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Amritha's LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/

In this episode, Scott interviewed Amritha Arun Babu Mysore, Manager of Technical Product Management in ML at Amazon. To be clear, she was only representing only own views on the episode.

In this episode, we use the phrase 'data product management' to mean 'product management around data' rather than specific to product management for data products. It can apply to data products but also something like an ML model or pipeline which will be called 'data elements' in this write-up.

Some key takeaways/thoughts from Amritha's point of view:

1. "As a product manager, it's just part of the job that you have to work backwards from a customer pain point." If you aren't building to a customer pain, if you don't have a customer, is it even a product?
2. Always focus on who you are building a product for, why, and what is the impact.
3. Data product management is different from software product management in a few key ways. In software, you are focused "on solving a particular user problem." In data, you have the same goal but there are often more complications like not owning the source of your data and potentially more related problems to solve across multiple users.
4. In data product management, start from the user journey and the user problem then work back to not only what a solution looks like but also what data you need. What are the sources and then do they exist yet?
5. Product management is about delivering business value. Data product management is no different. Always come back to the business value from addressing the user problem.
6. Even your data cleaning methodology can impact your data. Make sure consumers that care - usually data scientists - are aware of the decisions you've made. Bring them in as early as possible to help you make decisions that work for all.
7. ?Controversial?: Try not to over customize your solutions but oftentimes you will still need to really consider the very specific needs of your consumers. Build for reuse but also build where your consumers are actually having their needs met. A mediocre solution for all is usually worse than a few specialized solutions.
8. Prioritization is crucial in product management. That applies to features within the products but also the products themselves. There are many potential use cases that won't be met because there isn't enough value. That's the name of the game, return on investment; it's not about capturing all value possible.
9. Communication and building relationships/trust are foundational in product management. It's an art as much as a science. If you can't have tough conversations and get alignment, it is FAR harder to build a product that meets customer's needs.
10. Relatedly, establish regular communication with your customers. You shouldn't only be talking to them when things go wrong. Stay on top of what is driving value for them and look to augment your product proactively, not only reactively.
11. Product management requires patience as much as diligence. Sometimes your data product/element violates its SLAs but it's an outlier, a one-off. Don't look to overreact and jump to changing things. But you obviously need to have serious conversations if elements aren't meeting expectations over a more extended time period.
12. If you aren't sure what products you should create in a new area, talk to people and find the points of friction. What are the pain points and is there enough value in addressing them to justify doing the work?
13. It's crucial to deeply converse with potential users of a data product/element to assess if it's really going to be worth the effort. There is always a chance you build something that isn't used/valuable but through deep investigation and ideation with potential customers, you can avoid that far more often.
14. When you are building something, even before it hits 'GA', get validation. You can save yourself a ton of effort in rework as you find a better solution sooner.
15. Product management is about collaborating to drive towards value. You are there to prioritize and coordinate. You don't have to know everything, but your job is to uncover as much understanding as possible to maximize your value creation and minimize wasted work.
16. Always ask what value building something for your customer will drive. But also ask what happens if we don't build it. What is the cost of not acting?
17. The only constant is change, especially in data. Leverage a "loosely dependent architecture" to be able to adapt to change. And be open and honest with customers that things will change. Emphasize you'll work with them to adapt to those changes.

Amritha started the conversation on some key differences between software product management and product management around data - whether specific to 'data products' or not. One similarity is the focus on solving a particular user problem but in data, you might have to build something to address multiple users' problems. A much bigger difference is that in data, you often don't own the entire process as you might be reliant on others to source your data. In software, you are generally building the data sourcing because you own the interaction creating the data. How the data is stored and collected throughout the upstream process impacts what you can do.

The user problem, the business value, and the user journey are some key guides to doing data product management well for Amritha. Keep coming back to those as you build out your solution. Focus on understanding what the user really needs and work backwards to the sources. And then of course focus on making sure you are actually addressing user needs when you deploy the solution. There are many reasons a data element may not be performing up to expectations so be prepared to deep dive; is there a problem with what you've built, what's feeding your data element - maybe sources have changed or there is a quality issue -, or is it just not performing to expectations because the hypothesis was wrong?

Amritha dug a bit more into some challenges specific to product management in machine learning and AI. While data scientists want clean data, when possible they want to even be part of the process of selecting the cleaning methodologies - even that can impact the data enough to change outcomes. So really start from the process of bringing them in as a stakeholder as soon as you can and don't throw data over the wall at them. And if you already have something developed, share your methodologies and help them figure out if it's the right fit for them or if something new needs to be developed. Again, we want reuse but we also want solutions that address their problems. Always a hard set of needles to thread.

"As a product manager, it's just part of the job that you have to work backwards from a customer pain point." Amritha questions if you are even building a product if you don't have a customer. What is the business value of the work? For a product manager of product without a customer, are you focused on your own thoughts and biases rather than the needs of consumers? "So the point here is that at any given point, you have to be cognizant of who are you building this for, why, and what that is the primary customer. And the secondary is: who else if I build this, what are the impacts it will have on my secondary customers, or other downstream or interacting applications?"

Amritha talked about one crucial rule in product management: prioritize. There are many use cases you _could_ solve but are they actually worth the effort? Think about what will impact your organization the most. Don't try to solve every use case and don't try to make products that can serve every potential customer - focus on delivering value. Scott note: this can be a slippery slope in data mesh. You want to take on use cases you actually can tackle when you are learning. Don't only go for the biggest value but also tackle problems where the juice is worth the squeeze, where the outcome is worth the effort.

In product management, Amritha believes it's absolutely crucial to understand the art and the science. The science is more about is this product specifically meeting the needs it was designed for. Basically, measuring the level of success and determining if that's good enough or especially is it _still_ good enough. But even that last bit can be a bit of art. The real art is all about communication and building relationships. If you build the world's objectively best product but no one trusts it or understands it enough to use it, it's not a valuable product. You must build strong relationships and have the tough conversations with stakeholders, earning their trust, to align on what needs to get built and why as well as when a product isn't meeting expectations. Establish regular lines of communication so it's not that the only time you talk to your customers, it's bad news or big changes. Continue to extract information from them to drive to business value.

When it comes back to the science, that's when Amritha believes you should dig into the why something isn't meeting expectations from the technical perspective :) And have some patience around that. Sometimes it's a blip on the radar, not anything more.

When figuring out what products/data elements you might want to build in a specific area, Amritha recommends digging into the potential workflows and user journeys. Start to really think about what you think could exist and why. But, instead of trying to ideate only yourself, go and talk to people and listen for their pain and points of friction. They may not even realize they have pain but you can find the challenges that people will want to address. Again, work backwards from the user journeys to discover what products you should build 😅

Amritha talked about how to make maximize the chance that what you're building will be used/valuable. A lot of it is simply digging in deep with potential customers in the ideation phase to make sure this will actually drive value. There are ways to do that but a lot of it is simply spending the time to really understand the likely impact of what you're building. As Alla Hale said in episode #122, "What would having this unlock for you?" Also, ask, "what if we don't do this, what is the impact of not doing this?" And make sure to get validation as you're building. It might be the value hypothesis was wrong or that you're building something that is the wrong or suboptimal way to address the challenge/opportunity. You can save yourself a lot of headaches and rework. It's all about that collaboration to drive to value.

In wrapping up, Amritha talked about how changes, especially in data, are inevitable. Make sure to communicate with consumers so they have realistic expectations. Sometimes those are proactive changes but often, you don't have that much control over changes, especially coming from upstream in data. Look to build in a way that can adapt and leverage a "loosely dependent architecture".

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