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SSE 2017 10-09
- 1. Data, AI, Innovation, and the Future of Jobs Galina Esther Shubina November 2017 Data Business Product Technology
- 2. Where am I from? Moscow: 15 years US East Coast: 12 years Silicon Valley: 4 years Sweden: 10 years
- 3. Big Data? AI? Product Strategy In Our Technological World
- 4. Overview ● Data-driven & Data-informed ● Artificial Intelligence & Business ● Future of Jobs
- 7. To Amazing
- 8. “Every day, we create 2.5 quintillion bytes of data - so much that 90% of data in the world today has been created in the last two years alone.” - IBM
- 9. “The number of transistors in a dense integrated circuit doubles approximately every two years.” - Moore’s Law
- 11. “Productivity growth is a key economic indicator of innovation.” - Jorgenson, Ho, Samuels
- 12. Why now?
- 13. Why Now? Cheap storage and computing Algorithms for big data - big data for algorithms Progress in machine learning (AI! Deep learning!) Evolving consumer behaviour and user expectations Excellent mature tools for data manipulation and analysis Increasing awareness of the power of data
- 14. WhatisBigData?
- 15. Big data & Machine Learning are just technology thingamabobs
- 16. The important things are the stories we tell with our data and tools
- 17. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data Macro Futures
- 18. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data Macro Futures
- 19. Data-Driven Products Products using data and algorithms to solve problems and create value Data plus machine learning algorithms NOT about traditional databases or units of data Digital natives → Data natives
- 22. Product management = crafting the right product with UX + Data + Machine Learning + Product & Business Strategy
- 23. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data Macro Futures
- 24. Data-Informed Product Development Product Strategy Ideation Prototyping Evaluation Macro Insights Development Testing Business Analytics Product Analytics A/B Testing
- 25. I feel the need - the need for speed!
- 26. Agile
- 27. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data And Macro
- 29. Data-Informed Business Daily (hourly?) health of your business on dashboards … democratically available … with ability to quickly get meaningful and actionable Insights … with ability to explore … 80% of questions answered without analyst involvement
- 30. Data-Informed Business Product ManagementCEO Board Management Business & Product Analysts Data Scientists Data Engineers Dashboard Drill-downs Databases Data Lakes Raw Systems High-Level Dashboard
- 32. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data Macro Futures
- 33. Data & Macro Your Company or Product
- 34. … in the greater universe Your Company or Product social economic political environmental
- 36. Data-Driven Business is Eating the World Data-driven products and services Data-informed product development Data-informed business Data Macro Futures
- 37. Data ≠ Results The Year is 2017 and search still sucks on most websites. Forbes: “Why Investments In Big Data And Analytics Are Not Yet Paying Off” Getting results from data requires ● New roles ● New collaborative mindsets ● New organizational structures ● Not getting stuck in one of the four boxes
- 38. OK, what about that AI thing
- 40. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 41. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. Artificial cognition G. All of the above Terminator
- 43. Matrix
- 44. OK, try #3
- 45. Blade Runner
- 47. Ex Machina
- 49. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 50. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 51. Machine learning - algorithms that allow computers to learn without being explicitly programmed.
- 52. Training Data + Labels Model Training Coefficients! New Data Model Training Coefficients! Predicted Labels
- 53. Sample Classes of ML algorithms ● Classification ● Clustering ● Predictive algorithms (i.e. regression) ● Neural networks ● Supervised algorithms ● Unsupervised algorithms ● Reinforcement learning
- 54. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 56. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 57. From computer vision to fully autonomous systems
- 58. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 59. Google going AI-First Information you need, when you need it
- 60. What are the implications of this product/user shift? ● See above ● Product strategy! ● Requires higher precision/lower errors ● Closer integration of all data functions ● Great data first ● Not your old-style IT department ● Not optional (AI startups?)
- 61. Risks
- 62. Risks Data security Privacy issues, tech surveillance, social media monitoring Ethical implications of complex machine learning models Complexity of software systems and interactions (new this year) Over-focus on metrics leading to a local minimum State and big company players
- 63. Is data your moat? Commoditization of big data and machine learning technology
- 66. In modern companies, we’re looking to solve relatively open ended problems in innovative ways with the newest technology often by producing fairly complex systems, whose shape is unknown at the start. Producing unknown complex outputs requires complex inputs and complex processing systems.
- 67. Is social capital your moat?
