Product operations (product ops), the product management discipline’s latest way to track and understand the inputs that drive product strategy, is important. Far too long companies have had it “easy” in product development accounting, as projects were often “done” when they were shipped, and the numbers were rarely investigated more than superficially after that point.
Work from Melissa Perri (CEO at Produx Labs) and Christine Itwaru (Director of Product Operations at Pendo) in the public sphere, as well as some of the work here at Mind the Product, highlight the importance of product operations. To the uninitiated, product ops represents a systematic way to connect your data and processes to product development outcomes. If you’ve ever wondered what product development “costs”, and tried to model it in a way that helps you forecast, product ops is your skill.
As a coach for product development leaders at Approaching One, I often chat with leaders about product ops, especially at smaller companies. A very common topic is how they can engage with systematic product ops practices, and use them to make better product decisions with less waste. Of course, this is after the company has committed to embracing product operations. If your organisation isn’t there yet, I recommend looking at Melissa and Christine’s work (linked above) and advocating for it, if you’re encountering that kind of systematic “leakage” in your data and processes.
Over the course of my conversations with leaders, I’ve noticed a few common product ops pitfalls:
- A lack of data fluency
- No insight into learning opportunities
- Failing to think about innovation
These pitfalls leave teams not getting the most out of the product ops they’ve set up – which is doubly frustrating for them because the most common goal of starting to deploy product ops is to gain efficiencies! I want to talk about all three, and ways you can immediately use the data you are collecting to get more out of the product operations work that you are already doing.
1. Lack of Data Fluency
Many of the tools we use make sure they can import and export data. It’s overwhelming, because suddenly data exists in multiple forms, in multiple tools, for various purposes.
The key issue I hear about from product leaders isn’t a lack of data, it’s a lack of alignment around that data. To gain organisational alignment, the organisation must have a level of data fluency.
What is Data Fluency?
Data fluency is the ability to understand the data that you collect, and being able to apply it to something meaningful. It isn’t how much data you have, but how effectively you can use it.
As a product leader, you’ll need to be able to judge your team’s ability to understand the data you’re using to achieve business goals. Ask yourself this question:
If I randomly asked someone in the organisation to tell me what the most important KPI is, where they get the data from and why… could they answer it meaningfully?
For most teams, the answer is a slightly sheepish “No”. This is a sign that, even though you are collecting good data, there is an opportunity to evangelise better practices and alignment. For product ops to be useful across the organisation, you’ll need to make sure that people understand why the data is important, what are the critical sources of data, and of course – what the key metric is.
If there is a lack of understanding, well, that’s a great lead-in to our next section…
2. No Insight Into Learning Opportunities
Great product teams aren’t judged just by their ability to create things, but also to learn. The more a product team learns in service to a company’s mission, and uses those learnings to create impact while minimising risk, the better the company’s “decision fitness” tends to be.
Learning doesn’t just come with a launch. Every interaction with a customer can lead to learning that improves the decision fitness of the entire company. More importantly, when you have solid product operations going, the data you collect should give you insight on how learning is going across the company, as well as the gaps in the company’s knowledge.
It’s worth asking yourself this question:
If I asked people what was the last major learning we had as a company – would people have similar answers?
If teams are unsure when and where learning opportunities are coming from, then any progress you make on “insights” is random, at best. The point of product ops is to tie numbers to outcomes to improve those numbers, and create a consistent cadence of learning opportunities.
Speaking of improvement…
3. Failing To Think About Innovation
How is your research and development (R&D) going?
If you are wondering if you have R&D, you do – if you are a young startup, then your entire existence is R&D. If you are a larger company, you still have R&D – and if you aren’t intentionally managing it, then it’s happening in pockets, organically.
In these ad-hoc situations, this turns into “science experiments”, or projects that come from the bottom up. You’ve probably got a rogue executive with interesting, creative ideas, who may or may not be in line with the vision of the product.
This might sound like a great idea initially, however, too many uncoordinated science experiments in a company dooms it to being a hamster wheel. If you ever wondered why your team seems to be in the same place despite constantly trying out ideas and experiments – well, I’d bet that you are in the middle of an uncoordinated treadmill.
Ask Yourself Again – how is Your R&D Going?
Innovation can happen randomly and organically. However, if you want it to happen consistently, you need some process. If you find yourself in a place where you feel things are the same, like an innovation treadmill, it’s worth investigating that process as a whole.
Product ops is designed to give you a place to start your investigation. What’s new? What’s different? Where are we gathering information? Is that information useful? How impactful was our last experiment?
Those questions (among others) will help you understand if you are on that innovation treadmill, or if organisational creativity is a random, chaotic process. If you find yourself there, asking those questions and making changes to either stabilise or energise can bring some consistency into your innovation process.
You’ll find some great opportunities through your product operations data.
Conclusion
Product operations are important. The data we collect there can lead to massive insights that scale and build across the entire organisation. Having it available and consistent is a huge plus when it comes to finding alignment, and making things happen at high velocity
That’s why it’s critical to make sure you are keeping an eye on your company’s data fluency, its learning cadence, and its R&D coordination to ensure you’re using your resources to effectively get product operations up and running, and to massively level up your product organisation.