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Predictive Analytics – Which Side Outweighs?

Introduction to predictive analytics

http://www.youtube.com/watch?v=BjznLJcgSFI

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Going deeper into the topic of predictive analytics and trying to find more opinions and angles on the matter, I started the research where I left off last week, with the negative side to data mining…


Critical Approach to Predictive Analytics

Eric Siegel informs us that predictive analytics can be very sensitive when they are made about private matters of a customer’s or employee’s data and can even stand in conflict with civil liberties, as illustrated in this interview . It is no surprise that complaints about invasion of privacy and civil liberties become louder, while predictive analytics is a booming practice taking hold across many industries. Computers predicting our most likely behavior, determining if a person should stay in prison or valuating which employee is most likely to quit his/ her job. To support this statement, we can see in another article that unconventional use of predictive analytics and big data is a very frequent matter and is being used on a large scale, which makes peoples personal, business and online lives a part of business platforms. Interesting and informative blogs on predictive analytics and data mining by Will Dwinnell, which examine the matter critically, can be found here. Inevitably, during my research, I stumble back to the positive side of predictive analytics, which leads me to…

How Important is Big Data?

We evaluate another side of predictive analytics, where positive outcomes may be that police patrols are becoming more effective, as well as the reduction of fraud or improvement of health care. Businesses have to survive in a very competitive market and data mining helps to improve decision making and the efficiency of a firm. It may help with the launching of a global product in record time, not by building a new factory, but by building a social network, or the American presidential campaign not relying on opinion polling but on predictions. For government entities, companies, agencies or non-profit organizations, data will be the basis of competitive advantage. Virginia Rometty, of IBM, states that “data will be the next natural resource” and that it goes along with three principles, which are “1. Change how you make decisions, 2. Change how you in fact create value and 3. Change how you deliver value”. Going from link to link, researching, I come to…

The Technical Side


Yan Krupnik emphasizes that predictive analytics should be built specifically to every business, to consider all its influencing factors and how they relate and affect each other. This would give the most accurate prediction results possible.

Leading me to a more technical approach on data mining, it is of a great importance that the creators of predictive models, data scientists and the final users keep in mind, what they are analyzing and what to do with the valuations. It is of interest to all of the parties that an understanding of the decisions, which are intended to be changed or improved, stays in the focus as well as what the model is being used for in the end. In reality there are so many variables to consider, how can they all be calculated in one model?!

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Conclusion


Reading more and more about this topic, I can see that data mining leads to a huge amount of important information, often on private individuals, and has to be treated as such.
The further my research goes, the more I find information and opinions about the negative side of predictive analytics and data mining, but equally a lot about the positive side. It is stressed by the necessity which data mining has in our modern society, where predicting human behavior provides an advantage in every field it is used. Coming to a final conclusion on which side outweighs the other, positive or negative, seems still impossible to me, even after reading and researching so much about it. I lean towards being in favor of data mining because no real harm is caused by predictive analytics but a lot can be prevented. Still, I cannot really answer my final question and end this post openly…


Which Side Do You Think Outweighs?

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Predictive Analytics, Data Mining

What is Data Mining?

In my first question and post on this topic I explained that predictive analytics analyses historical and current events, trying to predict the future for one’s own benefits. It is used in every field of society and economy and affects therewith everybody.
While I got more into detail about the retailer “Target” at the beginning and researched their power and influence over customers, I started seeing a more negative side on the subject…

Who profits/ loses?

While big firms and the market benefit without a doubt from data mining, because they collect their clients and customers data and analyze it for their advantage in the business, the question remains who has a disadvantage of these predictions. If you get coupons of your interest send to your house why should the private consumer complain? The problem lies with how much companies know and how deep their data mining goes…

Is privacy being invaded?

This third question leads me to my recent source, which compares China’s freedom of the internet use to the one of the United States. Seems like a ridiculous comparison at first, but not after current events. While China’s civilization has basically no free access to information and open discussion on the internet because it is being restrained by the government, the US citizens relish in a totally constraint less use of the internet. But how private does this usage remain? It has been revealed that Obama administration’s own surveillance and cyber operations did their own share of hacking and cyber theft and people see their civil liberties and privacy invaded. Obama defends this by stating that there cannot be total security and total privacy.
So a final question, which I am hardly able to answer is…

Where is the limit to Predictive Analytics and Data Mining?

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I am working on the topic of predictive analytics and data mining, starting to go more into detail about the two different sides the matter opposes.  During the previous weeks I collected general information and then started to see more positive or negative aspects, whereas the positive side overweighs the other one and I find it harder to gather criticism. The obvious downside to predictive analytics is the invasion of privacy, but I find more and more appraisal to this kind of data mining, in front the advertisement of IBM.
Here two videos to give another impression of the topic.

http://www.youtube.com/watch?v=m30LxzzbRik

http://www.youtube.com/watch?v=NDv6fnDmqYE&list=PLAD6EEA3C161A84F1

The question to which I have to find the answer is still on how far predictive analytics affects us positively or negatively.

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Predictive Analytics

I found this video while researching my topic and decided to build this week’s post around it because it shows a different perspective of predictive analytics.
Until now, I only wrote about the negative sides of data mining and predictive analytics, influenced especially by Eric Siegel’s opinion and interviews, as well as the article about data mining of the retailer “target”. This little added video clip illustrates in a nice and comical way how predictive analytics can be used in a positive way, for example to prevent crime.
I hope to find more of these kinds of examples, to present both sides of the topic equally.

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Predictive Analytics, Data Mining

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I am working on the topic of predictive analytics in the business of retailers. By using the technique of data mining, supermarkets for example can find out unbelievably much about their customer’s lives and habits and are therewith able to control their shopping habits as well. Due to studies in this area it was discovered that people are especially flexible in changing their shopping habits during big events in their lives like moving to another town, marriage, graduation or especially a pregnancy. By documenting everything they know about their customers, retailers create a profile of every individual with the help of data mining and use this to their advantage to influence and persuade their consumers into buying what profits themselves.
I think it is important to find out how far the data mining and its influence can go and how developed this issue is in Germany. How effected are we by predictive analytics? It is important to know the influence others have on you and your life and you have to be aware of your privacy and what it means to you.
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