If other writers are like me, when you write a lot on something, well a lot of great little tidbits and nuggets just don’t fit in. The thoughts are interesting, but will ultimately disrupt the flow of the article or series of articles. The joy of having my own site is I don’t have to junk those ideas like a Universal exec junking another monsters franchise.

For instance, last July I dug pretty deep into the M&A (mergers & acquisitions) landscape as it relates to media, entertainment and all communications (that’s my term for the pipes, both real, spectral and bundled) that deliver it. I’ve long been fascinated by M&A, doing some in my career, but this gave me the chance to study the trends at a higher level. So I devoted most of July to this topic.

But a lot of thoughts didn’t fit into my initial piece. Consider this the DVD commentary/directors edition of that post along with a slight update into M&A in entertainment, media and communications since the huge surge of 2018. Did the pace continue? Has the consolidation worked? And how has the media covered it?

**Data Thought: M&A Is a Messy Data Set**

What does this mean? It means that with fuzzy definitions, small sample size and exponential effects, you can make M&A data do lots of things.

Let’s pause on that last sentence. Another way to say that is my least favorite quote of all time, “There are three types of lies: lies, damned lies, and statistics.” This implies all of statistics is a lie. And what I’m about to show is how you go about doing that: taking small sample size, selective dating and fuzzy definitions to weave a narrative.

But the word “narrative” is the key to that last sentence. The quote should be “lies, damned lies and narratives”. Narratives are created by weaving together anecdotes and reasoning from first principles, sometimes using statistics as your anecdotes. Good data analysis is the antidote to bad narratives. The problem is that data analysis is hard to do and takes lots of time.

But maybe if I show you how messy this data set is, the next time *The Hollywood Reporter* or *The New York Times *does an M&A article, you can see how they may be selectively pulling data to sell a narrative.

In fact, let’s use the *New York Times *and *Bloomberg *to show this*.* A lot of the inspiration for this series came from the *Times* June 2018 article showing how huge M&A was in 2018 through the first six months, and expectations it would continue at that frantic pace. Here is they key image from *The New York Times*:

Yikes. So M&A in the first half of 2018 was five times the amount of all of 2017. That’s a 5X jump. A jump that big is clearly the signal through the noise in this small sample size data set. So presumably, if Bloomberg wrote a similar article on media & entertainment M&A, we’d see similar results. And here we have that:

Frankly, it is hard to reconcile these numbers. The New York Times divides up telecommunications and media & entertainment, while Bloomberg combines them. But it doesn’t matter because the numbers are way off either way. How could Thomson Reuters data be off from Bloomberg’s data by three times in 2017?

I could make this story even crazier. Here’s an article from *Variety *from October of 2018, and it uses Thomson Reuter’s information, and it doesn’t even match the New York Times numbers. Then they give PwC’s numbers, which don’t match either set:

* Thomson Reuters reported $145.7 billion worth of media and entertainment deals across the Americas in the first six months of 2018 — up from $141.7 billion for all of 2017. PwC, looking through a different lens, found $82.4 billion worth of U.S. media and telecom deals in the first half of the year, up 197% from last year.*

Sometimes M&A doesn’t even match at the same paper. Take the *Hollywood Reporter*. Doing research for my series in July, I found two different charts from articles less than a year apart, one from March 2016 and one from January 2017. Even they don’t match.

The point is M&A data is messy, as I wrote way back in July. By choosing either when a deal closed or was announced or what counts as “entertainment” you can draw very different conclusions. It’s confusing enough that I want to do a quick explainer on it.

**A Quick Primer on M&A Data Variables**