- 68. A few notes on modern organizations ● Engineering departments are like diabetes... ● From hierarchies to networks ● Companies as knowledge propagation mechanisms ● Multi-disciplinary approach is often a must ● Diversity on multiple dimensions ● Continuous deployment for systems - continuous development for people ● Executives and board who have walked the walk earlier
- 69. Jobs of the Future Future of Jobs
- 71. Yes, do study some programming (but know that good software engineering is art on top of science)
- 72. Data: Everyone needs it! “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. “ Hal Varian, Google’s Chief Economist January 2009
- 73. Jobs of the Future Backend Engineer Statistician Data Engineer Product Manager Product Owner (Tech) Project Manager Frontend Engineer Mobile Engineer Data Scientist Machine Learning Engineer Product Analyst UX Researcher Business Analyst Embedded Engineer
- 74. Jobs of the Future + Domain Knowledge Backend Engineer Statistician Data Engineer Product Manager Product Owner (Tech) Project Manager Frontend Engineer Mobile Engineer Data Scientist Machine Learning Engineer Product Analyst UX Researcher Business Analyst Embedded Engineer
- 75. Design, implement, maintain data platform Ask questions Extract data Clean and join big data Analyse, and visualize data Build data-based feature prototypes Statistics, ML, AI Communicate results Ask questions Extract data Analyse and visualize data Communicate results Data Engineer Data Infrastructure Data Scientist Product Manager Business Analyst Machine Learning Engineer Build Skills - Not Silos
- 76. Best data people I hire these days come from domain fields (aeronautics, physics, biology, pure math, archeology, etc)
- 77. People are not resources Hire for: ● Common sense ● Curiosity and interest ● Communication skills ● Ethics ● Basic technical skills Develop: ● Technical skills ● Business skills ● Presentation and story-telling skills ● Management skills
- 78. Future of Jobs
- 79. … and how AI/technology will affect it
- 81. Social wealth accumulated in cities and countries.
- 82. 1. High productivity and wages 2.Resistance to automation 3.The potential to employ massive amounts of labour 2 out of 3
- 83. Open Closed
- 84. Thank You!
- 85. Q & A & D[iscussion]
Editor's Notes
- Modern life, there’s an app for that. The leading character in my story is technology and data. But equally important are people.
- How can we move from creating just operational and reporting and into creating new products and business models.
- Amount of data (the quote), cheap storage and computing (cloud, powerful machines in DCs), infrastructure and tools, algorithms. Also mention increasing awareness of the power of data and companies using data successfully. Evolving consumer behavior and expectations on products to be customized : users are not only mere consumers of the data that companies produce any more, they are the producers and influencers through their posts, likes, shares, recommendations, reviews, ratings etc Personalization/customization - it is also the effect of the amount of data being produced, can’t consume all or search through everything, we need to customize based on user preference (preset/selected) and/or usage data. Big Data, what exactly is it?
- Bigdata is like a healthy diet, everyone talks about it, nobody really knows how to do it, everybody thinks everyone else is doing it, so everyone claims they are doing it. (Rephrasing a quote from Dan Ariely)
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- Confusingly enough all of this is called Ai-first
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- Innovation is a process
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- Data-informated vs data-driven Train your intuition Take the guesswork out of it. Collect data and talk to users. Why do both? Because if you’re living big, you won’t be able to interview everyone; because users do one thing and say another. Still talk to users because your data may not surface all the problems, because your metrics may not be capturing general discontent. Bring it back to collaboration and contributions from a variety of specializations
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- There are four primary aspects of how data-driven businesses are working to creating the effortless experiences and tools we use daily. The four pillars of these are: One. Data-driven products and services: Next generation of products and services: by looking closely and collection data about key content, users, we can create better products and services our users actually want and need. Every part of our product can be more efficient: for ex, personalised search suggestions are no longer a novelty - they are now expected of all applications. We expect suggested completion on everything (think of all the search field in all your apps), but this completion is based on behaviour of all users before and also this particular users. For purchases made online, we can have sophisticated follow up scenarios. We can write text automatically knowing other texts, machine translation is in some ways a solved problem. Reputations can be managed when we buy things online (Fraud). User generated content is a key driver of the content economy too. Data-driven product development: How do we decide which features to implement or not? How should we prioritize our work? How can we enable the fast-iteration product development cycle that Jacqueline was talking about in the first section? We need to create key metrics for our business - and then some - and then use them consistently and automatically to inform ourselves about where we want to go. [New, faster ways of developing products: as discussed before, how do we figure out what our products should be like? We talk to our users, but we may also have so many, we do fast iterations and experiment. In order to do this, we need to be able to create experiments with a subset of our users, and track to see how happy they are. We create success metrics (proxy for how happy users are), and then we have automated ways of figuring out how our experiments compare to our controls. For example, a new recommendation algorithms - we want to see whether people look at more articles or objects, or are more likely to buy something on a site. ] Data-Driven Business: Business and technical efficiency: Quarterly reports, monthly or even weekly cycles of data collection for key business metrics are becoming a key of the past. In order to optimize business, we want key metrics (usually around users, their happyness, and revenue) be available live or near-live. Being able to do simply sophisticated analyses, allows us to detect vulnerabilities and issues - as well as opportunities. It can also dramatically improve decision making in an organization.We can predict which customers you might lose _now_ and offer them something to prevent it. Technical efficiency: health, resource utilization, optimization of services (say, based on day-night patterns). Customer loyalty, churn, profitability, user segmentation Data vs Competition: Competitive advantage: most companies view data as their competitive advantage. User-generated content. Analyzing their users like, building better personalized products, thus getting more users and better data can create a kind of a moat, a competitive advantage for your business. It is not enough to create beautiful products, creating effortless products requires optimizing user processes so they seem effortless - to do so we need to both use fine-tuned data analysis of user behaviour as well as that of our content.
- The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so the result would not depend on the machine's ability to render words as speech.[2] If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give.
- Commoditization of everything else. Not only.
- Commoditization of everything else. Not only.
- Machine learning engineer - that’s where true innovation. Compress collaboration
- Image from xkcd via Slate Thought -
- Image from xkcd via Slate Thought -