Greetings:
Thank you to the 60 odd folks who subscribed to this newsletter. I appreciate your intent to engage with my thoughts. I don’t take it for granted. Thank You.
In the past few months I have had a number of people who have asked something along these lines:
I just started a data product manager role and my background is Ops and UX Research for a decade.
Or
I’ve recently started director of data products at a large company. I have a decent grasp of analytics, BI, semi comfortable with data science and AI, but almost zero experience with data engineering.
If it were you stepping into a role like this, what would you prioritize?
How would you think about data warehousing / lakes, how to think about data streaming / governance, different data modelling approaches etc.?
How to think about learning the Data Engineering domain and building up a team from scratch?
The last time I put a post out on
’s led to some more queries and interactions -
It took me another 3 months to act on more of these queries to reboot this newsletter.
No I have a single-threaded focus. Well, sort of. I have been meaning to create a course on technical product management for data platforms since
I am going to dedicate some time each month, maybe some time every 2 weeks to write up a newsletter specifically covering a strategic, a tactical, and a technical aspect of data product management to guide folks who are swimming at the deep end of a technical role looking to break in; or swimming at the deep end in a data product management role and looking to deliver results and grow consistently.
Here is what I need from each of you who have asked a data product management related question to me recently:
Ask your burning questions in the comments and engage with each iteration of the newsletter.
Biggest Opportunities in Data Product Management:
For today’s iteration, consider the biggest opportunities.
I am using the term opportunities as a deliberate equivalent of challenges, and problems which trouble a large number of people willing to exchange money to solve them. Begin by setting the expectations low and the performance targets high. Opportunities are meant to be difficult and inconvenient. If it was easy it would not be worth it.
The biggest opportunities in data product management are right under our noses:
Trust and Consistency
Seamless User Experience
Deep Professional Development
Deliberate Organizational Design
Optimize Unit Economics and Profitability
You can likely guess that these are the most basic levels. Here is a simple diagrammatic representation of the opportunity spheres as concentric circles. You can see how these are related to each other and one thing leads to another.
Here is a diagram representing the opportunity spheres:
In my books an efficient business in a competitive market is beneficial to the customers, the teams, the stakeholders. The goal of product management is ultimately to improve the business. And throughout the lifecycle of products its always an optimization problem.
The core focus has to be on the customers, closely followed by the team, which leads into the organization and the business.
Starting with the foundations of opportunities, we can overlay aspects of strategy, tactics, and technology.
Here is some more context:
Trust and Consistency: Trust and consistency is the basic currency of data products. No one will spend time and money with information they don’t trust. Trust depends on data quality, insights explainability and interpretability, system reliability etc.
Seamless User Experience: Usability has historically been an afterthought in data. Arguably, it’s been one of the significant reasons for the failures of large scale data projects which promised much. Trust factors are a basic building block of user experience.
Yet there is more in UX in terms of interoperability across interfaces, interactions, accessibility which are orthogonal to trust issues. Understanding the journey of the consumer and creating an intuitive, smooth, frictionless experience is the goal.Deep Professional Development: To deliver an optimal experience to customers there needs to be professionals who consistently train, persevere, and deliver impactful solutions to customer problems. Proactive professional development and performance management is a powerful lever to influence success. Yet, it’s also the most underrated.
Professional development properly done needs to develop every team member holistically to maximize strengths and reinforce areas of improvements to make the overall system that is the org better.Deliberate Organizational Design: Effective org design is a matter of designing with the 3 previous points in mind. Key traits in people that set them apart in teams are integrity, humility, and curiosity. Assuming the basic traits, teams are better set up to be cross-functional and able to zoom in and out to apply the appropriate scope, priority, capacity, budget, and effort.
In org design there are things on team shuffles, restructures, project allocation which are strategic bets available to proactive product managers.Optimize Unit Economics and Profitability: All said and done there is no benefit in spending more in delivering incremental value that reduces profitability. Counterintuitive as it may sound constraints are an extremely powerful lever of innovation. Unlimited resources in terms of capital, capacity, compute resources produces deeply unprofitable monopoly companies who ultimately plays in the realm of ideology and not data. It’s just better for businesses to maximize efficiencies and compete fairly. It creates more opportunities and better solutions for the market. Optimizing economics forces innovation and simplifying unnecessary complexity.
This is the part where budget allocation, team growth, investments, bets, stakeholder communications etc. fit in.
These 5 opportunity areas assume a critical presupposition. It is a highly risky assumption as well. It is the law of market failure for data products.
For every product, there is 1 way to succeed and 2^(n-1) ways to fail.
Without substantive expertise any data product is guaranteed to fail.
The data and the empirical evidence is clear for all products. the odds are stacked against all products and features.
And every data product requires deep substantive expertise about the market it serves, the customers workflows, the economic levers and the number with high degrees of precision, confidence, accuracy. Without substantive expertise none of the opportunities mean anything. And substantive expertise is not merely experience working in a domain.
This is why professional development is right in the middle of the layers. Ultimately a well trained team is well positioned to compete hard. There are several tactical elements associated with all of this.
Hopefully, this sets up the foundations. Now we can think about these opportunity areas in the context of different data businesses.
Send me your questions and sticky situations. And I will figure out a polling mechanism to dive into the topics which are most urgent for you.
Rooting for all of you builders of data products.
Deb.
Welcome back! 🥁