When most people think of the word “integrity,” ideals of morality probably come to mind first. But the actual root word of the term comes from the Latin, “integer,” meaning “intact.” In any data application, integrity counts.
But where does integrity start in measurement, and who is responsible for making the call to determine which metrics and datasets to use?
This issue became blatantly apparent to me when the now-famous recording of the conference call between Georgia Secretary of State Brad Raffensperger, Donald Trump, and several of their lawyers, was released.
Naturally, the call was heated and tense. The stakes were some of the highest stakes in the world, because the Presidency of the United States is one of the top positions of power, globally. There was a lot to lose, and a lot to win.
And yet, even with such lofty players and such high stakes, integrity in measurement became the matter at hand. Many, many analysts know what I’m talking about.
Read the transcript below, where I’ve bolded the language and terms that stuck out to me as I listened. As you can see, the conversation is focused on access to sourcing and sharing of data, expertise and authority around data (“certified accountants”), and navigating multiple versions of reports:
Hilbert: Mr. President and Cleta, this is Kurt Hilbert, if I might interject for a moment. Um Ryan, I would like to suggest just four categories that have already been mentioned by the president that have actually hard numbers of 24,149 votes that were counted illegally. That in and of itself is sufficient to change the results or place the outcome in doubt. We would like to sit down with your office and we can do it through purposes of compromise and just like this phone call, just to deal with that limited category of votes. And if you are able to establish that our numbers are not accurate, then fine. However, we believe that they are accurate. We’ve had now three to four separate experts looking at these numbers.
Trump: Certified accountants looked at them.
Hilbert: Correct. And this is just based on USPS data and your own secretary of state’s data. So that’s what we would entreat and ask you to do, to sit down with us in a compromise and settlements proceeding and actually go through the registered voter IDs and registrations. And if you can convince us that that 24,149 is inaccurate, then fine. But we tend to believe that is, you know, obviously more than 11,779. That’s sufficient to change the results entirely in of itself. So what would you say to that, Mr. Germany?
Germany: Kurt, um I’m happy to get with our lawyers and we’ll set that up. That number is not accurate. And I think we can show you, for all the ones we’ve looked at, why it’s not. And so if that would be helpful, I’m happy to get with our lawyers and set that up with you guys.
Trump: Well, let me ask you, Kurt, you think that is an accurate number. That was based on the information given to you by the secretary of state’s department, right?
Hilbert: That is correct. That information is the minimum most conservative data based upon the USPS data and the secretary of state’s office data that has been made publicly available. We do not have the internal numbers from the secretary of state. Yet, we have asked for it six times. I sent a letter over to Mr… several times requesting this information, and it’s been rebuffed every single time. So it stands to reason that if the information is not forthcoming, there’s something to hide. That’s the problem that we have.
Germany: Well, that’s not the case sir. There are things that you guys are entitled to get. And there’s things that under the law, we are not allowed to give out.
In the transcript above, what’s happening is that different groups at the table are referencing different sets of data, which proclaim to be measuring votes, registrations, and the overall shape of the election activity in Georgia. The players who have authority are “USPS,” the “Secretary of State,” and “Certified Accountants.”
It was no surprise to me that a call about power took about 45 minutes to get to the heart of the matter, which was the data. Who had it, what it said, what it counted, whether it was to be believed.
This is a very typical occurrence in measurement today. Because so many different organizations are counting, each using their different methodology and metrics, we end up with different versions of personal truth.
The hard truth is that while we often reach for data to help legitimize our positions, there’s a lot of junk data out there.
It’s time now for all of us, as global citizens, to strive for data literacy so we can strive for integrity in measurement. By getting familiar with junk data, good data, and everything in between, we can think critically about the world around us.
It’s time for data literacy. It’s time for integrity in measurement.