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REASONING WITH PROBABILISTIC
GRAPHICAL MODELS IN A PROJECT
BASED BUSINESS
BY OLGA TATARYNTSEVA, DATA SCIENTIST
• Neural networks
• Deep learning
• Natural language processing
• Image processing
• Big data
• Bioinformatics
Today we ar...
• Neural networks
• Deep learning
• Natural language processing
• Image processing
• Big data
• Bioinformatics
• Project b...
Chief Technical officer
... Delivery Director
... PM
... Architects
Business
Analysts
Developers QA
Data
scientists
...
.....
Chief Technical officer
... Delivery Director
... PM
... Architects
Business
Analysts
Developers QA
Data
scientists
...
.....
Scope
Resources
Quality
Time
Project management triangle
ProjectTeam
Direct contacts with the
customer
Requirements
management
Solution design
Product development
Project budget p...
Chief Technical officer
Delivery director
PM PM …
Delivery Director
PM PM …
...
PM ...
1000+ employees
Simplified structur...
Don’t panic
We have PGM
CTO at ELEKS has a BI Dashboard
• Declarative representation of our understanding of the world
• Identifies the variables and their interaction with each ...
«As far as the laws of mathematics refer to reality, they are not certain,
as far as they are certain, they do not refer t...
• Causal inference
• Information extraction
• Message decoding
• Speech recognition
• Computer vision
• Gene finding
• Dia...
Observations:
None
Project health:
success = 68.91%
Project
health
Time: In time Time: Exceed
Budget:
Match
Budget:
Exceed
Budget:
Match
Budget:
Exceed
Success 95 50 40 10
Fa...
Bayesian Model with pgmpy
c_maturity_cpd =
TabularCPD(variable='Customer maturity', variable_card=2,
values=[[0.4, 0.6]], ...
Bayesian Model with pgmpy
pr_health_cpd =
TabularCPD(variable='Project health', variable_card=2,
values=[[0.95, 0.5, 0.4, ...
Bayesian Model with pgmpy
pr_model =
BayesianModel([('Customer maturity', 'Requirements elicitation'),
..., ('Time schedul...
Bayesian Model with pgmpy
res = BeliefPropagation(pr_model).query(variables=["Project health"])
print res["Project health"...
Observations:
Customer maturity: mature
Customer budget: large
Project health:
success = 73.82% (↑4.91%)
Bayesian Model with pgmpy
res = BeliefPropagation(pr_model).query(
variables=["Project health"],
evidence={'Customer budge...
Observations:
Customer maturity: mature
Customer budget: large
Resource availability: available
Project complexity: small
...
Observations:
Customer maturity: mature
Customer budget: large
Resource availability: available
Project complexity: comple...
Maturity
level
Lead
time
Team
composition
Staffing time
Story point
time variance
Risk
management
Granularity of
stories
S...
Events:
None
Project health:
success = 87.36%
Cooperation model: fixed bid
Project stage: stable
Cycle time: decreasing
Number of the opened and reopened
bugs: decreasi...
Observations:
Listed on previous slide
Project health:
success = 12.71% (↓ 74.65%)
Latest date:
2017-02-14
Project health:
success = 53.18%
Major degradation happened on:
2017-02-10
Reason:
Outage in Quali...
• New communication tool for all levels of the company which is
already in use on every day basis
• For C-level provides u...
• Improve metrics for measuring project performance
• Build even more intuitive dashboard
• Introduce the model to our cus...
Inspired by Technology.
Driven by Value.
eleks.com
DataScience Lab 2017_Графические вероятностные модели для принятия решений в проектном управлении_Ольга Татаринцева
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DataScience Lab 2017_Графические вероятностные модели для принятия решений в проектном управлении_Ольга Татаринцева

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Графические вероятностные модели для принятия решений в проектном управлении
Ольга Татаринцева (Data Scientist at Eleks)
Как часто вам приходится принимать решения, используя знания в определенной предметной области? На сколько хороши такие решения? А теперь представьте, что вы собрали знания лучших экспертов в предметной области. Похоже, что ваши решения, основанные на этих знаниях, будут куда более взвешенными, не так ли? Мы будем говорить о системе ProjectHealth, которая была построена на основе опыта лучших экспертов в проектном управлении в компании Eleks. Для реализации поставленной задачи была использована графовая вероятностная модель, а именно байесовская сеть, имплементированная на Python. За время работы над проектом мы прошли шаги от извлечения требований, поиска данных и построения модели с нуля до реализации BI дашборда с возможностью углубиться в детали, доходя до сырых данных. Сейчас ProjectHealth экономит большое количество времени для топ менеджмента и ресурсов компании, так как мониторит состояние бизнеса в малейших деталях ежедневно и делает это как настоящий эксперт.
Все материалы: http://datascience.in.ua/report2017

