Becoming a Data Head

“If we have data, let's look at data. If all we have are opinions, let's go with mine.”

Many people work with data without having a dialect for it. However, we want to ensure we're all speaking the same language to make the rest of the book easier to follow. So, in this chapter, we'll give you a brief crash course on data and data types. If you've had a basic statistics or analytics course, you'll know the terms that follow but there may be parts of our discussion not covered in your class.

DATA VS. INFORMATION

An Example Dataset

Table 2.1 tells the story of a company. Each month, they run a different marketing campaign online, on television, or in print media (newspapers and magazines). The process they run generates new information each month. The table they've created is an encoding of this information and thus it holds data.

A table of data, like Table 2.1, is called a dataset.

Notice that it has both rows and columns that serve specific functions in how we understand the table. Each row of the table (running horizontally, under the header row) is a measured instance of associated information. In this case, it's a measured instance of information for a marketing campaign. Each column of the table (running vertically) is a list of information we're interested in, organized into a common encoding so that we can compare each instance.

The rows of each table are commonly referred to as observations, records, tuples, or trials. Columns of datasets often go by the names features, fields, attributes, predictors, or variables.

Know Your Audience

Data is studied in many different fields, each with their own lingo, which is why there are many names for the same things. Some data workers, when talking about the columns in a dataset, might prefer “features” while others say “variables” or “predictors.” Part of being a Data Head is being able to navigate conversations within these groups and their preferences.

A data point is the intersection of an observation and a feature. For example, 150 units sold on 2021-02-01 is a data point.

TABLE 2.1 Example Dataset on Advertisement Spending and Revenue

Table 2.1 has a header (a piece of non-numerical data) that helps us understand what each feature means. Note that not every dataset will have a header row. In such cases, the header row is implied, and the person working in the dataset must know what each feature means.

DATA TYPES

There are many ways to encode information, but data workers use a few specific types of encodings that store information and communicate results. The two most common data types are described as numeric or categorical.

Numeric data is mostly made up of numbers but might use additional symbols to identify units. Categorical data is made up of words, symbols, phrases, and (confusingly) sometimes numbers, like ZIP codes. Numeric and categorical data both split into further subcategories.

There are two main types of numeric data:

■ Continuous data can take on any number in a number line. It represents a fundamentally uncountable set of values. Consider the weather. The outside temperature, if collected and turned into data, would represent a continuous variable. A local news station might measure a temperature of 65.62 Fahrenheit. However, they may choose to report this number to you as 65 degrees Fahrenheit, 66 degrees Fahrenheit, or 65.6 Fahrenheit.

■ Count (or discrete) data, unlike continuous data, restricts the precision of the data to a whole number. For example, the number of cars you own can be 0, 1, 2, or more, but not 1.23. This reflects the underlying reality of the thing being measured.10

Categorical data also has two main types:

■ Ordered (or ordinal) data is categorical data with an inherent order. Surveys, for example, take advantage of ordinal data when they ask you to rate your experience from 1−10. While this looks like count data, it's not possible to say the difference between survey ratings 10 and 9 is the same as the difference between 1 and 0. Of course, ordinal categorial data does not have to be encoded as numbers. Shirt size, for example, is ordinal: small, medium, large, extra-large.

■ Unordered (or nominal) categorical data does not have an underlying order to follow. Table 2.1, for example, has a Location feature with values Print, Online, Television. Other nominal variables include Yes or No responses; or Democrat or Republican party affiliation. Their order as presented is always arbitrary—it's not possible to say one category is “greater than” another.

You'll notice Table 2.1 has a Date feature, which is an additional data type that is sequential and can be used in arithmetic expressions like numeric data.

HOW DATA IS COLLECTED AND STRUCTURED

BASIC SUMMARY STATISTICS

Data does not always look like a dataset or spreadsheet. It's often in the form of summary statistics. Summary statistics enable us to understand information about a set of data.

The three most common summary statistics are mean, median, and mode, and you're probably quite familiar with them. However, we wanted to spend a few minutes discussing these statistics because we frequently see the colloquial terms “normal,” “usual,” “typical,” or “average” used as synonyms for each of the terms. To avoid confusion, let's be clear on what each term means:

■ The mean is the sum of all the numbers you have divided by the count of all the numbers. The effect of this operation is to give you a sense of what each observation in your series contributes to the entire sum if every observation generated the same amount. The mean is also called the average.

■ The median is the midpoint of the entire data range if you sorted it in order.

■ The mode is the most common number in the dataset.

Mean, median, and mode are called measures of location or measures of central tendency. Measures of variation—variance, range, and standard deviation—are measures of spread. The location number tells you where on the number line a typical value falls and spread tells you how spread out the other numbers are from that value.

As a trivial example, the numbers 7, 5, 4, 8, 4, 2, 9, 4, and 100 have mean 15.89, median 5, and mode 4. Notice the mean (average), 15.89, is a number that doesn't appear in the data. This happens a lot: the average number of people in a household in the United States in 2018 was 2.63; basketball star LeBron James scores an average of 27.1 points per game.

