Why Big and Small Data Is Important by Google's Product Manager
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In this talk, Dan McClary, a Product Manager at Google, walked through the importance of using data to drive product decisions, as well as how to quickly pull together an architecture using free tools to help grow a product effort from market analysis to live data capture and data-driven product decisions. We also played a rousing game of Breakout.
![Adding app-logging
direct to BigQuery
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var rows = [req.body];
console.log(rows);
bigquery
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.insert(rows)
.then((insertErrors) => {
console.log('Inserted:');
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insertErrors.forEach((err) =>
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Why Big and Small Data Is Important by Google's Product Manager
- 1. Why Big and Small Data Is Important by Google Product Manager www.productschool.com
- 2. FREE INVITE Join 12,000+ Product Managers on productschool.com/slack-community
- 4. Coding for Managers 2-month part-time Courses
- 5. Data Analytics for Managers 2-month part-time Courses
- 6. Include @productschool and #prodmgmt at the end of your tweet Tweet to get a free ticket for our next Event!
- 8. Show Your Work Why data is important to Product Management Dan McClary
- 9. Rough Agenda Show Your Work: Why PM requires collecting and analyzing data Case example: planning a product Case example: instrumenting a product Case example: analyzing the product and users
- 10. Who is this person, anyway? Product Manager @ Google I work on Google BigQuery and Dremel Mostly, I’m going to use BigQuery as a tool to explain a broader concept Before that PM at Oracle, Startup Eng Manager, Researcher, etc. Ph.D. in CS, Postdoc in “complex systems”
- 11. What is this talk about? Playing Breakout! http://bigphaser-demo.appspot.com
- 12. What is this talk really about? When I’m asked about “what I do as a PM,” it’s really hard to come up with one answer Technical design, marketing, strategy, accounting, pitching, etc. There’s only one constant in my job I analyze data, every day. So should you. We’ll do some demos, and talk about concepts. If you get bored, play Breakout!
- 13. So this isn’t a talk about Big Data? It can be, if you have that much data Remember Before you have Big Data, you just have data After you have Big Data, you will still just have data Sometimes joining two spreadsheets is more important than a PB of logs
- 14. Why does a PM need to analyze data? Credibility Never assume that as a PM you have the authority to order things (you probably don’t) Most of our work comes in convincing groups to do the right thing Engineering build the right feature Marketing craft the right message Sales execute the right play Users choose the best product
- 15. How to convince most of the people, most of the time Engineering build the right feature Marketing craft the right message Sales execute the right play Users choose the best product Your background might give you inherent credibility with 1 or 2 of these groups There’s only one thing that’s credible to all of them: facts
- 16. Where to get facts 01 Get data (hopefully good data) 02 Analyze it 03 Share the results of the analysis and show your work
- 17. Why show your work? Show your work because: This forms a basis for consensus (or disagreement) Argue about actuals, instead of abstracts You will always have to argue about some abstracts, but you can make them less abstract using the preceding recipe.
- 18. Scenario: Let’s make a video game! We could just start making one… But we probably should think about the problem space. What will our stakeholders want to know before we start? How much money could we make? What kind of game is the right kind to make? How will we position it?
- 19. So I went and found some data A CSV of video game sales data A web-scrape of all the video game ratings on IGN.com Great -- we should probably analyze these. I’ll just open each of them in Excel/Google Sheets and… STOP! How is that going to help you show your work?
- 20. “Showing your work” → Organizing for reusability Sharing our results and analysis credibly Organizing data so that it can be found (and analyzed) again Organizing data so that it can be analyzed by different tools For example, this is part of what Google BigQuery is designed for.
- 21. What is Google BigQuery? Industry-Standard SQL Encrypted, Durable and Highly Available Petabyte-Scale Fully Managed, No-Ops Enterprise Data Warehouse Virtually unlimited resources
- 22. Pricing for any scale product effort Feature Price Storage $0.02 per GB, per month $0.01 per GB, per month for long term storage First 10 GB is free Streaming Insert $0.01 per 200 MB Load, Copy, and Export Free Pay-as-you-go Queries $5 per TB First 1TB per month is free
- 23. Let’s analyze data: sizing an opportunity Who’s the biggest publisher in the industry (by revenue)? How many companies make more than $1M? Less than $1M? How much could we make in a year, per region? Let’s do it in SQL!
