Big Data Downsides

  • Comments posted to this topic are about the item Big Data Downsides

  • Self fulfilling prophecies.  Existing customers' purchase habits are used to market next logical product.  This is successful but amplifies the emphasis on existing behaviours to the exclusion of new behaviours.

    It can make it very hard to get a new product introduced, especially if its performance is measured against a popular, mature and heavily emphasised product.

    As with so many things there are small but important customer behaviours that, when understood, can result in actions that are highly profitable.

     

  • Heh... I remember my Grandmother telling me when I was quite young, "Figures can lie... and liars figure". She was so right even back in the late 50's when she told me that.

    The other truth about BigData is that the problem of "Confirmation Bias" can go unnoticed because there's enough data to, especially when combined with Granny's wisdom, make virtually any outcome appear viable (i.e.  the way you WANT it to look... go figure 😀 ) .

    --Jeff Moden


    RBAR is pronounced "ree-bar" and is a "Modenism" for Row-By-Agonizing-Row.
    First step towards the paradigm shift of writing Set Based code:
    ________Stop thinking about what you want to do to a ROW... think, instead, of what you want to do to a COLUMN.

    Change is inevitable... Change for the better is not.


    Helpful Links:
    How to post code problems
    How to Post Performance Problems
    Create a Tally Function (fnTally)

  • Thank you, Steve, for sharing this. The article you linked to on No Film School introduced me to the concept of "3Vs", which I've never heard of before. I appreciate knowing about that.

    Anyway, I don't see any simple solution to this. Let me illustrate with some news that came into my email Inbox today. I work in my state government's health department. In an email today we learned that a young person died from an illness which often manifest itself in the spring. This is a tragedy which no one wants to happen again. Our epidemiologists using data analytic tools and skills will be working hard to identify where else this disease might manifest itself again to try to stop any more deaths. I'm proud of the work they do and the small part I play in this effort. It involves parts of big data. I don't know what all the epidemiologists consult besides the data our software collects, but if pouring over social media sites to determine travel patterns of people is a part of it, to determine the spread of a disease, then so be it.

    Where we place the bar and how far we should go isn't simple. If human lives are at stake, then I am more inclined to use whatever means possible. If profit is all that's involved, then I'm disinclined. But to what extent we do either - well, that's a hard choice to make.

    • This reply was modified 1 year ago by  Rod at work. Reason: Making it clear what I'm trying to say

    Kindest Regards, Rod Connect with me on LinkedIn.

  • Rod at work wrote:

    Where we place the bar and how far we should go isn't simple. If human lives are at stake, then I am more inclined to use whatever means possible. If profit is all that's involved, then I'm disinclined. But to what extent we do either - well, that's a hard choice to make.

     

    Well said, Rod

  • As with so many things, it is not the size of the wand but the magic of the magician that makes the difference.

  • ".. However, using data to try and improve your efficiency has a downside. It can lead you to a very narrow focus in your approach .."

    A good example of this is the auto insurance industry where data points like: a person's zip code, your grade point average in high school, or whether you rent or own your home - are used to calculate rates. These are all publicly available or easily accessible data points - but  actually how useful are they in evaluating risk?

    "Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho

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