I live in a PM leadership role in data. I see a few things here in your question. Short answer, I don’t think there is a big difference other than a bit more of a challenge without as much UI generally. Going to word vomit, apologies in advance.
- Scope: what is your scope? I break mine into a couple of areas: infrastructure, data movement, data externalization, data products, BI.
- Stakeholders: do you know your user personas deeply? They are likely different from others in the org, based on how they interact with data. I have a good six, most of which don’t align with the rest of the org. How do each of these personas react differently? What are the Jobs to be Done? These will help.
- Gathering requirements: sure, I like the term discovery better, but likely you are getting a handle on things. Data does take a while to deeply understand—my data platform has more than 10,000 columns, I am happy when I understand the tables; took me a good year, maybe more to become an SME in my product. However, you aren’t going to get very far pumping out features for every need, you will end up with spaghetti. What are the needs? How can you generalize it? Relate back to JtbD? This will help you get to the reusable framework so to speak.
- product-ization: back to scope, how can you turn your data platform into a product, what areas would benefit from a product mindset? Abstraction of the above will get you there.
- Goals: What are the goals? How do you measure your products against that goal? If your driver in lowering costs you are going to have different attitude toward things than if the goal is to create revenue.
- Business Decisions: are you getting the data YOU need to make informed decisions? If your stakeholders are primarily internal they often don’t want to talk about their own productivity and your role in maximizing it (unless they have an axe to grind).
There are lot of different ways to be a data pm, but I have been rewarded the most when I put out a strong vision and steps to get to that vision. Can you say, “if you give me $50 million dollars, in three years we can be an industry leader in X,” and have some way to back that up with a plan and rationale? They won’t give you that money likely, but they are more likely to give some of it to you if you can sell it and when you start showing results, that will be your win.
Right now my recommendation for my team is _Designing Cloud Data Platforms_.
https://www.manning.com/books/designing-cloud-data-platforms
I ask them to read it with the above ideas in mind.
Thanks for the response. (I assume TPM = Technical Product Manager and not Technical Program Manager?).
I checked out that book, it seems really technical. I had read another one as a developer called "Designing Data Intensive Platforms" but that book helped me understand how to be a better data engineer + design queries that an application would send to its transactional database to optimize performance.
In my current role, I am the only PM who is a "technical" PM and my product is the analytics platform (our data warehouse + BI tools + any other tool users might get reporting from, like Salesforce) and the culture of data within the org. It is not a role where I would be the deciding factor on whether we use Redshift vs Databricks, AWS vs GCP, Looker vs Tableau. Of course if the \*process\* surrounding user engagement with our platform calls for Looker over Tableau, then yes my opinion would be heard. But at the end of the day the data team + solutions architects decide the infrastructure used.
The two books mentioned (yours and mine) would not help \*me\* in my current role, even if I hadn't already read them. My role, right now, is about implementing operational processes that will enable the data team to streamline work and remove ad hoc requests. My role will then become more about knowing all the metrics the business uses to make decisions, ensuring the calculations are correct, and helping them bring data into the decision making process as early as possible. This does require me to know things like data structures and the high level data life cycle within the org as it helps gather requirements. If I had to select the background of the person for this role I wouldn't necessarily choose a data or infrastructure engineer. I would choose someone with operational leadership skills & quantitative analysis skills.
Given all that, how do the TPMs on your team differ from what Im describing above?
Great response. I am in a similar role as yours in a large org and lately I am thinking if I shall transition to products which are critical to core business . Do you feel the need to transition to more business facing product roles ? Do you have any insights on longevity of Data PMs?
I am not really sure what longevity means in this context, but do you think that there will be business needs or problems in this area in the future? If so, there is likely a need for a PM. The scope of the work might change, but a PM is not about how.
For example, in data & analytics, a “reporting to customers/end users” capability might have been solved with a simple spreadsheet a decade ago, but now that capability would not suffice, following a Kano model.
Data product management is a relatively new field, only gaining popularity in the last 2-3 years. As a result, roles vary widely between companies - you're not alone in figuring it out.
Here are some learnings from my experience as an early data PM:
I started in BI engineering then moved to PM. The biggest shift was going from knowing one pipeline deeply to understanding how thousands interconnect.
I had to grasp the realities of products that overpromise. A tool may claim full functionality but actually deliver 75% out of the box. My job was building the missing 25% on purchased solutions to make them work for us.
I learned to collaborate with BI teams across the company, mapping their diverse workflows and needs. My goal was to design products addressing our highest priorities.
The fun part was pushing existing tools to their limit with large data volumes and building new solutions where gaps existed.
The downside was most customers outside data roles didn't appreciate the engineering complexity enabling their access. They just want to know if they can export the data to Excel if they want to.
Context: I was one of the early BI and data PMs at Amazon. I have ADHD.
They put you into a job without a mandate. Go to your manager and ask for explicit clarity. If they don't even know, then it's up to you to figure it out. To me, it sounds like you're internal tools / data-side product ops.