- 24. Who’s the biggest publisher in the industry (by revenue)? SELECT Publisher, SUM(Global_Sales) as overall_revenue FROM `bigphaser- demo.market_data.game_sales` GROUP BY 1 ORDER BY overall_revenue desc
- 25. What are the top-selling, top-rated games? SELECT AVG(score) as avg_score, SUM(Global_Sales) as sum_sales, s.Name, r.genre FROM `market_data.game_ratings` r, `market_data.game_sales` s WHERE title = s.Name GROUP BY 3, 4 ORDER BY sum_sales desc, avg_score desc
- 26. But I don’t know SQL! There are other free tools we can tap into to help us analyze this data…. While maintaining a consistent repository of data. Example: Google Data Studio
- 27. Scenario: We made a game, but what are people doing? Once we’ve got an MVP, understanding user adoption and behavior can be done many ways. We could use surveys to understand user happiness. We could collect data from our application to understand what users are doing.
- 28. Adding app-logging direct to BigQuery app.post('/data', (req, res) => { var table = dataset.table(tableId); console.log(req.body); var rows = [req.body]; console.log(rows); bigquery .dataset(datasetId) .table(tableId) .insert(rows) .then((insertErrors) => { console.log('Inserted:'); rows.forEach((row) => console.log(row)); if (insertErrors && insertErrors.length > 0) { insertErrors.forEach((err) => ...
- 29. “Extend your work” → Continue to build your data foundation Adding additional data to your analytical footprint can help you add exponential value. Let’s take a look at some of the things that players are doing… How could we understand user happiness and still analyze it in the context of the data we already have?
- 30. Surveying and Analyzing User Happiness Here’s a quick survey for our users. Take it now. https://goo.gl/forms/ jWiGDM0alSIn8Zf83
- 31. Leverage your foundation broader sets of stakeholders You can leverage your foundation to draw additional insight out of new pieces of data. For example, we can examine our survey responses in isolation…. Or we could make them a table in our data warehouse so that we can combine it with data we already have. Let’s try it!
- 32. Scenario: If we make a change to the game, how can we collect that data? Based on what we know, let’s make a product decision: How many lives should a user have in the game? If we make that change, it would be good to have information about the application change.
- 33. Building beyond the foundation Once you’ve established an analyzable repository for data, you’ll inherently want more data to leverage. Most of these can be added easily to BigQuery Application and service logs Billing and cost data Marketing data SaaS and Social data
- 34. Wrapping Up: Why Show Your Work Your background, charisma, and “product feel” will never be enough to influence all your stakeholders all the time. Building consensus requires facts Facts means Data Analysis with work shown It’s not enough to say “I analyzed it.” Show people and you’ll build both credibility and consensus.
- 35. Wrapping Up: How to show your work Find a place, tool, or set of tools for your data that let’s you Analyze Join Share Grow your data as if it were capital, and you’ll find it pays dividends for you, your team, and your product
- 36. Q&A
- 37. Part-time Product Management Courses in San Francisco, Silicon Valley, Los Angeles and New York www.productschool.com
Editor's Notes
- #3: When you checked in tonight, you got an email inviting you to join our slack community In that community, we have 12k product people who have come through different companies like google, facebook, uber Sharing information about events, job offers from our partner companies, and valuable online content Please check your email and join - it’s free
- #4: In our PM Course, we teach how to build products and how to get a job as a software product manager All our classes are 2 months, part time, and compatible with full time jobs. We have two options, Tues/Thurs in the evening and Saturdays in the morning Instructors- are senior level product managers from companies like Google, FB, Uber, etc
- #5: In addition to our PM class, we offer our Coding for Managers class Also two months and part time tailored for professionals who don’t come from a traditional engineering background The goal of this course is not to make you a software engineer, but to give you enough technical background to build a fully functional website and pass the technical interview
- #6: Similar to our coding course, we also offer our Data Analytics for Managers Tailored for people who don’t have a technical background but to give them enough knowledge of analytics to become product managers Also two months, compatible with full time jobs The goal of the course is not to make you a data scientist, but to make you technical enough to understand web analytics, learn SQL, and machine learning concepts
- #7: We are also live streaming our event to our online audience If you want to share, please tweet @productschool and #prodmgmt for a free ticket to our next event