Find the full Amazon blog series here: charleskunken.com/season2
Data is an investment, just like any other asset[1].
We have to think about it this way because it forces us recognize that there is a cost to obtaining it; we can’t just magically snap our fingers.
It’s not only the cost of purchasing data sets that takes money. Sometimes we have to invest in capturing our own data correctly during the course of our work. We might have to pay for new tools, software, training, etc.
In addition to money it takes time to setup. You also have to invest in the ability to interpret the data effectively – like producing metrics. There is an opportunity cost to this as well.
Then there is the benefit: better decision making[2].
So if you’re going to invest in data it has to be weighed against whether or not the resulting insights will pay for themselves.
This brings us to our last point in the series: what does it really mean to be ‘data-driven’? Stay tuned.
-
End Notes:
[1] I first heard this from professor Foster Provost at NYU, the man who literally wrote the book: http://data-science-for-biz.com/authors.html .
[2] Professor Provost’s Day 1 lecture notes: “For all the breathless promises about the return on investment in Big Data, however, companies face a challenge. Investment in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making.” – Shah, Home, and Capela, Harvard Business Review, 2012, https://hbr.org/2012/04/good-data-wont-guarantee-good-decisions
Have some thoughts? Feel free to drop a comment or hit me up: charlie@charleskunken.com
A good input metric is one that enables us to monitor and change our behavior. It should have the following characteristics: (1) Focus – it measures one thing; (2) Ownership –a specific person is accountable for its performance and accuracy of the measurement; (3) Benchmark – there is a target which informs whether or not performance is satisfactory.