Keynote data analyticsforsw_productinnovation_pdf
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This document summarizes a presentation on data analytics for software product innovation. It discusses how analytical open innovation can be used for innovative product development by leveraging external knowledge and applying innovative processes and technologies. Examples are provided on how different analytical techniques like cluster analysis, crowdsourcing, simulation etc. can be used to gain insights from data to support decisions around product planning, feature prioritization and release readiness optimization. Case studies are presented on how clustering of customer data and Kano modeling of requirements can help customize products to different customer groups.
![12/12/2014
25
What Counts is Insight not … Numbers
We strive to do
the best we can with
the evidence at hand,
but we accept that
evidence may be
incomplete, noisy,
and even wrong
If you have certain
patterns in mind, you
will look for supporting
evidence naturally.
So ask for
We will be able to
gain insights from
the past to
improve the
future
Data science should anti‐patterns!
be about causation, not
correlation (watch for
bias and confounding
factors!)
Data, analyses,
methods and results
have to be publicly
shared
Good data science
does not get in the
way of developing
software but supports it
(makes it more efficient)
Don't show me
what is; show
me what to do
Your project has a
history. Learn from it.
Decide from it.
Embrace it!
Underlying theory
needs to inform the
data analysis
SE data sciences
should be actionable,
reproducible.
PROFES 2014, Helsinki, Finland ©Guenther Ruhe 49
References
[1] Nayebi, M and Ruhe, G (2015), “Analytical Product Release Planning”, accepted to be
published in the book “The Art and Science of Analyzing Software Data: Analysis
Patterns”, C. Bird, T. Menzies, and T. Zimmermann (eds.), Kaufman & Morgan 2015.
[2] Nayebi, M and Ruhe, G (2015), “Analytic Open Innovation for Trade‐off Service Portfolio
Planning – A Case Study on Mining the Android App Market”. Submitted to Special Issue
on Software Business, JSS
[3] Nayebi, M. (2014), “Mining Release Cycles in the Android App Store”, The 36th CREST Open
Workshop on App Store Analysis, London, England
[4] S. Alam, S. M. Shahnewaz, D. Pfahl, and G. Ruhe, “Analysis and Improvement of Release
Readiness ‐ A Genetic Optimization Approach,” Proceedings of Product Focused Software
Development and Process Improvement (PROFES), 2014
[5] Workshop on Data Analytics, Dagstuhl 2014
[6] Chesbrough, H., “Open Innovation: The New Imperative for Creating and Profiting from
Technology”, Harvard Business Press, 2003.
[7] Ritchey, T. , "Wicked Problems‐‐Social Messes: Decision Support Modelling with Morphological
©Guenther Ruhe
Analysis.," Springer 2011.
Profes 2014, Helsinki ‐ © Guenther
Ruhe 50](https://image.slidesharecdn.com/keynotedataanalyticsforswproductinnovationpdf-141215113429-conversion-gate01/85/Keynote-data-analyticsforsw_productinnovation_pdf-25-320.jpg)
Keynote data analyticsforsw_productinnovation_pdf
- 1. 12/12/2014 1 Data Analytics for Software Product Innovation Guenther Ruhe University of Calgary ©Guenther Ruhe AGENDA How it began: The Esprit Project PROFES Product Innovation Analytical Open Innovation AOI for Innovative Products The Road Ahead PROFES 2014, Helsinki, Finland 2
- 2. 12/12/2014 2 Participating Companies PROFES 2014, Helsinki, Finland 3 ©Guenther Ruhe Project Team Markku Oivo Pasi Kuvaya Janne Jarvinenen Dietmar Pfahl Rini van Solingen Frank van Latum Guenther Ruhe PROFES 2014, Helsinki, Finland 4 ©Guenther Ruhe Andreas Birk
