I know I know... my whole blog I've been talking about how
great data is and all of its practical uses. However, I wanted to spin things
around today and play devils advocate in order to take a new perspective. Data
is numbers but sometimes numbers are wrong, sometimes numbers don't tell a
story, or provide reason. So lets take a look into some examples I found of how
data can be doing more harm than good perhaps.
Wall Street is famous for its "quants," high
paid mathematicians who build complex models to predict market movements and
design trading strategies. These are really smart people who are betting
millions and millions of dollars. However, sometimes their models fail. The key
difference between those models and many of the those being peddled around
these days is that Wall Street Traders lose money when their data model go
wrong.
Something has gone seriously wrong here in the world of
big data. When machines replace human judgment, we should hold them to a high
standard. We should know how the data was collected, how conclusions are
arrived at and whether they actually improve things. And when numbers lie, we should stop listening to them.
"Another example, imagine we're running a business that
hires 100 people a year and we want to build a predictive model that would tell
us what colleges we should focus our recruiting efforts on. A seemingly
reasonable approach would be to examine where we've recruited people in the
past and how they performed. Then we could focus recruiting from the best
performing schools." On the surface, that seems to make sense, but if you take a
closer look it is sometimes flawed.
So data… take it or leave it, but its always good to
question and never just assume. Data is really useful and I do believe it is a
great asset to many companies. However, questioning and learning along with
asking why is important when assessing and viewing data rather than just
assuming.
https://www.inc.com/greg-satell/is-big-data-doing-more-harm-then-good.html
Thanks for an interesting take on this (the world needs more devil's advocates). It is true, that because this data is gathered automatically and digitally, we take it as gospel. I rarely see people stopping to consider "But what if it's wrong?" Definitely something to keep on mind when analyzing digital data.
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