Cloud Cover: "If Data Mining is a Gray Area, It's A Dark One"

Government pioneered Data Mining to track terrorists and disrupt plots in the early 2000’s. It seemed to work for a few dozen times, but at a cost of our privacy. Government spends billions of dollars annually in their research and development of this program. Therefore, more advanced systems have come forth which have lowered the costs of smaller businesses to get on board with the Data Mining industry. From this, businesses learned the same techniques and used them to market to individuals personally. “A hard drive capable of holding a terabyte of data might have cost $1,000 around 2005, but now you can put that on a thumb drive for less than $100,” according to David Krakauer, director of the Wisconsin Institute of Discovery who studies the evolution of intelligence (Tenenbaum). Because of the advancement of algorithms in Data Mining more space is needed to hold more information and interpret the raw data, also it is contributing to the falling cost and increasing performance of the technology used for Data Mining. Though Data Mining users has affirmed they have stopped dozens of terrorist plots through this technology, there is a downside that we must sacrifice our privacy. This is a huge issue and will be discussed later in the cons' section of this blog.

In previous blogs we have also discussed the many ways Data Mining is used in different industries. This is definitely an advantage for the process. Not only can the data  be interpreted for the market to determine your trends, wants, and needs, but it may knows more about you than you do yourself. It never forgets your purchases and searches, and it ties users to groups and trends that we may have no idea even existed. This could be seen as creepy, which brings us to our next advantage.  
Yes, there is an advantage to the creepy side of Data Mining. A new industry is emerging because of this privacy issue. Because businesses in America and other countries see the new threat of their privacy and secrets being screened and recorded by our government, they have began innovating private clouds run in other countries. According to Reuters,”Germany and China are among the countries exploring the idea of building "private" clouds within their borders - essentially versions of the public Internet cloud that they control. In the wake of the NSA scandal, France has openly called for a sovereign cloud of its own” (Barr). This shows how serious the threat of our government is and why the privacy issue is considered a disadvantage, and it too will be discussed in the next section.

Another advantage of this new industry is that these cloud companies will allow businesses to rent their storage and systems by the hour instead of buying expensive data mining centers, which will increase their flexibility of time and money in technology spending as needed. 
As you can imagine, when it comes to the Government and companies researching our private information, conflicts will arise. Privacy is not taken lightly in the land of the free and the home of the brave. It is especially disturbing for me that until recently, through Snowden, did I find out how much of my privacy is at stake and being stored by our Government. Then, through research seeing other companies have followed Big Brother’s footsteps and have been doing the same for years now. Many of us have now changed certain patterns and approaches and have become more aware when using our telephone, internet, and credit cards.

Privacy is one issue, but security is another. As you know, our information is not only being interpreted, but it is being stored in data warehouses that have potential of being hacked, leaked, or even sold to other companies. Hackers may use your information to commit fraud, steal your identity, or even create worms/viruses using your URL address. Also, think about businesses; they do not always last. What happens to all the information collected on you? It is then either dumped into space and possibly leaked or sold for final liquidation to other companies who can then know everything about you, all without ever having you as a customer. Think about all the firms who know your account numbers, passwords, social security numbers, who you chat with at school online, this information is at the fingertips of people we don’t know. Some may abuse this information and some may use it unethically for their advantage. This is a big negative with Data Mining, as is inaccurate information.

Inaccurate information is another negative on my Con list. Inaccurate information could be collected when others are using your computer, telephone, and even credit card. My wife and daughter both use my computer just as much as I do. The information collected there is inaccurate for my profile. Also, we make lots of purchases for others. Therefore, my wife or I  may order some things for others that we may never even think of purchasing for ourselves. However, we get thrown in a database that shoves ads for similar items to these purchase all over my computer screen. Inaccurate information could also hurt big businesses. If they are making business decisions using inaccurate data, they will be in a big mess before they know it and make wrong decisions.

CONCLUSION:

We have seen some ups and downs when it comes to data mining. There are some advantages to it; however, it seems the bad outweighs the good. Organizations may call it a gray area; if it is gray, I say it's a dark one. Some people may not be bothered at all with the issues at hand while some are. However, I guarantee the more informed we become about Data Mining, the less we will approve of such private information being shared with the entire universe and we will tighten up on regulation thereafter. I am interested on your stance on regulation of how our information is gathered and stored.

