

3.)    A better way to understand the future.

Chapter 3 includes a scenario stemming from a theoretical CEO of Applebee’s who read some scathing reviews of his restaurant on YELP.com (p. 95). Although the data could provide a potential opportunity for improvement, there were a number of restaurants in the review area and the review was not specific. Without the help of data analytics software, no effective action could take place in this scenario.

Also a “gang of four” is mentioned in the big data field (p. 99). This gang reaches astronomical big data collection points:

1.)    Amazon: 160 million products on its website; 300 million customers on file.

2.)    Apple: 25 billion application downloads

3.)    Facebook: 1 billion users; over 1 billion pieces of shared content per day.

4.)    Google: 34,000 searches per second.

Chapter 4: Big Data Solutions

With a large amount of unstructured data we have come to a point in society in which companies can create entire business models on applications, technologies, and web services that actively attempt to sort the data. Technologies such as Hadoop, NoSQL, and Co-Lumnar databases can fill important needs within the market. The market, however, does not know how to incorporate collected data into their business model. IBM, for example, uses the database software Hadoop to sort their data (Zikopolous, Eaton, DeRoos, Deutsch, & Lapis, 2013, p.73).

While some of these data collection companies and software programs have components that people find extremely helpful, they are also “anything but perfect” (Simon, p. 121). Programs like Hadoop have a heightened potential for data spillage due to consolidating most of the data into one environment. In other words, because the technology is changing so quickly there are no unique procedures for how to police the standards for the process. Collectively, these new data sorting systems have become a recognized aspect of the new normal. This standard practice now includes the range, depth and width required to sort through the plethora of data needed for business focus.

While chapter 1 and 2 focused on how big data doesn’t like to play well with others chapter 4 spends most of the time explaining how there are a number of services available to sort and pull information from your data. A process that can both be expensive and time consuming however extremely beneficial for the overall growth of your company.

Chapter 5: Case Studies: The Big Rewards of Big Data

      In a basic reward system a cookie is given to a participant at the end of a long maze. The cookie is seen as a benefit to the individual or team that acquires the knowledge to solve the maze. With that in mind we enter a phase of data that was best described by Charles Darwin’s quote, “It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change” (Simon, p.143).

There is a company, founded in 2006, that targets web measurements. The company is relatively small, with 250 or so employees. The company analyzes more than 300 billion observations of media consumption. (p. 142). The company was ranked #46 on Forbes’ list of most innovative companies. The company in question is Quantcast. What they do is analyze and sell a predictive model of pulls from a web audience. For example, if you purchased ad space on a specific website via their operational tools, significant gains could be made from utilizing their collected data. They are essentially focusing the efforts of your voice into one specialized arena for large gains.

Also mentioned in this chapter are Hadoop, which has been used to measure healthcare efficiency, and NASA, which has acquired over 100 terabytes of data from its missions (p. 153).

Chapter 6: Taking the Big Plunge

The big data train is moving out of the station and if you do not organize your business in a way that you can catch it, you may end out being left in the dark. A reasonable approach would be to continuously add data to your database and pull out information that could be useful to your operation. For example, if your objectives are to be successful in the restaurant business, understanding your predicted customer base for any particular day could be highly useful for staffing and inventory purposes. Primarily, chapter six deals with the importance and challenges of incorporating business goals with big data results.

Big data is a big commitment. You must strive to push forward at every turn. Businessmen must accept the fact that with the power of technology business can strive, but if you put minimal effort into any aspect of the business (big data included) then your corporation will struggle. Implementing big data software into your organization in a gradual manner will result in a healthy business model, whether your organization has three dollars in assets or three million. Adapting to a model that uses data tracking is essential to growth in this new generation.

Chapter 7: Big Data: Big Issues and Big Problems

While big data has the potential to push business to the next level, it also has the potential to be ethically unsound. This occurs when a data collection site creates tracking modules on customers. There are many security concerns when it comes to big data. For example, the department store Target had a huge data spillage in 2013 which potentially leaked over 70 million customer’s personal information (Fung, 2014).