- 3. 12/12/2014 3 Elements of PROFES • Combining and enhancing the strengths of goal‐oriented measurement, process assessment, product and process modelling and experience factory PROFES 2014, Helsinki, Finland 5 ©Guenther Ruhe ISO 15504 GQM ISO GQM 15504 PROFES ISO9126 QIP/EF Focus on PPDs • Focus on investigating the relationship between product and process quality PRODUCT PPD PROCESS PROFES 2014, Helsinki, Finland 6 ©Guenther Ruhe
- 4. 12/12/2014 4 Different Facets of PPDs Context Characteristics PROFES 2014, Helsinki, Finland 7 ©Guenther Ruhe Technologies Used Design Inspections Product Quality Software Process Reliability Software Design Low or Average Overall Time Pressure ©Guenther Ruhe AGENDA How it began: The Esprit Project PROFES Product Innovation Analytical Open Innovation Analytics Case Studies The Road Ahead PROFES 2014, Helsinki, Finland 8
- 5. 12/12/2014 5 Innovation –What is it … after all? • Innovativeness is the measure of “newness” • New to the: ©Guenther Ruhe World Market Industry Adopting unit Consumer PROFES 2014, Helsinki, Finland 9 Crossing the Chasm PROFES 2014, Helsinki, Finland ©Guenther Ruhe 10
- 6. 12/12/2014 6 Being New … Being First PROFES 2014, Helsinki, Finland 11 ©Guenther Ruhe • New technology • New product line • New product features • New product design • New process • New service • New customers • New uses • New quality • New type of benefit A Powerful Force for Everyday Fitness PROFES 2014, Helsinki, Finland 12 ©Guenther Ruhe
- 7. 12/12/2014 7 ©Guenther Ruhe AGENDA How it began: The Esprit Project PROFES Product Innovation Analytical Open Innovation Analytics Case Studies The Road Ahead PROFES 2014, Helsinki, Finland 13 Responding to change for gaining competitive advantage in the era of smart decisions will be based not on "gut instinct," but on predictive analytics. Ginni Rometty, Chairman, President and CEO, IBM, 2013 PROFES 2014, Helsinki, Finland 14 ©Guenther Ruhe
- 8. 12/12/2014 8 Innovative product development: New ideas from leveraging external knowledge and resources, applying innovative processes and technologies PROFES 2014, Helsinki, Finland ©Guenther Ruhe 15 Open Innovation • An (open) approach for integration of internal and external ideas and paths to market that merges distributed knowledge and ideas into production processes. Chesbrough, H., “Open Innovation: The New PROFES 2014, Helsinki, Finland 16 ©Guenther Ruhe Imperative for Creating and Profiting from Technology”, Harvard Business Press, 2003.
- 9. 12/12/2014 9 Open Innovation for New Products PROFES 2014, Helsinki, Finland ©Guenther Ruhe 17 Analytic Open Innovation • Open innovation utilizing the power of analytics (processes, tools, knowledge, techniques, decisions) PROFES 2014, Helsinki, Finland 18 ©Guenther Ruhe
- 10. 12/12/2014 10 ©Guenther Ruhe AGENDA How it began: The Esprit Project PROFES Product Innovation Analytical Open Innovation Analytics Case Studies The Road Ahead PROFES 2014, Helsinki, Finland 19 New Products – Data & Information Needs Profes 2014, Helsinki ‐ © Guenther Ruhe ©Guenther Ruhe 20 Information needs Type of release planning problem Features Feature dependencies Feature value Stakeholder Stakeholder opinion and priorities Release readiness Market trends Resource consumptions and constraints What to release × × × × × × × Theme based × × × × × × × When to release × × × × × × × Consideration of quality requirements × × × × × × × Operational release planning × × × Consideration of technical debt × × × × Multiple products × × × × × × ×
- 11. 12/12/2014 11 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 21 PROFES 2014, Helsinki, Finland 22
- 12. 12/12/2014 12 Value Synergies PROFES 2014, Helsinki, Finland 23 ©Guenther Ruhe In consideration of synergies Without synergy considerations Considering constraints is causing structural differences in plans and increase value (stakeholders feature points) Time‐dependent Value Re‐planning of not implemented features before starting Q2 with updated data from different customer groups Re‐planning of not implemented features before starting Q3 with updated data from different customer groups Re‐planning of not implemented features before starting Q4 with updated data from different customer groups PROFES 2014, Helsinki, Finland ©Guenther Ruhe 24