Barr, Alistair. “Data Mining Puts Cloud Security Back on Agenda.” (June 19, 2013). Reuters. World Wide Web. Date Accessed: 2013/09/20. http://www.reuters.com/article/2013/06/20/us-summit-cloud-idUSBRE95J01720130620

Tenenbaum, David J. “Data Dance: Big Data and Data Mining.” (June  

26, 2013) Why Files. World Wide Web. Date Accessed: 2013/09/20.

http://whyfiles.org/2013/mining-data/

Data mining 24/7

The following are examples of how data mining is applied in different industries, and its 24/7 functionality:

The Energy Sector: In 2009 utility companies such as Duke Energy implemented a data mining system called Smart Grid.  This system enables a two-way "conversation" 24/7 between the company and the customers using advanced meters and other high-tech communications equipment that collect and sends data to the data administrator. This device is connected to the main transformer to collect timely information about what is happening in the area, quickly detect and resolve problems, prevent and shorten power outages, improve service reliability and give customers information to better manage their energy use. 

Home Security Systems: Security and surveillance companies are looking at a different type of data mining called video mining. A San Francisco based company created the "Dropcam" which stores limitless videos in your cloud, eliminating the need for SD memory cards. When Dropcam is on and streaming, it sends encrypted live footage to the cloud for motion and sound detection 24/7. Your video then streams securely from the cloud to your devices for viewing. Users can review videos of the last 7 days and they don’t have to watch them from the beginning to the end.  Instead, Dropcam automatically marks the video segment with motion so that you can jump to those segments right away.

Electronics and Home appliances: Smart TVs are now in the market. Stores are in constant renovation of their inventory, it appears as consumers are no longer looking for the size and quality, but also for “smartness" of their TV. Most TVs now have internet connectivity,  a camera, display content from your smart phone, and play movies from YouTube and Netflix.  They also have Facebook, Skype, and voice command recognition systems, similar to Siri on the iphone.  These TV’s collect data such as the type of movies or shows selected by day, time, category, etc nonstop; it captures the viewer’s activities on the remote control, stores the information and is capable to make recommendations based on historical data.

Political campaigns: If you watched the last presidential debate, you were part of the data-mining operation for Obama’s reelection campaign. His campaign consisted of an analytics department larger than the 2008 team utilizing a sophisticated platform called “Narwhal”. This platform was used to gather data 24/7 and provide information for strategic use during his campaign.  They were able to partner with television service providers, mixing data from both sources to obtain a real target viewers who tuned on the presidential debate.

 

These are only a few examples, nowadays companies are strongly investing in technology and taking advantage of the limitless opportunities found in data mining. What are your thoughts on the different uses of Data-Mining? Can you think of anything you see day-to-day that could use data-mining?


Sources:

Chen, Adrian. "How the Obama Campaign's Data-Miners Knew What You Watched On TV." Gawker. N.p., 2013. Web. 18 Sept. 2013.
"Duke Energy: Envision Smart Energy." YouTube. Duke Energy Media Center, 06 Nov. 2009. Web. 17     Sept. 2013.
"Samsung Smart TV 2013 with Smart Interaction - Discover What's New." Samsung Smart TV. Samsung, 2013. Web. 18 Sept. 2013.
"There's No Need to Be Insecure." Dropcam. Dropcam, Inc, 2013. Web. 17 Sept. 2013.

Data Mining at Your Grocery Store

       One industry in which data mining is already heavily used is the grocery and supermarket segment. Many people have reward cards from their favorite food emporium, from Food Lion's MVP card to Harris Teeter's VIC card, and many would likely be surprised to learn that their cards do much more than provide a modicum of savings on items throughout the store. These cards, generally swiped at the point-of-sale or cash register terminal, usually provide discounts on marked items as well as sometimes collecting “rewards points” that can be redeemed for coupons or store credit. But from the perspective of the supermarket's parent corporation, these cards are capable of providing vast amounts of data that can be used for marketing decisions, advertising campaigns, and inventory planning.

       Tesco is a British supermarket chain that was ahead of the curve when it came to utilizing technology to gather information about their customer base. They launched their reward card system, called the Clubcard, in 1995, and it soon proved extremely valuable. Originally, they were only able to use the information sent in by customers when they requested their card, such as address and household members, which they used for direct marketing campaigns. As technology improved and more advanced POS systems were installed, they were soon able to glean much more information about their customer's buying habits and preferences. First, they used this data to create more effective direct marketing, both encouraging increased consumption of favorite products as well as attempting to get consumers to switch to products with a higher profit margin. Next, Tesco saw an opportunity to market other products and services to their customers. People who paid with a credit card would be more likely to purchase a store credit card, or another credit card from a financial partner. Individuals who visited a store in a remote location were deemed less likely to use public transportation, and their information was sold to car insurance companies. Finally, Tesco combined customer shopping trends with weather data, allowing them to streamline staff for slower days and be more prepared for busier times as well. Tesco eventually overtook their competitors and became the market segment leader in the United Kingdom.

