It also creates a disconnect that happens far too often. Insights from data don’t seem relevant. Time is wasted. Everyone is frustrated and the business suffers.
This scenario should be easily avoidable. Product managers have a great sense for a product and its users. They are usually analytical enough to understand data and metrics.
Data analysts have fantastic analytical skills, and can usually communicate well enough for a non technical audience to understand their insights.
If this is the case, why does the disconnect happen? It happens because the two are often coming from different perspectives.
Consider this tweetstorm by Kaz Nejatian, who talks about how a product manager should improve a water bottle. Kaz does a fantastic job breaking down how they should think about the bottle. This requires examining different users and what each of those users are looking for in a water bottle.
Thinking in this level of detail is important for product managers because they’re involved in the entire product lifecycle. They need to get a product from 0 to 1, 1 to 10, 10 to 100, etc. If they can think through all those scenarios that Kaz mentions and make smart decisions, it’s likely that the water bottle and the water bottle business will improve. Over time, this becomes harder to do.
This is usually when data analysts come into the picture. The product has matured and more company resources are now available. Data analysts are supposed to provide insights that will continue the growth/optimization of the product.
When data analysts do get involved, they come from a different perspective. Their thinking starts with the metrics of the business. In this case, they want to figure out and share what things related to water bottles will improve customer acquisition, revenue, or retention. Once they analyze data, they make recommendations to the Product Manager.
This is where the disconnect starts.
In many cases, there’s a slight difference in what matters to each person. Here’s one example…
According to Kaz, thinking about the type of end user for a water bottle (athlete, office worker, etc) isn’t the best way for a product manager to think about improving a water bottle. However, if the data clearly shows a difference in metrics between those types of end users, that’s something a data analyst will highlight in their analysis.
The analyst may make a recommendation to try to get other user types to copy the behavior of the best performing user type. However, this approach doesn’t match up with how the product manager has thought about a water bottle previously.
Since it’s a different way of thinking about a water bottle, the product manager may feel like they aren’t getting what they need. When this isn’t reconciled, the narrative can turn into “The product manager isn’t getting the data they need from the data analyst”.
It’s not that either person is wrong, it’s just hard to reconcile the two, especially when they are so grounded in how they each normally look at things. Regardless, this can stagnate product improvement and cause friction in the relationship.
What’s the best way to solve this problem?
I don’t have all of the answers, especially from a product manager perspective. From a data analyst perspective, I can say that bringing analysts into the product lifecycle as early as possible will help a lot.
I’ve been fortunate enough to be in that situation in the past and it was great. Not only did I work with a product manager to agree upon metrics we’d consistently look at for a new product, I was also able understand how developers would log the product data. I could make recommendations on how to store the data, so when I analyzed it and shared insights with the product manager, it would automatically be in the format we wanted it.
I’m sure there are additional ways to tackle this. I’m hoping that as I get more into product, I’ll come up with some other helpful solutions that solve this problem.
If you have any additional thoughts or experience on how to deal with this, I’d love to hear from you. I’m most active on twitter @mattpupa, but will also respond to emails as well.