While a large spillage like that at Target can affect millions of people, the alternative may include a non-desirable state in which your data is not held. Therefore your experience is hindered by a perpetual “beginning questionnaire” type state. Thus we must ask ourselves if the ease of which our personal information is passed is worth the price of possible spillage.

Of course the other fear is that mobile computing will completely phase out a mass of occupations that deal directly with the manual workforce. For example, typists (in the traditional sense) are being phased out, replaced instead by network engineers and a relatively new class of “knowledge workers” (Simon, p. 192). In general the fear that comes from big data is balanced by the privacy and security that businesses place on their information on their clientele.

In a world that Apple IPhone customers can download more than 600,000 unique application programs via the AppStore the security of the interface becomes increasingly important. (p.187). While Apple IPhones have an increased security risk with a large number of people “jail breaking” them, Apple attempts to create applications that only work within the active network thus it encourages people to use their phone as intended, in a non-jail broken state.

Chapter 8: Looking Forward: The Future of Big Data

One way that big data can change the way in which people spend their time shopping is by revolutionizing the consumable goods market at the grocery store. For example, if an individual selects a certain brand of cookie but switches to a cookie with less sugar, the company may respond by manufacturing more sugar-free products. Similarly, if the same complaint for a stoplight has been made via an open forum or discussion board, then the city might look towards remedying the problem.

The overall challenges that big data presents also uniformly pale in comparison to the benefits of which real change can be made. If homeowners lack the initiative to vacuum their homes, then the market could react by offering consumers practical cleaning solutions based on the data collected.

Furthermore, a society without using big data would lose out on potential gains via batting a blind eye at an otherwise gold mine. In an expansion to the basic realm of thinking we can also equate the research that most companies perform on food, film, or drugs all lead to big data. Most movie studios test their films with numerous audience members of differing background. They then require the viewers to fill out comment cards and base their opinions on how to either re-cut or market the film. There have been films that have been completely re-shot based on the initial viewers. That being said we should all be continuously scanning and examining the data that we surround ourselves with every day.

With economic pressures on both sides of the political aisle, it seems completely logical that the benefits of big data far outweigh the potential loss of privacy that those outraged describe. With a wide array of political activists screaming for tighter budgets and change to the status quo, we may end out dealing in a big data world whether we like it or not.  Essentially, the “government should innovate; our politicians shouldn’t need the excuse of shrinking budgets to embrace new technologies and Big Data” (Simon, p. 214).

 

Phil Simon, Author

Phil Simon holds a Bachelor’s of Arts degree from Carnegie Mellon University and a Master’s degree from Cornell University. Mr. Simon is a highly regarded public speaker who has also written six management books including The Age of the Platform which explores how Amazon, Facebook, and Google have redefined business. In 2014, he published The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions. It explores how decision can be made using tools for discovery within the landscape of the data that is collected.

He is self-described as “a sought-after speaker and recognized authority on technology, trends, and management” (www.philsimon.com).

His contributions can be found in a number of news magazines, including Business Weekly and Forbes. He is also featured as a consultant to a number of Fortune 500 companies. His consulting fees can be found on his website (http://www.philsimon.com/about-phil/rates/).

On social media platforms, Mr. Simon can be found on both LinkedIn (under his full name) and on Twitter using the handle @philsimon.

References

 

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worse-phone-numbers-addresses-of-up-to-70-million-customers-leaked/

Simon, P. (2013). Too big to ignore: The business case for big data. Hoboken, NJ: John Wiley

& Sons, Inc.

Waldrop, M.M. (2001). The dream machine: J.C.R. Licklider and the revolution that made

computing personal. New York, NY: Penguin Putnam, Inc.

Woo, S. (2011). Under fire, Netflix rewinds DVD plan. The Wall Street Journal. Retrieved on

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Zikopolous, P., Eaton, C., DeRoos, D., Deutsch, T., & Lapis, G. (2013). Understanding big data:

            Analytics for enterprise class Hadoop and streaming data. New York, NY: McGraw-Hill

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