           Safeway is another major supermarket chain that has made extensive use of data mining a large part of their business processes. Through information culled from sales figures in their UK stores, Safeway discovered, “that only eight of the 27 different types of orange juice in the stores sold enough to be profitable”(Rae-Dupree 1). They responded by cutting their orange juice offerings dramatically, recognizing that there wasn't consumer demand for that much variety. Similar research indicated that, “only a dozen of more than 100 types of cheese were profitable.” However, more advanced data mining techniques showed that Safeway's most profitable customers overall tended to buy the types of cheese with the lowest profit margins. They decided to keep selling all types of cheese, realizing that they didn't want to potentially lose any of their most valued clientele.

         Closer to home, well-known grocer Harris Teeter recently partnered with a company called Tresata to “provide (Harris Teeter) with next-generation predictive analytics software”(Johnsen 1). Tresata uses the Hadoop platform to crunch massive amounts of data, promising “the rapid monetization of big data.” Without going into specifics, Harris Teeter claims that this partnership will allow them to “provide its customers better value across its online, mobile, social, and brick-and-mortar channels.” What does the future hold for Harris Teeter and other supermarkets as more and more data is generated and collected? Will social media and mobile technology provide further opportunities for companies to utilize data mining to their advantage? How will consumers respond to the continued use of big data to analyze their shopping habits?

Citations

“Dominating With Data, Data Mining Emerges As The Future Of Business Analysis." San Jose Mercury News (California). LexisNexis Academic. Web. Date Accessed: 2013/09/10.

Johnsen, Michael. “Harris Teeter Takes Step Toward Monetizing Big Data.” Drug Store News. (July 31, 2013). 2013/09/10. http://drugstorenews.com/article/harris-teeter-takes-step-toward-monetizing-big-data.

Rae-Dupree, Janet. “Data Business; Retailers Strike Gold With A Mine Of Information...” Computing. (October 28, 2004). LexisNexis Academic. Web. Date Accessed: 2013/09/10.

Introduction to Data Mining

Geographic, demographic, and psychographic behaviors are consumer data types that are researched through various data mining sources through the eyes of marketers and data analysts. Data mining, is occasionally named as “data” or “knowledge discovery;” is the process where data analysts analyze data from various perspectives and the ability to further compile and outline the data to useful information.  This information can be used for numerous objectives within corporations or elsewhere, for example it can be used to raise revenue by fluctuating prices, cut costs, or both. 

Data mining is used by a great number of professionals in diverse fields. As mentioned before, marketers use data mining material to analyze trends in consumer buying habits. In addition, it helps marketers sufficiently identify their target markets. Recently, data mining has taken a great toll on many business decisions. CEOs of several companies use historical business information to be prudent in making crucial decisions. Furthermore,the use of data mining has begun to be very essential within Scientists and Engineers. To be specific in bioinformatics, genetics, and pharmaceutical companies.

Every time you as a consumer, swipe your Harris Teeter rewards card, Bi Lo Bonus Card, or any in-house grocery store rewards card when purchasing an item to receive the current sales, data is being downloaded. Every time you become a member of a website whether it’s a social media website or signing up to receive notifications on deals/sales in the Charlotte metropolitan area (e.g. Twitter, Facebook, Groupon, Living Social), your information is stored and compiled in data files in hopes of determining your likes and dislikes as a consumer. Organizations of all kinds are constantly downloading data on databases through various ways.  Normally this is how mass amounts of data are compiled for data analysts and marketers to conform the data into useful information.

A specific example of data mining is when a consumer buys a laptop online, for example. Various ads will tailor to that consumer. Particularly on Amazon, if one buys a Mac Notebook, advertisements for Mac Notebook accessories will begin to appear. Even if the consumer goes to other websites, specifically on Facebook, advertisements will tailor to what the consumer purchased or the consumer’s website history. These are very effective ways to advertise to consumers, it enables the marketers to up sale and always allow the data that the consumer is constantly feeding to determine their wants, needs, likes, or dislikes.

Citations: 
"Data Mining: What Is Data Mining?" Data Mining: What Is Data Mining? UCLA, n.d. Web. 04 Sept. 2013.

Hall, Shane. "Examples Of Data Mining Vs. Traditional Marketing Research." Small Business. Chron, n.d. Web. 04 Sept. 2013.