Data is not important
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When I was right out of college - all bright-eyed and optimistic - I wanted to change businesses using math. Before college, I loved the CBS show NUMB3RS, which heavily played into that view. It was a good time for that, as data science was just coming into it’s own as a profession. I was convinced that, given the right algorithms, we could make significant impact to the bottom line.
A few years in the corporate world taught me that it wasn’t more algorithms that were needed. It was better access to data, in general. Silos, lack of standards, and lack of purpose (toward solving specific business problems) stood out to as major problems. That drove my career and passion toward data engineering.
Now, I have reached another milestone in my view of data. Data is not the driving factor behind change. It’s people.
There is a dangerous and unseen current which has been sweeping through the tech industry for close to a decade. Many big name companies have come in to massive success by way of data -Netflix, Facebook, Twitter, and AirBnB, to name the big ones. Their initial cultures were heavily data-driven. Many other companies have tried to emulate their approaches, pouring millions of dollars into data teams and technologies.
Why do so many of them fail to make an impact? Why does do much time, talent, and money go to waste in the field of data?
It comes down to a simple question: are people leading the charge with data, or are they pushing the data with a charge?
How do you tell them apart?
Pushing the data with a charge #
- Quick to argue with data
- Over-promising the value of specific data
- Seek out data which supports the status quo
Leading the charge with data #
- Quick to accept and incorporate data-driven feedback
- Intentionality when exploring value of new data
- Seek out data which conflicts with the status quo
Now, read those two lists again outside the context of data:
Pushing a charge #
- Quick to argue
- Over-promise, under-deliver
- Avoid changing the status quo
Leading a charge #
- Quick to accept and incorporate feedback
- Intentionality when making commitments
- Be suspicious of the status quo
Simply put: those who can make an impact with data will also make an impact without data; those who cannot make an impact without data will not make an impact with data.
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