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DECISION MAKING
An Introduction to Data-Driven
Decisions for Managers Who
Don’t Like Math
by
Walter Frick
MAY 19, 2014
Not a week goes by without us publishing something here at HBR about the value of data in
business. Big data, small data, internal, external, experimental, observational — everywhere
we look, information is being captured, quantified, and used to make business decisions.
Not everyone needs to become a quant. But it is worth brushing up on the basics of
quantitative analysis, so as to understand and improve the use of data in your business. We’ve
created a reading list of the best HBR articles on the subject to get you started.
Why data matters
Companies are vacuuming up data to make better decisions about everything from product
development and
advertising
to
hiring
. In their
2012 feature on big data
, Andrew McAfee and
Erik Brynjolfsson describe the opportunity and report that “companies in the top third of
their industry in the use of data-driven decision making were, on average, 5% more
productive and 6% more profitable than their competitors” even after accounting for several
confounding factors.
This shouldn’t come as a surprise, argues McAfee in a pair of recent posts. Data and algorithms
have a tendency to
outperform human intuition
in a
wide variety of circumstances
.
Picking the right metrics
“There is a difference between numbers and numbers that matter,” write Jeff Bladt and Bob
Filbin
in a post from last year
. One of the most important steps in beginning to make decisions
with data is to pick the right metrics. Good metrics “are consistent, cheap, and quick to
collect.” But most importantly, they must capture something your business cares about.
The difference between analytics and experiments
Data can come from all manner of sources, including customer surveys, business intelligence
software, and third party research. One of the most important distinctions to make is between
analytics and experiments. The former provides data on what is happening in a business, the
latter actively tests out different approaches with different consumer or employee segments
and measures the difference in response. For more on what analytics can be used for, read
Thomas Davenport’s 2013 HBR article
Analytics 3.0
. For more on running successful
experiments, try
these
two
articles.
Ask the right questions of data
Though statistical analysis will be left to quantitative analysts, managers have a critical role to
play in the beginning and end of the process, framing the question and analyzing the results.
In the
2013 article Keep Up with Your Quants
, Thomas Davenport lists six questions that
managers should ask to push back on their analysts’ conclusions:
Answers
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