Advice for avoiding the data waterfall in presentations

Hosted by Neil Amato

Tom King, CPA, has worked in corporate America and now is a professor of accounting at Case Western Reserve University. He wrote the book The Numerate Leader in part to help professionals better approach how they share data.

If the right data is presented the right way, audiences won't simply nod their heads or completely tune out. King shares advice for how to get more "wow" and fewer yawns in this episode of the Journal of Accountancy podcast.

What you'll learn from this episode:

  • An introduction to the concept of numeracy.
  • How the author came up with the idea to write the book.
  • Why the question "What problem are we trying to solve?" is important as it relates to data.
  • A commonly used business term that King calls "an empty phrase."
  • How accountants can "think statistically" to do their jobs better.

Play the episode below or read the edited transcript:


— To comment on this episode or to suggest an idea for another episode, contact Neil Amato at


Neil Amato: Do you know the difference between presenting data and presenting the right data? Probably most of us would say yes, but sometimes we might tend to share too much as opposed to sharing the numbers that are the absolute most important when trying to make a point. On today's episode of the Journal of Accountancy podcast, a university professor and author discusses that concept and others in an interview with me, the JofA's Neil Amato. That's right after this word from our sponsor.

Welcome to the Journal of Accountancy podcast. This is Neil Amato. Joining me is Tom King, a professor of accounting at Case Western Reserve University and also an author, and we're going to talk today about his book. Now Tom, first of all, welcome to the podcast.

Tom King: Thank you, Neil. I'm thrilled to be here.

Amato: The title of the book is The Numerate Leader. First, can you tell me some about that phrase and how you came up with it for the book.

King: Well, in school, I read an article called "Rudiments of Numeracy," written by a statistician and the article resonated with me. In it, the article railed against colleagues who made presentations and shared waterfalls of data without reaching a conclusion or giving an interesting "so what." How many times have we sat in on presentations where the other person droned on and on, slide after slide, table after table and at some point you just ask, "Where are we going with this?" Now, I'm sure like most of our listeners, I have a limited attention span and eventually, I just tune out.

By contrast, when you see a numerate presentation where the presenter distills all of this data into a tight conclusion that's presented upfront and supported by relevant data, I learn something and say "thank you." What I get out of this, is I want to be helpful to our colleagues in the accounting profession, and to help us be more informative.

Here's a test, just ask our listeners to consider. We're giving a presentation or explaining something to someone and the other person's going, "Sure, sure, sure, sure" or "Right, right, right, right," and what they're really saying is, "Dude, there's no news in what you're presenting." You know you've struck gold when the other person says, "You're kidding me" or "Get out of here." That is the gift of numeracy, and so I'm trying to promote more informative conveyance of information.

Amato: What are some examples of numeracy that maybe made you want to write this book?

King: My first job out of business school, where I went to work for a car insurance company and the job was really simple. Look at historical accident data and use this to set future prices. You set prices that are too high, you're not going to sell much. You set prices that are too low, you're going to go broke.

I had studied lots of statistics in school and so I thought I was very well prepared for this assignment — wrong. My initial track record was unblemished by success, but I got help from numerate colleagues who taught me how to apply a handful of basic statistical ideas to extract information from data.

A measure of success was, I was able to pay off our mortgage, send our kids to the colleges of their choice, and then leave corporate America to be an underpaid academic. I figured there's something to this, and I wanted to be helpful and so I wrote a book that I thought could be useful for accountants and other professionals.

Amato: You mentioned earlier that waterfall of information that some of us get in many areas of our lives, not just presentations, not just our jobs, we're kind of awash in data. How can we be better at learning what the data means or how to find the right data instead of just being overwhelmed by it all?

King: The trick is to start with the question, "What problem are we trying to solve?" A way of framing this is to say, "If I knew this, I would do that."

Then once we get agreement on what problem it is we're trying to solve, then we can get the right data, and then we can perform the statistical analysis. When you look at this, the amount of time on the statistical analysis is not very much.

The real work is upfront. So think of coding if you have an IT project. The real work is in the requirements. What are we trying to accomplish? If we don't do this, I guarantee you what happens as we start pushing buttons on a computer and very quickly, we have pages and pages of statistical output and we become tired and confused.

We latch onto a relationship we find and then we work backwards trying to find a problem associated with the solution that we identified, so put simply to answer your question, spend a lot of time upfront asking the question, what problem are we trying to solve?

Amato: The phrase "data analytics," it's out there a lot. What does that mean to you, and is it maybe one of those things where its true meaning or understanding often gets lost?

