The rise of Data Product Management: Data from LinkedIn, Glassdoor, and Google
Tracking the growth of data product management over the years
At the end of 2021, nearly two years after I'd started using the Data Product Manager (DPM) job title officially (though it's a job I first started doing back in 2017), I made an observation1: Data product management was on the rise.
More and more organisations were realising that they needed a Product Manager / analytics translator / consultant-esque person to help drive value out of their costly investments in data, analytics, and AI2. A smaller number of data orgs were similarly realising that they were de facto employing such people, who had often taken up tasks like user discovery, value mapping, and feature prioritisation organically (which is one of the most common Product Manager origin stories, yours truly included).
So I thought I'd start collecting data on this, and see how my prediction fares in the coming years. Since the summer of 2022, I've been taking snapshots from LinkedIn and Glassdoor of the # of people with a DPM-related job title, as well as DPM-related job postings. While this is far from the best methodological approach (see the 'Limitations' section later on), it's been easy to take 5 minutes every few weeks and write down a few numbers.
The trends I saw roughly matched what I was anticipating a couple of years ago:
The number of DPMs and the DPM job title has increased significantly (+51% since June 2022)
DPM has overtaken the data/analytics translator role (from 2020-onwards)
DPM is more commonplace than DPO
Show me the data!
Alright, here you go. My amateur-level ‘research’ on the popularity of the DPM job title among the denizens of LinkedIn:
I looked at the number of DPM+DPOs on LinkedIn, first without a geographical filter (black points), and then for the US and the UK. Why not any other countries, you rightly ask? Laziness. Or, well, the knowledge that if I had to manually extract 20 different data points every time I remembered to do this, I’d likely give up and end up with zero data. When you collect your KPIs manually, it’s essential to consider the likelihood your human data collectors will give up or give you wrong data, after all.
I also wanted to look at Analytics/Data Translators, a job title that I first came across in 2018 when McKinsey championed it. I kept the y-axis equal to the one for DPMs to make it easy to compare the two.
I also took to Google Trends, which I trust more than my ad hoc LinkedIn queries, but is also less precise in what it measures. The pattern holds:
Of course, these results are all relative to one another, nothing close to overall search volume. For reference, here's how Data PM compares to DS and PM searches:
But hey, August 2023 was the first time DPM was at 1%, rather than <1% compared to the other two. To the moon!
Another comparison I looked at was with the "data products" and "data as a product" keywords. I was expecting data as a product to have spiked a lot more aggressively in the last couple of years - another reminder that my LinkedIn feed is a bubble.
What about job postings?
The LinkedIn data in the previous section can be a bit misleading - what about folks who used to be Data PMs, but aren’t anymore? LinkedIn would count those too (unless they’ve deleted that info from their profile, which would explain for example the decrease from Aug 2023 to Sep 2023). So, let’s look at job postings too.
The data here has been a lot less reliable - I think the way LinkedIn returned results to my queries changed, first between Feb and Jul 2023, and then again more recently.
Besides data quality, there’s the broader issue with using LinkedIn to understand recruitment - a lot of these jobs aren’t advertised in public channels, but instead get filled internally or via word of mouth. But still, it was interesting to see the drop in open roles from H2 2022 to 2023 (and lines up with the wave of layoffs + hiring freezes in tech).
Bringing in Google Trends again:
Google Trends data here isn’t illustrating the demand for DPMs - probably more of a proxy for DPM job-seekers looking for such roles.
Limitations of this research
No data cleaning: I couldn't extract postings, and instead just had to note down the snapshot count. Sometimes, when you search for a keyword or phrase on LinkedIn Jobs, even if it's wrapped in quotation marks, you don't quite get what you'd expect. From the spot-checks I did over the months, this seems to mostly be an issue for the job posting results, and less so for # of people results.
Non-exhaustive search phrases: I did not include "AI Product Manager", "AI Product Owner", "Data Science Product Manager" and so on. I suspect "AI" ones will get more popular over time.
Manual, ad hoc data extraction: I repeated the searches used to populate these charts on an ad hoc basis, so some trends might be exaggerated due to randomness (and others might be understated). For example, I didn't take any snapshots between Feb 2023 and Jul 2023 when life got in the way - was the growth linear? Was there a larger peak followed by a dip? Not sure.
Lack of self-identification as DPM: Many people doing the work of a data product manager don't necessarily know that that's what they're doing, or for other reasons don't self-identify as DPMs (e.g. because their job title is something industry- or company-specific, or because they'd rather brand themselves as PMs more generally)3
Confusion/disagreement over the DPM job title: This used to be a lot more prominent, with folks using the term to mean "Product Manager who uses data analytics to better understand their user base". Thankfully, with the rise of data products and data product management, this has become less common (though Udacity's not-a-DPM-course course is still the top result when I google data product management... sigh4). So, again, this data is all about what people put down on LinkedIn, not about the job they're actually doing. Just like how there are lots of folks with "product manager" as their job title, but the orgs they're in force them into being little more than project managers - it is what it is.
Like with lots of other data, my advice is to focus less on the exact absolute numbers and look more at the proportions and directional changes: For example, that there has been a sharp increase in the number of people with DPM-related job titles, or that Analytics Translators are a lot more rare than DPMs.
Conclusion
If you're reading this, you're probably not surprised by the data I've shown, just like I wasn't. Some of the growth in the DPM role has been due to more organisations waking up to the need for bringing product management / analytics translation / value realisation into the fold.
Cynically, I also think a lot of it has come from the slightly more mindless and superficial "copy what seems to work without fully understanding it" trend we see continuously with traditional organisations trying to catch up on all sorts of digital, data, and technology topics. As I wrote in my last article, this stems from seeing what 'good practice' looks like, rather than understanding deeply what good practice is.
Still, adopting the nomenclature of data products and data product management is a good starting point. Knowing what to Google is sometimes half the battle, and so as more of us share what we've learned the hard way about building data products, I hope that'll mean the easier it'll be for newcomers to catch up and overtake us. At the same time, I'm certain that I have lots of hard lessons ahead of me too - we're only just getting started!
You could also call this a prediction. It’s both, really.
I wrote some brief thoughts about the phenomenon of far too many orgs wondering why their expensive data initiatives aren't paying off back in August 2022
I also suspect this has changed significantly in recent years, thus explaining a big % of the trend seen in the data: In some cases, it’s a realisation that someone’s job would more accurately be called a DPM, while in (a lot of) others I suspect it’s more a case of companies jumping on the bandwagon of data products and just renaming their Project Managers into ‘Product Manager’. Call me a cynic if you like.
I don't want to labour the point too much, but there are several courses and videos out there that confuse "Data Product Management" with "Data-Driven Product Management". I'm not talking about the relatively esoteric philosophical debates about what 'data products' are (I'll rant about that another time, promise) - this is plain bad English in my opinion. I am of the very strong opinion that we should aim to be precise with the words that we use, avoiding ambiguity (or worse, flat-out errors) wherever possible.