Data-Driven Product Management
Jon Loyens is the Co-Founder and Chief Product Officer of data.world. In a past life, he was the VP of Engineering for Traveler Products at HomeAway, and before that, a VP of Engineering and Director of the Labs group at Bazaarvoice. As a long-time technology executive in Austin, he has seen the rise of data and analytics as a democratizing force. Jon brought A/B testing and data-driven product management to Bazaarvoice and massively expanded the data programs at HomeAway. Jon introduced GitHub to Bazaarvoice and made OSS contribute a major part of their culture. He’s seen the benefits of openness and transparency in software development and, at data.world, is bringing the same benefits to data and analysis work.
He recently spoke at a Product That Count hosted webinar and went beyond typical A/B testing talks and presented on how to align a company and its products effectively through data-driven product management. The talk started with goal setting and value alignment and went through process, tools, and strategy to create a great data-driven product management organization.
In this webinar, Jon talks about what product management is, what product management shouldn’t be, and how to use data to drive great product management. You can view Jon’s whole presentation above. The highlights of the webinar are detailed below.
Anti-Patterns in Product Management: The Ideas Person
Jon talked about what great product managers shouldn’t be doing before he got to the things they should.
“The first anti-pattern I want to call out is the ideas person. It’s the person who gets into product management because they have all the good ideas. The self-styled Steve Jobs types who think that they have the greatest ideas. In fact, I think the best product managers aren’t even ideas people. They’re really great at synthesizing and listening and being empathetic.
I once heard really great product managers compared to new music producers who are bringing all the best ideas together and figuring out how to distill them. How to align people around them, right? I love the music producer analogy because you think about bands, a lot of bands have a lot of dysfunction. A great music producer figures out how to get all of those pieces working together. All the individuals have great ideas, great creativity, [music producers help them] focus on the best of those ideas.”
On how Product Leaders can help lead a Product-Focused Organization
Representing product at the leadership level takes an understanding of the business.
“How much money did you make last quarter? If you don’t know what the revenue of your company is, frankly, you’re not in a position to lead your company. And if you expect your company to take your leadership, you need to know the fundamentals of the business, the economic value you provide, and how that business is actually run. You have to lean in there.
This isn’t a magic world where just doing right by the by users all the time it just magically creates a functional business. So, if you want to lead your company, you have to be well-versed in all aspects of your company. Be prepared to play that underlining role through all parts of the company.”
On the value of Data-Driven Product Management
Data-driven Product Management is important because it’s hard to argue opinions over data.
“I think one of the best ways a product manager can start providing that alignment with your hesitation is to bring data to the conversation to start instituting data-driven product management. When you start bringing well thought through, data-driven arguments to the table, it’s amazing how a lot of the opinion that goes into decision making in any organization can start to drop away.
Folks like Jim Barksdale would say, ‘bring data to an argument with me because otherwise, all we have is opinions. If we’re gonna go with opinions, we’re going to go with mine.’”
On Data-Driven Product Management as a science
Using data and building it into your product management isn’t the same as artificial intelligence (AI) or machine learning (ML).
“We call it product science because we want to avoid the trap of trying to make everything about building AI and ML. Instead, make it about doing the basic analytics that we need to do to understand what people are doing with our product.”
Special thanks to our partners at Amplitude for sponsoring this great webinar.