King: Data analytics to me is an empty phrase. I hear it all the time from our students, our recruiters, our faculty, and so on. In my mind, data analytics is simply performing analysis on raw data. Raw data is the raw material that we're working from.

The real issue with numeracy is to figure out what problem we're trying to solve and then find answers to that problem. Typically, we start with a hypothesis, we have an idea, a proposed prediction or explanation, and if we get clarity on the hypothesis, then we bring the statistics to bear.

Two things happen. We either find data that's consistent with the hypothesis, so it lives to fight another day. Or we find data and analysis that refutes the hypothesis and say, "Well, I'm not going to waste my time on this." I can rule out that prediction or explanation and move on.

Amato: In a previous conversation with me, you used a phrase that really stuck with me, and I want to ask you about it. How can accountants "think statistically" to be better at their jobs?

King: What accountants can do is recognize that all observable data are samples. All datasets are samples of larger unobservable populations. We earn our salary when we look at the observable data and make meaningful, defensible conclusions about an unobservable population.

Think about an accounting ledger. Some accountants would argue that they capture all of the transactions an organization has within an accounting period. That cannot be true.

Consider the retained earnings accounts. Retained earnings balance captures all undistributed earnings since inception of the firm and its predecessor organizations. There is no feasible way that all of the accountants who came before us captured every single piece of revenue and expense associated with business operations.

Or consider the equity investment account on the asset side of the balance sheet. If you want to value equity securities, you have to think about the present value of future cash flows extending to the indefinite life of an organization, of a corporation. Today's transactions are a small fraction of the total future transactions that this investment will have, and we have to think much more broadly if we want to value equity securities.

Or consider the liabilities on a balance sheet. Are you telling me that there is an accounting system out there that captures all liabilities and all contingencies for every division, every subsidiary, every joint venture, every employee, every agent, every contractual relationship that an organization has? No.

Thinking statistically means taking the observable data that we have and making informed, defensible conclusions about things that we can never observe directly.

Amato: When did the book come out?

King: October of 2021.

Amato: What did your students think when they found out, oh, my gosh, our professor is an author?

King: Well, here's the trick. I had years of practice using this material on our students. They were the guinea pigs, and I tested out the concepts and exercises on them. The interesting thing about teaching is you quickly realize when you're being unclear.

It is so easy to lose an audience when we talk about statistical reasoning. I did my level best to take decades of work and bring it down into 200 or so crisp pages. I can say that this manuscript has stood the challenge of some very bored, distracted students. I'm proud of the work product. I thank these students for their help in improving the work product.

Amato: I think that's a good story. Beyond thinking statistically for finance professionals, how can they apply some of the book's other concepts to be better at forecasting or scenario planning or risk management in their day-to-day jobs?

King: What they can do is recognize that nothing is constant except change. When we're working with a small sample, a thin sample, let's say we have fewer than 30 observations, we get nervous about reaching conclusions. Why? Our math could be perfect, but the presence of one or two outliers can completely throw off the conclusion. So, we're asking what is the average net worth of a group of people listening to this podcast and then Elon Musk tunes in. We've just blown the conclusion that we can reach from this.

The way to mitigate the problem is we expand the sample, we add more observations. But here's the thing: The world keeps changing. Observations from three years ago are different than they are from two years ago. They're different from now. They are different a year from now and so on. When we do statistical analysis, we can't help it but compare apples and oranges.

Let's give an example. We have, let's say, one of our listeners is interested in the automotive industry. We want to study profitability of well-established car manufacturers. Well, our sample can include VW and Toyota and General Motors, and then we can get data over a number of years. We just aggregated a bunch of data that are not identical. There are different accounting principles used in Japan, in Germany, and the United States, and then these accounting principles change over time.

If we're trying to draw conclusions from these disparate data points, we have to be very measured in how we think about what we're concluding and how we explain it to our bosses or colleagues or clients.

Amato: Tom, it's been an interesting learning about the concept of numeracy and the book The Numerate Leader. Is there anything as a closing thought you'd like to leave for the listeners?

King: Be humble. In the movie Love Story, the heroine says, "Love means never having to say you're sorry." Well, in the world of statistics, that means never having to say you're certain.

We make provisional conclusions based on the data that we can observe, and we can have the best of intentions and use the best training and still be completely wrong. I ask our listeners to never oversell their conclusions when using statistical reasoning to solve problems.

Amato: Tom King, thanks very much.

King: Thank you, Neil. I had fun today.