- 13. 12/12/2014 13 PROFES 2014, Helsinki, Finland ©Guenther Ruhe 25 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Analytical Kano model Text mining Pattern recognition Morphological analysis Optimization PROFES 2014, Helsinki, Finland 26
- 14. 12/12/2014 14 1 2 Clusters Having two cluster of customers Customization towards groups of customers Having six clusters of customers PROFES 2014, Helsinki, Finland ©Guenther Ruhe 27 ©Guenther Ruhe Comparison of planning without clustering and by considering 6 clusters created from the crowd. PROFES 2014, Helsinki, Finland 28
- 15. 12/12/2014 15 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 29 PROFES 2014, Helsinki, Finland ©Guenther Ruhe 30
- 16. 12/12/2014 16 ServiceID Service S1 Live channel coverage s2 Multiscreen S3 Switch display S4 Aspect ratio change S5 EPG S6 Remote control S7 Support without touch screen S8 Video on demand S9 Youtube integration S10 Source signal selection S11 Variety of product usage model support S12 Advertisement S13 Archive S14 Search S15 Intuitive navigation S16 Detect location S17 Bookmarking S18 Categorization S19 Triple play S20 Social network accessibility S21 Playlist S22 History S23 Multicast S24 Different views supportability S25 Replay S26 Instant streaming S27 DRM S28 Memory management S29 Player integration S30 Variety of quality support S31 Parental control S32 Channel preview S33 Picture‐in‐picture S34 Peer‐to‐peer wireless screen casting support S35 Video recommendation S36 Share content PROFES 2014, Helsinki, Finland 31 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 32
- 17. 12/12/2014 17 Customer satisfied Customer dissatisfied Requirement fulfilled ©Guenther Ruhe 33 Requirement not fulfilled One‐Dimensional requirement Attractive requirements Must‐be requirements (Berger et al., 1993) Articulated specified Not expressed measurable technical Customer tailored Cause delight Implied Self‐evident Not expressed Obvious OTT Services ‐ Kano Questionnaire How would you feel if “Support of Video‐on‐Demand (VOD)” was provided with this mobile app? How would you feel if “Support of Video‐on‐ Demand (VOD)” was NOT provided with this mobile app? PROFES 2014, Helsinki, Finland 34 ©Guenther Ruhe ______ I like it that way ______ It must be that way ______ I'm indifferent ______ I can live with it that way ______ I dislike it that way ______ I like it that way ______ It must be that way ______ I'm indifferent ______ I can live with it that way ______ I dislike it that way Functional form of the question Dysfunctional form of the question https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_eeMrc9WjpFX6ZKd
- 18. 12/12/2014 18 Kano Evaluation Table PROFES 2014, Helsinki, Finland 35 ©Guenther Ruhe Customer Requirements Dysfunctional questions Like Must‐be Neutral Live with Dislike Functional questions Like Q A A A O Must‐be R I I I M Neutral R I I I M Live with R I I I M Dislike R R R R Q Must‐be (M) One‐Dimensional (O) Attractive (A) Indifferent (I) Reverse (R) Questionable (Q) Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 36
- 19. 12/12/2014 19 PROFES 2014, Helsinki, Finland ©Guenther Ruhe 37 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 38
