Looking back at my time so far as a data analyst, some of the biggest mistakes I’ve made were not technical in nature but around how I communicated within the organization.
Two real-world examples to illustrate:
A few years ago at Automattic our ad revenue was way down from what our marketing team expected it to be. For example (and I’m making up numbers here), for every $100 we were spending on ads, we had been making $150 historically, but in recent months we were making $25. Either the performance had gone way down or there was some issue with the tracking and reporting.
I was on the data team at the time and volunteered to work with the marketing team to investigate. As it turned out, there was indeed an issue: there was a problem with the way AdWords was appending UTM parameters to our URLs which was breaking our tracking. For example, a visitor would click an ad and land on wordpress.com/business&utm_source=adwords – note that there’s an amperstand after the URL path instead of a question mark, so the correct UTM source wouldn’t get tracked and the customer wouldn’t get attributed to AdWords.
Fortunately, we had some event tracking set up on these pages (Tracks for the win) that recorded the full URL, so I was able to go back and determine which customers came from ads and calculate what our actual return on ad spend was. After figuring out the issue and determining how much unattributed revenue we had, I wrote up a lengthy post about what happened and published it on our internal marketing blog without informing the marketing team about it first.
Second example: a few months ago at Help Scout, we had an ambitious revenue target for Q1. With a few days left in the quarter, we were still projecting to come in short of the target and no one realistically expected us to reach it. Something about the projection seemed off to me so I dove in and realized there was a mistake in one of the calculations (it was my fault – in the projection we weren’t counting revenue that we earned that month from customers that were delinquent who then became paying again). As a result, our projection was too low and we likely were going to hit our target (and eventually did!). I wrote up a lengthy message about what happened and published it in one of our company Slack channels without informing any of the leadership about it first.
To understand the problem, it’s important to note that as a data analyst, I haven’t typically been responsible for the performance of our metrics. I help set up tracking and reporting and help ensure accuracy, but someone else in the organization is responsible for how well those metrics were doing.
In both of the cases above, I wasn’t intentionally bypassing people. At the time, it was more like “oh, hey, there’s a bug, now it’s fixed, better let everyone know about it” – and probably an element of wanting credit for figuring out the issue too.
However, not consulting with those responsible for the metrics before reporting it was a mistake for several reasons:
- They didn’t have an opportunity to help me improve how the issue and impact were communicated with the rest of the company and its leadership.
- I missed an opportunity to have them doublecheck the revised calculations, which could have been wrong.
- Even though we were doing better than we had been reporting in both cases, it may have indirectly made people look bad because they had been reporting performance based on inaccurate data. They should not be finding out about the issue at the same time as the rest of the company.
In neither case was there any big drama about how I went about it, but it was a mistake on my part nonetheless.
Here’s what I’d recommend for anyone in a similar role: if someone else in your organization is responsible for the performance of a metric and you as a data analyst discover some issue with the accuracy of that metric, always discuss it with them first and collaborate with them on how it is communicated to the rest of the company.
It sounds obvious in retrospect, but it’s bitten me a few times so I wanted to share it with the hope that it helps other analysts out there avoid similar issues. Soft skills like this are incredibly important and worth developing in parallel with your technical skills.
If you’ve made any similar mistakes or have any related lessons learned, I’d love to hear about them in the comments or by email. Cheers!