Software is Eating the World, And You're For Lunch"
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Something happened in Silicon Valley. Tech firms stopped just making software for industry to buy, and decided instead to use software to enter new markets. Companies such as Amazon, Uber, and Netflix have marched into mainstream industries, backed by data-driven learning machines. As Marc Andreessen famously said, software is eating the world. How do you keep on the winning side of this change? The secret is in how you view and exploit data.
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Software is Eating the World, And You're For Lunch"
- 1. SOFTWARE IS EATING THE WORLD, AND YOU’RE FOR LUNCH! Edd Dumbill @edd • edd@svds.com
- 2. 2 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED.
- 3. 3 “… let's seek to understand how the new generation of technology companies are doing what they do, what the broader consequences are for businesses and the economy and what we can collectively do to expand the number of innovative new software companies created in the U.S. and around the world.” – Marc Andreesen
- 4. 4 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. DIGITAL NERVOUS SYSTEM
- 5. 5 Data is your business
- 6. 6 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED.
- 7. 7 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. SILICON VALLEY’S DATA MACHINE
- 8. 8 Where does it go wrong?
- 9. 9 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. CLEAN VALIDATE CONTROL PROTECT CONVENTIONAL DATA STRATEGY “WHAT YOU DO TO DATA”
- 10. 10 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. MODERN DATA STRATEGY “WHAT YOU DO WITH DATA” TARGET VIP CUSTOMERSATTRACT NEW CUSTOMERS AUTOMATE
- 11. 11 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. UP vs. OUT in the Enterprise Different use cases put different demands on the data infrastructure. Increasing cost per unit of capability from scale- up architectures causes rationing of resources. Only the most valuable use cases are pursued. USDollars Data Resource Usage Scale-up cost Scale-out cost UC1 UC2 UC3 UC4 UC5
- 12. 12 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. USE CASE 2 NEW TOOLS MAKE IT POSSIBLE USE CASE 1 WORKLOA D A WORKLOA D B WORKLOA D C WORKLOA D B WORKLOA D C USE CASE 3WORKLOA D B WORKLOA D C WORKLOA D D
- 13. 13 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. THE DATA VALUE CHAIN How data can be understood strategically Discover Ingest Process Persist Integrate Analyze Expose
- 14. 1414 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. • Make it cheap • Failure as a feature • Ask good questions • Make it quick • Both learning and adaptation • Enable the feedback loop • Don’t break things • Make operations a platform for innovation • APIs, platforms, simulation BUILD FOR EXPERIMENTS
- 15. 15 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. THE EXPERIMENTAL ENTERPRISE We need to both support investigative work and build a solid layer for production. Data science allows us to observe our experiments and respond to the changing environment. The foundation of the experimental enterprise focuses on making infrastructure readily accessible.
- 16. 16 © 2014 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. BECOME DATA NATIVE • Can only win with situational awareness • New architectures offer new opportunities • Creation of data-driven value requires new approach • Business must lead, and understand the potential of the technology
- 17. 17 Edd Dumbill edd@svds.com @edd Yes, we’re hiring! info@svds.com
Editor's Notes
- Next we have to consider: How will technology make it possible? This is where technologists need to take the reins Requires data and analytics expertise – exposure to and understanding of a rapidly changing landscape Describe the use cases – for each objective (the tactics from before) (from why to what to how) What will you actually build? (e.g., segment customers to discover VIPs, build predictive demand models, build user search capability) Exploit patterns and reuse - platforms, services, workloads Look across *all* the use cases What are the common workloads you can make use of? (e.g., social media sentiment analysis, customer product intelligence, and customer service call transcript analysis all share a common need for some form of text analytics)
- Focus on the value You’ve identified what you could build and even how you might build it efficiently Lots of things you *can* do, but what should you do *first* Prioritize your efforts - Not all projects are created equal What matters most to the business? (hint: prioritize the business’s goals first, *not* projects or use cases) Considers many different dimensions of priority (e.g. skills, difficulty, deployment) This helps you identify where you might find quick wins or black holes Overcome your assumptions and preconceptions (e.g., root out pet projects with real data) Because you did this together, you’ve got a shared sense of priority and a reason you’re headed down this path If someone is convinced that image processing is the most important technology project but that doesn’t line up with the business priorities, that will come out
- Build a roadmap to give you direction Using that combined notion of priorities Fuel agile development with an end-point in mind Use the strategy to identify a pipeline of projects that take you toward your goal Even though we’re talking about agile, the Goal of the roadmap is still to produce a sustainable architecture based on a platform
- Analysis is at a point in time Meant to be a repeatable process Re-assess priorities, difficulties, plans often Respond to changes in priorities, changes in the market, or new technology possibilities Your strategy should be a living document that guides you