- 20. 12/12/2014 20 New Product (Super App) Design PROFES 2014, Helsinki, Finland 39 ©Guenther Ruhe M O A R I 2 2 1 2 0 {S4,S10,S11,S14,S20,S26,S27} Value Effort 1570 261 M O A R I 1 1 3 1 3 {S1,S2,S3,S4,S5,S6,S7,S14,S19,S21, S22,S23,S25,S28,S32} Value Effort 4506 261 Release Readiness Optimization 0.8 0.75 0.7 0.65 0.6 Percentage of issues fixed () Average method complexity Percentage of duplicated code Number of code smells per class Test coverage: Covered LOC/ LOC Defects/KLOC Percentage of defect fixed Defect find rate for last two weeks Code Churn per contributor per day Percentage of successful builds/integration 12/12/2014 40 ©Guenther Ruhe 0.55 142 149 156 163 170 Calculated readiness Projected readiness on release date Readiness Development time (days) 0.00 0.20 0.40 0.60 0.80 1.00 Number of feature implemented Level of attribute satisfaction
- 21. 12/12/2014 21 Release Readiness Optimization (2/2) 12/12/2014 41 ©Guenther Ruhe Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 42
- 22. 12/12/2014 22 2779 (X*Y)*X* 358 379 (YXm)n 1809 738 XmYnXl 21 Ym(YX)nXl XnYm 77 154 (XYm)n 154 (XmY)n 38 21 56 (YX)n X(XY)n (XYX)n 41 59 XYnXm YnXm 132 814 971 839 YXn YnX (YX)nXm 49 (XXY)nXm (XY)nXm 4 14 25 284 3 15 (YnX)m 9 76 PROFES 2014, Helsinki, Finland ©Guenther Ruhe 43 ©Guenther Ruhe AGENDA How it began: The Esprit Project PROFES Product Innovation Analytical Open Innovation Analytics Case Studies The Road Ahead PROFES 2014, Helsinki, Finland 44
- 23. 12/12/2014 23 Cluster analysis Crowdsouring Rough set analysis ©Guenther Ruhe Simulation Text mining Pattern recognition Morphological analysis Optimization Analytical Kano model PROFES 2014, Helsinki, Finland 45 PROFES 2014, Helsinki, Finland ©Guenther Ruhe 46
- 24. 12/12/2014 24 Innovative products through innovative processes INNOVATIVE PPD PRODUCTS PROCESSES PROFES 2014, Helsinki, Finland ©Guenther Ruhe 47 Innovative Products through AOI PROFES 2014, Helsinki, Finland 48 INNOVATIVE PRODUCTS PROCESSES • Acquiring innovation from external sources • Analyzing data • Integrate innovation • Commercializing innovations
- 25. 12/12/2014 25 What Counts is Insight not … Numbers We strive to do the best we can with the evidence at hand, but we accept that evidence may be incomplete, noisy, and even wrong If you have certain patterns in mind, you will look for supporting evidence naturally. So ask for We will be able to gain insights from the past to improve the future Data science should anti‐patterns! be about causation, not correlation (watch for bias and confounding factors!) Data, analyses, methods and results have to be publicly shared Good data science does not get in the way of developing software but supports it (makes it more efficient) Don't show me what is; show me what to do Your project has a history. Learn from it. Decide from it. Embrace it! Underlying theory needs to inform the data analysis SE data sciences should be actionable, reproducible. PROFES 2014, Helsinki, Finland ©Guenther Ruhe 49 References [1] Nayebi, M and Ruhe, G (2015), “Analytical Product Release Planning”, accepted to be published in the book “The Art and Science of Analyzing Software Data: Analysis Patterns”, C. Bird, T. Menzies, and T. Zimmermann (eds.), Kaufman & Morgan 2015. [2] Nayebi, M and Ruhe, G (2015), “Analytic Open Innovation for Trade‐off Service Portfolio Planning – A Case Study on Mining the Android App Market”. Submitted to Special Issue on Software Business, JSS [3] Nayebi, M. (2014), “Mining Release Cycles in the Android App Store”, The 36th CREST Open Workshop on App Store Analysis, London, England [4] S. Alam, S. M. Shahnewaz, D. Pfahl, and G. Ruhe, “Analysis and Improvement of Release Readiness ‐ A Genetic Optimization Approach,” Proceedings of Product Focused Software Development and Process Improvement (PROFES), 2014 [5] Workshop on Data Analytics, Dagstuhl 2014 [6] Chesbrough, H., “Open Innovation: The New Imperative for Creating and Profiting from Technology”, Harvard Business Press, 2003. [7] Ritchey, T. , "Wicked Problems‐‐Social Messes: Decision Support Modelling with Morphological ©Guenther Ruhe Analysis.," Springer 2011. Profes 2014, Helsinki ‐ © Guenther Ruhe 50