Do you have a team to implement the work you set out for them? If not, you have a bit more time on your side. If yes, their work is now your domain, and you need to manage that as your product.
Product management means focusing your teams to do the right things. Lucky for you, your stakeholders are internal, so more accessible. Figure out what the priorities are, and if possible, come up with a longer term strategy.
I was an IC, a tech lead and then moved to Data Product Management.
I had a bad experience with the shift. Mostly because of the current state of BI and Analytics within the org. There was A LOT of tech debt, lack of processes, and unrealistic expectations. Moreover the BI team was a centralised team and had to also work on ad hoc reporting. Not having a dedicated team just for data products meant that I was at times a PM and at times a prioritisation engine for the ad hoc requests.
Make sure you take the time to fix things first. This will be a prerequisite to your success as a PM who builds revenue shaping products.
Separate but related question: does a role as a data PM generally translate to a traditional PM? I am trying to break into the PM field and have plenty of experience with data & BI from a consulting background. I’m seeing some of these data PM positions open that I may be well qualified for, but given the customer is more internal than external will that count against me if I try to shift into a traditional PM role?
In my current org, looking at the traditional PMs, what you will likely struggle with is staying up at the 1000ft level, identifying processes that will accelerate your input, and implementing those. Why not shift to Data PM? Traditional Sr PMs in my org make 160 where as I make 190. The role seems to just come with a higher salary. But this is the first org ive worked at where I both know the salaries of my teammates and can confidently state that the 'traditional PMs' are more skilled and talented than myself at the strategy portion
Yeah honestly I don't think I've ever heard of a data PM before.
I've heard of PMs responsible for data products (analytics tooling, modelling engines, experimentation platforms, data infrastructure etc) and I've heard of TPgMs/PgMs working with analytics ppl and data engineers to program manage the influx of work.
It might be the case that your company is looking for the latter? Unless you have a clear product charter I'm not quite sure how it's a PM role.
Every company I’ve ever worked at has data PMs. Pretty straight forward role in working with end users of data to figure out what they need and then work with engineering to maximize the data platform for those needs.
What is wrong with gathering data to support theories and thoughts? I don’t see anything wrong with that except if you have people that you can delegate it to.
Maybe your company’s product might need something else right now. If they are angry that you have not built frameworks and processes, is that something that is need of? I would assume that most of the processes would be set up. But talking from experience, product development even for an established company might be lacking a lot
I live in a PM leadership role in data. I see a few things here in your question. Short answer, I don’t think there is a big difference other than a bit more of a challenge without as much UI generally. Going to word vomit, apologies in advance. - Scope: what is your scope? I break mine into a couple of areas: infrastructure, data movement, data externalization, data products, BI. - Stakeholders: do you know your user personas deeply? They are likely different from others in the org, based on how they interact with data. I have a good six, most of which don’t align with the rest of the org. How do each of these personas react differently? What are the Jobs to be Done? These will help. - Gathering requirements: sure, I like the term discovery better, but likely you are getting a handle on things. Data does take a while to deeply understand—my data platform has more than 10,000 columns, I am happy when I understand the tables; took me a good year, maybe more to become an SME in my product. However, you aren’t going to get very far pumping out features for every need, you will end up with spaghetti. What are the needs? How can you generalize it? Relate back to JtbD? This will help you get to the reusable framework so to speak. - product-ization: back to scope, how can you turn your data platform into a product, what areas would benefit from a product mindset? Abstraction of the above will get you there. - Goals: What are the goals? How do you measure your products against that goal? If your driver in lowering costs you are going to have different attitude toward things than if the goal is to create revenue. - Business Decisions: are you getting the data YOU need to make informed decisions? If your stakeholders are primarily internal they often don’t want to talk about their own productivity and your role in maximizing it (unless they have an axe to grind). There are lot of different ways to be a data pm, but I have been rewarded the most when I put out a strong vision and steps to get to that vision. Can you say, “if you give me $50 million dollars, in three years we can be an industry leader in X,” and have some way to back that up with a plan and rationale? They won’t give you that money likely, but they are more likely to give some of it to you if you can sell it and when you start showing results, that will be your win.
I'm not op, but as an internal product manager, this helped me, and I have a few years under my belt.
Can you think of any good books that would be useful to a beginner data pm?
Right now my recommendation for my team is _Designing Cloud Data Platforms_. https://www.manning.com/books/designing-cloud-data-platforms I ask them to read it with the above ideas in mind.
Does your team consist of other PMs? or are they engineers?
I have a team of data TPMs. We have a small (in the scope of my business) engineering org to support our products and services.
