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4/6/2019 4 Analytics Concepts Every Manager Should Understand

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DATA

4 Analytics Concepts Every Manager Should Understand by Amy Gallo

OCTOBER 31, 2018

JORG GREUEL/GETTY IMAGES

Like many professionals, my job doesn’t require expertise in data or analytics. I’m a writer and

editor, so I deal with words, not numbers. Still, nearly every knowledge worker today needs to be a

regular consumer of data analysis. For example, I need to understand whether and why articles on

having a mid-career crisis outperformed ones on receiving feedback or why pieces with particular

headlines get more traffic than others.

4/6/2019 4 Analytics Concepts Every Manager Should Understand

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INSIGHT CENTER

Scaling Your Team’s Data Skills

Help your employees be more data-savvy.

I also need to be able to read research on the topics I cover and understand whether the findings in

those studies are valid and generalizable, and be able to articulate the findings — and their

limitations — to you, our readers.

To do all of this, I need a more-than-basic understanding of data analytics. And while the statistics

course I took in graduate school was helpful, it didn’t fully equip me to grasp the important concepts

and have the conversations I need to around data analysis.

Fortunately, I had the opportunity to talk with

some of the best experts in the field — Tom

Redman, author of Data Driven: Profiting from

Your Most Important Business Asset, and Kaiser

Fung, who founded the applied analytics program

at Columbia University — about several critical

topics when it comes to data analysis. Here are four refreshers from our archives on data analytics

concepts that every manager should understand.

Randomized controlled experiments

One of the first steps in any analysis is data gathering. This often happens via a spectrum of

experiments that companies do — from quick, informal surveys, to pilot studies, field experiments,

and lab research. One of the more structured types is the randomized controlled experiment. Many

people, when they hear this term, immediately think of costly clinical trials but randomized

controlled experiments don’t have to be costly or time consuming and they can be used to gather

data on things like whether a particular customer service intervention improved customer retention

or whether a new, more expensive piece of equipment is more effective than a less costly one. In this

refresher, Tom Redman helps me understand what it means for a test to be “controlled” and how

you make sure it includes an element of “randomization.” The article also addresses questions like:

What are dependent and independent variables? And what are the steps to designing and

conducting one of these experiments?

A/B testing

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One of the more common experiments companies use these days is the A/B test (which is a type of

randomized controlled experiment). At their most basic, these tests are a way to compare two

versions of something to figure out which performs better. Companies use it to answer questions

like, “What is most likely to make people click? Or buy our product? Or register with our site?” A/B

testing is used to evaluate everything from website design to online offers to headlines to product

descriptions. It’s critical to understand how to interpret the results and to avoid common mistakes,

like ending the experiment too soon before you have valid results or trying to look at a dashboard of

metrics when you really should be focusing on a few. You can learn more about A/B tests here.

Regression analysis

Once you have the data, regression analysis helps you make sense of it. Of course, there are many

ways to analyze the data, but linear regression is one of the most important. It’s a way of

mathematically sorting out whether there’s a relationship between two or more variables. For

example, if you are in the business of selling umbrellas, you might want to know how many more

items you sell on rainy days. Regression analysis can help you determine whether and how inches of

rain impacts sales. It answers the questions: Which factors matter most? Which can we ignore? How

do those factors interact with each other? And, perhaps most importantly, how certain are we about

all of these factors?

Fortunately, regression is not something you typically do on your own. There are statistics programs

for that! But it’s still important to understand the math behind it and the types of mistakes to avoid.

In this refresher, I explain how regression works and share a common — but often misunderstood —

warning against confusing correlation with causation.

Statistical significance

Once you’ve done the analysis, you need to figure out what your results mean, if anything. This is

where statistical significance comes in. This is a concept that is also often misunderstood and

misused. And yet because more and more companies are relying on data to make critical business

decisions, it’s an essential concept to understand. Statistical significance helps you quantify

whether a result from an experiment is likely due to chance or from the factors you were measuring.

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This is a concept I sometimes struggled to fully understand myself but, fortunately, the average

professional doesn’t need to understand it too deeply. According to Tom Redman, who helped out

with this refresher, it’s more important to understand how to not misuse it.

While you’re boning up on these four concepts, it would also be helpful to read this overview on

quantitative analysis from my colleague, Walt Frick. It is a nice primer on why data matters, picking

the right metrics, and asking the right questions from data. There’s also a great chart on correlation

vs. causation to help you make decisions about when to act on analysis and when not to.

Lastly, if you’re interested in analytics because you need to consume social science research, I highly

recommend this piece from Eva Vivalt, a research fellow and lecturer at the Australian National

University. She gives several tips for determining whether the evidence from a study should be

trusted.

Data analytics is ultimately about making good decisions. It doesn’t matter what business you are in

or what your role is at your company, we all want to — need to, really — make smart, informed,

evidence-based decisions.

Amy Gallo is a contributing editor at Harvard Business Review and the author of the HBR Guide to Dealing with Conict. She writes and speaks about workplace dynamics. Follow her on Twitter at

@amyegallo.

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