It's a common mistake for people to use the average (mean) to represent the midpoint of the data, which is the median. They assume half the numbers must be above average, and half below. This isn't true. In fact, it's common for most of the data to be below (or above) the average. For example, the vast majority of people have greater than the average number of fingers (likely 9.something).

To avoid confusion and misconceptions, we recommend sticking with mean or average, median, and mode for full transparency. Try not to use words like usual, typical, or normal.

CHAPTER SUMMARY

In this chapter, we gave you a common language to speak about your data in the workplace. Specifically, we described:

■ Data, datasets, and multiple names for the rows and columns of a dataset

■ Numerical data (continuous vs. count)

■ Categorical data (original vs. nominal)

■ Experimental vs. observational data

■ Structured vs. unstructured data

■ Measures of central tendency

With the correct terminologies in place, you're ready to start thinking statistically about the data you come across.

“Statistical thinking is a different way of thinking that is part detective, skeptical, and involves alternate takes on a problem.”13

This chapter is about how you think about data—about arming yourself with a mindset to consume and think critically about data you come across in your business or everyday life. It's laying important groundwork for the rest of the book, yes, but if any of these concepts are new to you, you'll likely find yourself watching the news or reading the latest popular press science articles through a new lens—a statistical lens.

Two important callouts before we start.

First, we're just scratching the surface here. This one chapter isn't going to replace a semester of Statistics (with apologies to any students) or dive into all the psychological aspects of “thinking” like the modern classic Thinking, Fast and Slow.14 But we will introduce several concepts and establish a foundation for statistical thinking, incomplete as it may be.

Second, there is a risk in reading these next few chapters that you will become cynical about data. You might throw your arms up, claiming this statistics nonsense obfuscates the truth under complicated equations and numbers, and swear off any analysis you see. Or maybe you'll throw tomatoes at every article you read because you learned a few tricks of the statistical trade and doubt they know as much as you.

Please don't. Our goal isn't to make you reject the information you see, but to question it, understand it, know its limitations, and perhaps even appreciate it.

ASK QUESTIONS

A core tenet of statistical thinking is “ask questions.”

Many of us do this to some degree in our everyday life. We assume you, as a reader of a book about data, naturally question sure-bet claims from advertisers (“Lose 10 pounds in a month!” or “This hot stock is the next Amazon!”) and bizarre posts on social media. (Open Facebook or Twitter, and you'll probably spot one right now.) So, the muscle is inside of you somewhere. Frankly, it can be fun to sit back as an observer and tear apart the obvious lies.

But it's much harder to do to claims and data personal to us. Every political election shows this. Take a minute to think seriously and honestly about how quickly you become suspicious of claims or numbers from the other political party.15 What goes through your head? “Their sources are bad. My sources are good. Their information is false. My information is true. They just don't understand what's going on.”

Clearly, this discussion can get philosophical very quickly. Our intent isn't to stir up a political debate or dive into the many factors that shape our personal and political ideologies. But there's a lesson to be learned here—it's hard to question everything when the “everything” includes your own thinking and reasoning.

More pertinent to the mission of this book, think about the information you see in the workplace. When you see data sprawled out across spreadsheets and PowerPoint presentations—information that impacts your company's success, your job performance, your possible bonus—is it viewed with any skepticism? In our experience, it's often not. Numbers in a boardroom are viewed as cold hard facts. Truth in black ink, rounded to the nearest decimal.

Why is this? Probably because you don't have time to question, poke and prod, or collect more data. This is the data you have, the data you must act on, and the data you can point to and blame if things don't work out your way. When faced with these constraints and limitations, skepticism turns off, almost reflexively. Another reason, you might suggest, is even if you understand issues with the data, your boss probably doesn't. A chain reaction occurs where everyone assumes that someone else, up the management chain (or even down below), will take the number at face value, and that assumption permeates down to those of us staring at the spreadsheet. They'll assume it's true, so we will act on it as if it is.

Data Heads can push back. And it starts with understanding variation.

We're using “statistical thinking” in a general sense, defined in the quote at the beginning of the chapter. You might prefer probabilistic thinking, statistical literacy, or mathematical thinking. No matter which phrase you prefer, all deal with the evaluation of data or evidence.

Some might wonder why this line of thinking is important. Businesses and life in general have gone on without it. So why now? Why should Data Heads care?

In an article titled, “Data Science: What the Educated Citizen Needs to Know,” Harvard economist and physician Alan Garber explains why:16

The benefits of data science are real and have never been more salient or important. Increasingly accurate predictions will make the products of data science more valuable than ever, and will increase interest in the field. The advances can also breed complacency and blind us to flaws. Workers of the future need to recognize not only what data science does to assist them in their work, but also where and when it falls short… . a deeper understanding of probabilistic reasoning and the evaluation of evidence is a general skill that will serve all of them well.

THERE IS VARIATION IN ALL THINGS