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Design vs. Data: Enemies or Friends?

Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1FbZwcD.

Big Design Upfront was considered so evil in the early days of Agile that it acquired its own acronym. But absent the careful thinking of good designers, our systems turn out to be mediocre collections of stuff that customers asked for or the data showed was desirable. It’s time we relearned that great products start with asking the right questions. Filmed at qconnewyork.com.

Mary Poppendieck has been in the Information Technology industry for over thirty years. She has managed software development, supply chain management, manufacturing operations, and new product development. Mary is a popular writer and speaker, and coauthor of the book Lean Software Development, which was awarded the Software Development Productivity Award in 2004.

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Design vs. Data: Enemies or Friends?

  1. 1. l e a nsoftware development www.poppendieck.comMary Poppendieckmary@poppendieck.commary@poppendieck.com Design vs. Data Enemies or Friends?
  2. 2. InfoQ.com: News & Community Site • 750,000 unique visitors/month • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • News 15-20 / week • Articles 3-4 / week • Presentations (videos) 12-15 / week • Interviews 2-3 / week • Books 1 / month Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations /design-data
  3. 3. Presented at QCon New York www.qconnewyork.com Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide
  4. 4. August 152 Copyright©2015 Poppendieck.LLC How do we avoid this?
  5. 5. August 153 Copyright©2015 Poppendieck.LLC Notre Dame de Paris 1163-1345 1850
  6. 6. August 154 Copyright©2015 Poppendieck.LLC Cologne Cathedral 1248-1473 & 1842-1880 1824
  7. 7. August 155 Copyright©2015 Poppendieck.LLC Poitiers Cathedral (~1160 – 1290)Chartres Cathedral (~1134-1220) 1160 1510
  8. 8. August 15 Copyright©2015 Poppendieck.LLC Strong Centers The VoidContrast Local Symmetries Echoes (Patterns) Good Shape Simplicity Boundaries Positive Space Alternating Repetition (Recursion) Not-Separateness (Connectedness) Levels of Scale Theory of Centers* – Fifteen Properties of “Wholeness” Roughness *Christopher Alexander Deep Interlock (Ambiguity) Gradients
  9. 9. l e a n Properties of Wholeness for Software* Levels of Scale Strong Centers Boundaries Symmetry Recursion Patterns Space Shape Simplicity Connectedness August 15 Copyright©2015 Poppendieck.LLC7 (Left Out):  Ambiguity  Roughness  Contrast  Gradient  Void Hypothesis: Learning through ongoing experimentation is not an excuse for sloppy system design. On the contrary: Strong systems grow from a design vision that helps maintain “Properties of Wholeness” while learning through careful analysis and rigorous experiments. *With credit to Christopher Alexander
  10. 10. l e a n A Design Vision to Make Data a Strong Center Understand Data and how to use it.  Data is central  A picture is worth a thousand data points  Appreciate statistics  Analyze / Experiment – know the difference  Everyone is on the same team Simplify the Job of Data Scientists.  Data pipelines must be wide and fast  Develop API’s for learning and control  Experiments need design and structure  Use an architecture that supports learning August 158 Copyright©2015 Poppendieck.LLC See, Think, Gain Amazing Insights  Know how to use the best tools and models  Be explicit about assumptions  Share the search for patterns and outliers  Get inside the data  Test insights rigorously Space Simplicity Levels of Scale Shape Boundaries Connectedness Patterns Symmetry Recursion
  11. 11. l e a n How we Use Data Monitor Control Simulate Predict August 159 Copyright©2015 Poppendieck.LLC
  12. 12. l e a n Monitor August 15 Copyright©2015 Poppendieck.LLC10 Something went wrong and the system is down. Lots of people are frantically looking under every rock, and finding dozens of things that are not right. Pinpoint the moment in time when everything went wrong. What went wrong in 1984? Mickey Dickerson
  13. 13. l e a n Control August 15 Copyright©2015 Poppendieck.LLC11 Controllers regulate temperature, position, speed, pressure, flow, weight, force, thickness, chemical composition, and practically every other variable that can be measured. P = Size of the error D = Rate at which error is changing I = Residual error that accumulates over time
  14. 14. l e a n Simulate For many embedded systems lack of hardware, complexity of hardware, or inconvenience of setup limits the ability to use Continuous Integration (CI). Simulation makes it possible to use standard PCs and servers to run code destined for even deeply embedded target systems, making an effective CI possible that is more flexible and cheaper than relying on hardware alone. August 15 Copyright©2015 Poppendieck.LLC12 Credit for this page: Jakob Engblom, Wind River, Kista, Sweden
  15. 15. l e a n Predict August 15 Copyright©2015 Poppendieck.LLC13 1. Insights: Extracted from data. 2. Predictions: What will happen, when, what will be affected, and what factors influences it. 3. Optimizations: The most valuable use of computational analytics is knowing “what to do next”. The new arrival – the Data Scientist
  16. 16. l e a n Exploratory Data Analysis (EDA) An approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to maximize insight into a data set 1. Uncover underlying structure 2. Extract important variables 3. Detect outliers and anomalies 4. Test underlying assumptions 5. Develop frugal models 6. Determine optimal factor settings August 15 Copyright©2015 Poppendieck.LLC14 All the Data Sample Try fitting various curves Variables that help create models that fit sample data Test promising variables on all data Variables that work on all data Potential Models
  17. 17. l e a n A systematic approach to problem-solving that applies principles and techniques at the data collection stage that ensure the generation of valid, defensible, and supportable conclusions. 1. Comparative Has a change in a single factor changed the whole process? 2. Screening/Characterizing Form a ranked list of factors that affect the process. 3. Modeling Create a mathematical model of the process. 4. Optimizing Determine the optimal settings for the process factors. Design of Experiments (DOE) August 15 Copyright©2015 Poppendieck.LLC15 Screening Experiments Mixture Experiments Response Surface Analysis Fractional Factorial Experiments Full Factorial Experiments Evolutionary Operations (EVOP)
  18. 18. l e a n Design for Data Basic Elements: Fast Pipelines Data Wrangling Data Analytics Data Visualization Designed Experiments Machine Learning Adaptable Business Systems and Processes (ready and able to use insights and services) August 15 Copyright©2015 Poppendieck.LLC16 Credit: Ravi Kalakota, Liquid Analytics
  19. 19. l e a n Design the System (not just the code) August 1517 Copyright©2015 Poppendieck.LLC Design like a Master Mason Rather than a Stonecutter
  20. 20. l e a nsoftware development www.poppendieck.comMary Poppendieckmary@poppendieck.commary@poppendieck.com Thank You! More Information: www.poppendieck.com
  21. 21. Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations/design- data

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