Let’s take a look at a simple example of intelligent
data-driven decision making in action.
Imagine that we’re an investor.
We’re considering making an investment in
apples (the edible kind not the iPod kind).
Our goal, obviously, is to make a profit.
However, we want to make our investment using
a data-driven decision-making process.
First, we learn that the price of apples has
been holding steady for the past year at $2
per kilogram (which is about $2 for 6 apples).
We create data when we observe and record
the current price of apples at $2 per kilogram.
Next, we learn that the price of apples has
risen this month from $2 per kilogram
to $3 per kilogram.
This price increase was caused by a unexpected
increase in consumer demand.
We create information when we analyze the
historical price data and discover the $1
increase in the price of apples this month.
Then, from many years of observation, we’ve
learned that when the price of apples goes
up by $1 per kilogram, then price of apple
cider will likely rise by $1.50 per liter
in the following month.
We acquired knowledge when we learned about
the relationship between an increase in the
price of apples and an increase in the price
of apple cider.
Next, based on our existing knowledge and
the new information about the price increase,
we make a decision.
We decide it would be smart to invest in apple
cider now, before the price of apple cider
rises by an extra $1.50 per liter next month.
Then, based on our decision, we take action.
We invest in apple cider on the commodities
market at it’s current (discounted) price
in anticipation of an increase in price and
thus an increase in the value of our investment.
Finally, if everything worked out in our favor,
and the price of apple cider rises as predicted,
we will have achieved our goal of capturing
a profit on our investment.
However, achieving our goal is entirely dependent
upon having correct data, information, knowledge,
decisions, actions and the apple-cider market
working in our favor!
While this has been an overly simplified example
of how data-driven decision making works,
hopefully, it helped to demonstrate to you
how we use data
to achieve a goal with data science.