Reducing Technology Risks Through Prototyping

How to deliver a successful product when technology landscape is new and rapidly changing? How to identify technology limitations before moving to production? What if there are no technology experts to answer your questions?

Strategic prototyping can help development teams respond to these issues instead of blindly building full-scale products. I will not be offering silver bullets of simple recipes for success. Instead, you will learn about the practical guidelines for prototyping, combining architecture analysis and a variety of prototyping techniques. With some Big Data systems development flavor on top of it.

Transcript

1. Reducing Technology Risks Through Prototyping Valdas Maksimavičius
2. Production Prototyping Continuous Integration Test Driven Development Automated Tests
3. Production Prototyping Continuous Integration Test Driven Development Automated Tests ?
4. Recommendation systems
5. Big Data Landscape 2017
6. Separate Hype From Reality
10. The 8 Lessons I Learned While Prototyping
11. 1. Agree on naming
12. Proof of Concept (PoC)
13. Prototype
14. Minimal Viable Product (MVP)
16. 2. Determine the proper questions to ask
18. A Checklist for Big Data Prototyping • Integration • Data quality and governance • Security • Visualization and analytics
19. A Checklist for Big Data Prototyping • Integration • Will you be ingesting real-time streaming data? • How much data is needed for your use case? • What type of data are you ingesting (relational, machine data, social, JSON)? • Which source systems are you ingesting from? • Outline the process for accessing data from those systems (owners, frequency) • ...
20. 3. Define the hierarchy of needs
22. Example #1
23. Example #2
24. 4. Identify your knowledge gaps
28. 5. Review the architecture
30. • Don’t forget about non-functional requirements • Avoid ivory tower architectures • Be aware of cultural differences
31. “Your early decisions make the biggest impact on the eventual shape of your system [...]. It’s a terrible irony that these very early decisions are also the least informed“.
32. 6. Track your efforts
33. A spike is a task aimed at answering a question or gathering information, rather than at producing a shippable product.
34. Technical Spikes • Determine a build-versus-buy decision. • Evaluate the potential performance. • Evaluate specific implementation technologies. • Develop confidence about a desired approach.
35. Functional Spikes • Use whenever there is significant uncertainty about how a user might interact with the system. • Use UI mock-ups, hardware prototypes, wire frames, page flows, or other techniques. • Collect feedback from the customer or stakeholders.
36. Example As a consumer, I want to see my daily energy use in a histogram so that I can quickly understand my past, current, and projected energy consumption.
37. 7. Document
40. 8. Stop starting, start finishing
41. Solution Idea
43. Solution Idea
44. Solution Idea Install IDE
45. Solution Idea Install IDE Install plugins
46. Solution Idea Install IDE Install plugins Learn shortcuts
47. Solution Idea Install IDE Install plugins Learn shortcuts
49. Minimal Viable Product Harness the power of CSV
50. Questions? valdas.maksimavicius@gmail.com blog.thevaldas.com
51. Summary 1. Agree on naming 2. Determine the proper questions to ask 3. Define the hierarchy of needs 4. Identify your knowledge gaps 5. Review the architecture 6. Track your efforts 7. Document 8. Stop starting, start finishing