Thursday, September 15, 2016

The Dark Side of Big Data

            In a Forbes article titled The Dark Side of Big Data, by Tom Goodwin, it discusses the dark side or some negative aspects of using Big Data. He uses examples such as tracking hurricanes and movies. Tom explains that big data can now find hurricanes a week earlier than we could ten years ago, but the problem is that the data has yet to find a way to predict the path of the hurricane leaving people on edge and anxious for a longer period. The example with movies is that big data can show us a certain movie to watch at a certain time that we would like, but if you were to watch a crap movie on a plane then it ruins any algorithms chances.
            Big data helps us understand more about what we know and sometimes we overestimate what we know. People assume that big data is the answer and will be the solution to all our problems. We need to use the big data to help us make decisions not rely on the data to make the decisions for us. From a marketing standpoint big data helps the company target a certain audience based on age, interests, gender, and race. I agree with the idea that advertisements are targeted more directly at me personally then say my age group as a whole. If I visit a website looking for watches and then visit another website with ads the same watches I was looking at will appear. Marketing has changed over the years because of the use of this data collected, while before it was based on emotions and gut feelings when targeting an audience. Almost all decisions now are supported with data whether the decision is rational or irrational.  A rational decision that turns out terrible and doesn’t work, but is backed with enough data is defendable and will not land you unemployed, but say an irrational decision with no data to support that decision will almost always lead to unemployment. On the other hand an irrational decision that turns out to be a success will be looked at as lucky but very risky.  People need to find the balance between using big data to help them and relying on big data to do it for them. I think that people do not fully understand the extent of big data and how it can help in all aspects of life from healthcare to business and it will only continue to grow exponentially. As technology and big data grows people still need to learn how it helps and affects their everyday lives. It explains what and when you do something but big data is yet to explain the why and how aspect of people’s actions it could interpret it wrong which can jeopardize the analysis all together.  Lastly, big data needs to be secured and safe in order to protect all the users and customers’ enormous amounts of data.


http://www.forbes.com/sites/tomfgoodwin/2016/07/14/the-dark-side-of-big-data/#58da314638a2

2 comments:

  1. When viewing the title “The Dark Side of Big Data”, I initially thought the article was going to be about privacy and security concerns within Big Data. However, the articles main purpose is to tell us that we are using Big Data wrong. In the hurricane example, the author claims that we are able to detect a hurricane about a week earlier than ten years ago, but he claims we have a longer time to “be anxious” because big data cannot detect its path and size. I completely disagree with the author in this example. There are various uses for Big Data and the majority of them are beneficial for businesses, organizations, and even the average person. In this case, Big Data is allowing us to detect when a hurricane is coming. Instead of thinking that we have a week longer to be anxious, we can think about all of the resources, safety precautions, and evacuations plans we need to gather to protect ourselves from the hurricane.
    Along with this, the author states we are using big data as a “cure-all.” I believe we are using big data to help us improve the accuracy and efficiency of everyday tasks. Whereas some businesses are solely relying on big data to do their work, the majority of businesses just use big data to assist them in their everyday business activity. However, I do agree that as big data usage increases and new technology is being developed, we have to inform ourselves and learn more about the data.

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  2. In the Forbes article, “The Dark Side of Big Data”, there are some good examples that show us how we’re using big data wrong. The article spoke about how we can use big data to detect a hurricane about a week early, but we are unable to track the path and size of the hurricane. It’s always great to know ahead of time if a hurricane is arriving, but not knowing the path and size of it will cause panic to the general public. For example, if a hurricane has been detected and it’s vicinity is announced, and then the majority of people will run to their local supermarkets and stock up on necessary supplies and food in preparation for the storm. This will be great for the local supermarkets, but there will be havoc of people running around in distress gathering supplies…this isn’t something we want during a predicted storm.

    However, Big Data is sometimes useful for product recommendations based on the predicted weather. For example, a supermarket could use weather forecast sites and figure out which days will rain, then stock up and display umbrellas closest to the entrance of the store on the days it will rain. This will keep it super convenient for customers to run in and purchase an umbrella. But, the author also states a good point, where Big Data can’t predict human nature and behavior. Netflix uses Big Data to help curate recommendations for its customers, where there’s an algorithm that tracks the movies and TV shows you watch and then outputs recommendations based off of those genres. This is great and all, but people like to watch certain movies at certain times depending on their mood. So, we can’t truly 100% rely on Big Data and algorithms. It’s crucial for people, especially companies, to figure out how to balance using Big Data to help them and relying completely on Big Data to do it all for them. As technology grows, we need to learn and adapt with it.

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