Main Takeaways: -Data Product Managers treat data as a product -Data & AI Fluency is an important core skills -Be a great storyteller -Understand Data Product Lifecycle -Data Product Success Metrics

1. How to be a Successful Data PM by Zillow
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6. 1
1
Presenters: Satish Mathew & Eduardo Melo
How to be a successful Data Product Manager

7. Hello!!!
My name is Satish Mathew
You can find me at: https://www.linkedin.com/in/satishmathew/
Current: Data Product Manager @ Zillow
Previous: Product Manager @ Microsoft
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8. What will I cover
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1. What is Data Product Management?
2. Data Product Management Focus Areas
a. Storytelling
b. Understand Data Product Lifecycle
c. Data Product Success Metrics
3. Real World Use cases @ Zillow
4. Key Takeaways

9. What is a Data Product Manager (DPM)?
Definition: A person who oversees the full lifecycle of how data is used within a company. The role is similar to
that of a traditional product manager, but with one key difference: they put data at the heart of everything they do.
Core Skills: - Speak the language of the business, technology and Data &
AI
Data Fluency: - Data Strategy, Data Governance, Data Serving Layer, Data Architecture &
Pipelines
Technical Skills: - SQL, Statistics, Basic Machine Learning & AI
concepts
Product Manager - Core
Skills
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Data Product Manager - Core
Skills

10. Why is Data so important?
Netflix is successful thanks to data and analytics.
- $150+ Billion dollar Company valuation
- In 2017, 93% of original Netflix TV shows were renewed
- What is the Netflix secret: Data & Analytics
- Netflix collects several data points from their 151 million subscribers to create a detailed profile of
customer’s behavior & movie watching habits
Why do we need Data PMs?
Without a data product manager, organizations typically fall into a couple of different categories:
1. “Data Wild West”
2. “Data Order Takers”
3. “Data Shiny Tool”
-Figure: seleritysas.com article
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11. Data Product Management Focus Areas
10
How to be a successful Data Product Manager

12. Data Product Management Focus Areas
11
1. Storytelling
2. Understand Data Product Lifecycle
3. Data Product Success Metrics

13. A product manager’s job is not simply getting products to market, but also communicating why
customers need those products in their lives
1. Stories simplify the message (help us filter out the noise)
2. Stories engage our emotions (which can persuade us)
3. Stories are easier to remember (help unify a cross-collaboration team)
Storytelling is the fastest and best way to empower your team to both understand and act on
data through the power of stories.
Why a Data PM needs to be a Great Storyteller
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14. - Storytelling helps to ensure focus on the customer, and customer pain points
- “The average product team spends 80% of their time in the solution space and 20% in the problem space.
Ideally, that time should be split 50-50 between both” - 280Group Article, 2019
-Figure: Medium.com article
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Why a Data PM needs to be a Great Storyteller

15. Understand Data Product Lifecycle
Offline Use Cases Online Use Cases
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16. So what does success look like ...
What happens when you don’t keep track of Success Metrics
Keeping track of Operational Success Metrics:
1. Data Availability
2. Data Freshness
3. Data Quality
4. Data Governance
Keeping track of Business Success Metrics:
1. Business Goals/KPIs
2. Revenue/Cost Metrics
Data Product Success
Metrics
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17. Real World Data PM Examples @ Zillow
16
How to be a successful Data Product Manager

18. Real World Use Cases @ Zillow: Example 1
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Data Problem: X number of external Multiple Listing Services (MLS) providers were not consistently
providing timestamp records for when a home is actively listed on the market.This is an example of “poor”
data quality impacting key critical KPIs - e.g. “Amount of Time a home is listed on market.
Business Impact: Multiple Zillow teams depend on the above timestamp records for
personalized home recommendations, home price calculations etc.
Where to start?
1. Always start with the customer & stakeholders (who, what, when .. build your storytelling)
2. Understand Data Source Constraints & Timelines (understand Data flow & deliverable deadlines)
3. Define what Success looks like (Data Product Success Metrics)
Possible Solution(s):
● Address data quality issues with upstream MLS provider
● Create fallback mechanisms e.g. system generated timestamp
● Find other reliable data sources that cover the same geographical region

19. Hello!!!
My name is Eduardo Melo
You can find me at:
https://www.linkedin.com/in/eduardo-melo-seattle
Current: Director of Product Management - AI & Analytics at Zillow Group
Previous: Product Management Lead at Microsoft (Bing, Office 365, Azure ML)
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20. Interview question: how to leverage AI to improve [name of our favorite
app/service]?
● Be a great storyteller
○ Customer, scenarios, unmet needs
○ Clear and compelling
● Understand Data Product Lifecycle
○ How is data and AI going to contribute to improvement?
○ When to use data/AI approaches vs more traditional approaches?
● Data Product Success Metrics
○ What are you optimizing for?
■ Adoption not always the right metric for optimization
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Real World Use Cases @ Zillow: Example 2

21. 1. Demand for Data Product Managers is growing!!
a. Data Product Managers treat data as a product
b. Data & AI Fluency is an important core skill
2. Focus areas to be a great Data Product Manager
a. Be a great storyteller
b. Understand Data Product Lifecycle
c. Data Product Success Metrics
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Key
Takeaways

22. https://www.zillow.com/careers
/
Learn more about AI/Data
teams:
All Zillow Career openings:
https://www.zillow.com/tech
/
Zillow is Hiring!!
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23. Thank you!

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