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Data Product Management
by Tinder Group PM

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5. Data Product Management
Leverage data to build products that deliver right experiences,
to the right users, at the right time

6. Agenda
● About Me
● What is a Data Product?
● Who’s a Data PM?
● What does Data PM do?
● Facets of Data PMing
● Where can you start?
● Pitfalls in my journey

7. Hi, I’m Murali
I grew up in India, have been living in Los Angeles for the last 10 years, am an avid
racquetball player, and am excited to share my professional experiences as a Data PM
with you all today !
Business Tech Analyst -> Sr.
Consultant
Deloitte Consulting, Los Angeles
Aug 2008 - Mar 2014
Engagement Mgr -> Sr. Product Mgr
Seven Lakes Technologies, LA
Mar 2014 - Aug 2018
Lead Product Mgr
DISQO, Glendale
Aug 2018 - August 2019
Sr. Product Mgr -> Group Product
Mgr
Tinder, West Hollywood
Aug 2019 - Present

8. What is Data Product?
data product - data is the
primary facilitator to reach end
goal of the product, and it's
not just means to an end
Types of data products
- Raw Data
- Transformed Data
- Algorithms
- Insights
- Automated Actions
Raw Data Algorithms
Transformed
Data
Automated
ActionsInsights
APIs
As-Is
Viz/Explore
What data product are you building?

9. Who is a Data Product Manager
the role data product manager is
similar to that of a traditional product
manager, traditional PM’s use/treat
data like raw material, a data PM treats
data as a product
data PM’s should treat data
as a product with real return on
investment just like they would a
consumer-facing product
My definition
Medium article says - The Data
Product Manager was born! In
addition to the skills of a regular
PM, he needs advanced soft-skills
to handle more senior teams —
these professionals are very
different to other developers
it’s time to embrace data product
managers. Building effective products is simply
impossible unless someone takes the time to
understand which teams will use the data and how, to
research how to meet each team’s unique data needs,
to test out different solutions, to observe and
onboard people with the data tool, and to gather
feedback and iterate on the data product.

10. What does a data PM do?
Data PM’s typically tends to work with
one or more of these data teams
- Data Engineering
- Data Analytics/Data Science
- Experimentation
- Machine Learning
data-informed decision making data-powered product building

11. Data PM needs to be much more than data-driven.
They need to be able to a build a data-informed
culture in the organization
- Take the data management out of the
hands of individual contributors
- Keep data sets and insights centralized
- Choose and maintain your product’s
Product Data Management software
- Ensure data quality is paramount to all
teams working with data
- Drive the conversation around data
governance
Facets of Data Product Management

12. Source: https://mattturck.com/data2020/

13. Where can I start?
Working with data at the core of a
product requires an understanding of
- Data modeling
- Data infrastructure
- Statistical & Machine Learning
- Data analysis
- Data viz
- Data translation
- Data governance
and, there’s much more to it...
First, understand the flow of data
Capture
- How is data
collected?
Process
-Ingested?
-Streaming vs
batch
processing
Store
-Data lake?
-Data
warehouse?
-Lakehouse?
Use
-Insights?
-Actionable
Insights?
-Data-driven?
-Data-informed?
Analyze
-ETL vs ELT vs
EtLt

14. Understand the data pipeline
Source: https://thecloudgirl.dev/analytics.html

15. Skills to use/acquire
- Clearly defining data goals, value, and vision within the
organization
- Building a short-term and long-term data roadmap
- Bridging the gap between engineers, analytics, and other data
consumers
- Aligning internal and external data customers to a single
cohesive plan
- Clearly mapping out key data consumers and personas
- Driving enterprise adoption of data products
- Facilitating data literacy in an organization
- Helping break down huge data initiatives into manageable
chunks which can be delivered incrementally
- Adding Agile project management principles into the data
organization by also playing the role of product owner and/or
facilitator

16. Pitfalls in my journey
DONT’s DO’s
● Ask stakeholders for problems - not solutions
● Get to the root cause of the problems
● Multiple problems could have a common
solution
1 Listening to stakeholder
solutions to the existing
problems
● Collect feedback from cross-functional teams on
the pain points
● Check if there are third party solutions which
solve the pain points
2 Engaging with too
many vendors
● Using methodologies/processes/tools that are
tried and tested will help
● Ensure everyone of your teams is onboard with
the idea of getting a new process/tool
3 Being an early adapter

17. Key Takeaways
Understand the flow of data
Know your consumers better
Build up on your EQ

18. Contact
Murali Bhogavalli
murali.bhogavalli@gmail.com

19. www.productschool.com
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