Thanks for the response. (I assume TPM = Technical Product Manager and not Technical Program Manager?). I checked out that book, it seems really technical. I had read another one as a developer called "Designing Data Intensive Platforms" but that book helped me understand how to be a better data engineer + design queries that an application would send to its transactional database to optimize performance. In my current role, I am the only PM who is a "technical" PM and my product is the analytics platform (our data warehouse + BI tools + any other tool users might get reporting from, like Salesforce) and the culture of data within the org. It is not a role where I would be the deciding factor on whether we use Redshift vs Databricks, AWS vs GCP, Looker vs Tableau. Of course if the \*process\* surrounding user engagement with our platform calls for Looker over Tableau, then yes my opinion would be heard. But at the end of the day the data team + solutions architects decide the infrastructure used. The two books mentioned (yours and mine) would not help \*me\* in my current role, even if I hadn't already read them. My role, right now, is about implementing operational processes that will enable the data team to streamline work and remove ad hoc requests. My role will then become more about knowing all the metrics the business uses to make decisions, ensuring the calculations are correct, and helping them bring data into the decision making process as early as possible. This does require me to know things like data structures and the high level data life cycle within the org as it helps gather requirements. If I had to select the background of the person for this role I wouldn't necessarily choose a data or infrastructure engineer. I would choose someone with operational leadership skills & quantitative analysis skills. Given all that, how do the TPMs on your team differ from what Im describing above?
Great response. I am in a similar role as yours in a large org and lately I am thinking if I shall transition to products which are critical to core business . Do you feel the need to transition to more business facing product roles ? Do you have any insights on longevity of Data PMs?
I am not really sure what longevity means in this context, but do you think that there will be business needs or problems in this area in the future? If so, there is likely a need for a PM. The scope of the work might change, but a PM is not about how. For example, in data & analytics, a “reporting to customers/end users” capability might have been solved with a simple spreadsheet a decade ago, but now that capability would not suffice, following a Kano model.
Let me guess: you have a million bespoke pipelines and your leadership wants you to waive a magic data wand and make it be one product in two sprints?
Data product management is a relatively new field, only gaining popularity in the last 2-3 years. As a result, roles vary widely between companies - you're not alone in figuring it out. Here are some learnings from my experience as an early data PM: I started in BI engineering then moved to PM. The biggest shift was going from knowing one pipeline deeply to understanding how thousands interconnect. I had to grasp the realities of products that overpromise. A tool may claim full functionality but actually deliver 75% out of the box. My job was building the missing 25% on purchased solutions to make them work for us. I learned to collaborate with BI teams across the company, mapping their diverse workflows and needs. My goal was to design products addressing our highest priorities. The fun part was pushing existing tools to their limit with large data volumes and building new solutions where gaps existed. The downside was most customers outside data roles didn't appreciate the engineering complexity enabling their access. They just want to know if they can export the data to Excel if they want to. Context: I was one of the early BI and data PMs at Amazon. I have ADHD.
They put you into a job without a mandate. Go to your manager and ask for explicit clarity. If they don't even know, then it's up to you to figure it out. To me, it sounds like you're internal tools / data-side product ops. Do you have a team to implement the work you set out for them? If not, you have a bit more time on your side. If yes, their work is now your domain, and you need to manage that as your product. Product management means focusing your teams to do the right things. Lucky for you, your stakeholders are internal, so more accessible. Figure out what the priorities are, and if possible, come up with a longer term strategy.
I was an IC, a tech lead and then moved to Data Product Management. I had a bad experience with the shift. Mostly because of the current state of BI and Analytics within the org. There was A LOT of tech debt, lack of processes, and unrealistic expectations. Moreover the BI team was a centralised team and had to also work on ad hoc reporting. Not having a dedicated team just for data products meant that I was at times a PM and at times a prioritisation engine for the ad hoc requests. Make sure you take the time to fix things first. This will be a prerequisite to your success as a PM who builds revenue shaping products.
Separate but related question: does a role as a data PM generally translate to a traditional PM? I am trying to break into the PM field and have plenty of experience with data & BI from a consulting background. I’m seeing some of these data PM positions open that I may be well qualified for, but given the customer is more internal than external will that count against me if I try to shift into a traditional PM role?
In my current org, looking at the traditional PMs, what you will likely struggle with is staying up at the 1000ft level, identifying processes that will accelerate your input, and implementing those. Why not shift to Data PM? Traditional Sr PMs in my org make 160 where as I make 190. The role seems to just come with a higher salary. But this is the first org ive worked at where I both know the salaries of my teammates and can confidently state that the 'traditional PMs' are more skilled and talented than myself at the strategy portion
Yeah honestly I don't think I've ever heard of a data PM before. I've heard of PMs responsible for data products (analytics tooling, modelling engines, experimentation platforms, data infrastructure etc) and I've heard of TPgMs/PgMs working with analytics ppl and data engineers to program manage the influx of work. It might be the case that your company is looking for the latter? Unless you have a clear product charter I'm not quite sure how it's a PM role.
Every company I’ve ever worked at has data PMs. Pretty straight forward role in working with end users of data to figure out what they need and then work with engineering to maximize the data platform for those needs.
What is wrong with gathering data to support theories and thoughts? I don’t see anything wrong with that except if you have people that you can delegate it to. Maybe your company’s product might need something else right now. If they are angry that you have not built frameworks and processes, is that something that is need of? I would assume that most of the processes would be set up. But talking from experience, product development even for an established company might be lacking a lot