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DataScience Lab 2017_Графические вероятностные модели для принятия решений в проектном управлении_Ольга Татаринцева

  1. 1. REASONING WITH PROBABILISTIC GRAPHICAL MODELS IN A PROJECT BASED BUSINESS BY OLGA TATARYNTSEVA, DATA SCIENTIST
  2. 2. • Neural networks • Deep learning • Natural language processing • Image processing • Big data • Bioinformatics Today we are NOT about
  3. 3. • Neural networks • Deep learning • Natural language processing • Image processing • Big data • Bioinformatics • Project based business • Company organization • Project management and project success • Applying the DS methods to business problems • Small example • Results we gained Today we are NOT about We are about
  4. 4. Chief Technical officer ... Delivery Director ... PM ... Architects Business Analysts Developers QA Data scientists ... ... ... Simplified structure of delivery organization
  5. 5. Chief Technical officer ... Delivery Director ... PM ... Architects Business Analysts Developers QA Data scientists ... ... ... Simplified structure of delivery organization PROJECT
  6. 6. Scope Resources Quality Time Project management triangle
  7. 7. ProjectTeam Direct contacts with the customer Requirements management Solution design Product development Project budget planning Working on improvements and up-sale opportunities Management of internal activities DeliveryDirector Summarized projects’ budget and cash flow results Calculated CSAT Calculated ESAT General view of project activities Information from reports built by project team CTO Budget flow for vertical Tendencies of overall CSAT Tendencies of overall ESAT Information from reports built by DDs Information transfer
  8. 8. Chief Technical officer Delivery director PM PM … Delivery Director PM PM … ... PM ... 1000+ employees Simplified structure of delivery organization
  9. 9. Don’t panic We have PGM
  10. 10. CTO at ELEKS has a BI Dashboard
  11. 11. • Declarative representation of our understanding of the world • Identifies the variables and their interaction with each other • Sources: • experts’ knowledge • historical data • Representation, inference, and learning • Handles uncertainty Probabilistic graphical models
  12. 12. «As far as the laws of mathematics refer to reality, they are not certain, as far as they are certain, they do not refer to reality» Albert Einstein, 1921 Uncertainty
  13. 13. • Causal inference • Information extraction • Message decoding • Speech recognition • Computer vision • Gene finding • Diagnosis of diseases • Traffic analysis • Fault diagnosis PGM applications Daphne Koller
  14. 14. Observations: None Project health: success = 68.91%
  15. 15. Project health Time: In time Time: Exceed Budget: Match Budget: Exceed Budget: Match Budget: Exceed Success 95 50 40 10 Fail 5 50 60 90 Success = 𝑃 𝑆 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑚𝑎𝑡𝑐ℎ + 𝑃 𝑆 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑒𝑥𝑐𝑒𝑒𝑑 + 𝑃 𝑆 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑚𝑎𝑡𝑐ℎ + 𝑆 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑒𝑥𝑐𝑒𝑒𝑑 = = 0.95*0.6659*0.8071 + 0.5*0.6659*0.1929 + 0.4*0.3341*0.8071 + 0.1*0.3341*0.1929 = 68.91% F𝐚𝐢𝐥 = 𝑃 𝐹 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑚𝑎𝑡𝑐ℎ + 𝑃 𝐹 𝑖𝑛_𝑡𝑖𝑚𝑒, 𝑒𝑥𝑐𝑒𝑒𝑑 + 𝑃 𝐹 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑚𝑎𝑡𝑐ℎ + 𝐹 𝑒𝑥𝑐𝑒𝑒𝑑, 𝑒𝑥𝑐𝑒𝑒𝑑 = 31.09% Node name: Project health States success fail Depends on Time, Project Budget Conditional probability distribution
  16. 16. Bayesian Model with pgmpy c_maturity_cpd = TabularCPD(variable='Customer maturity', variable_card=2, values=[[0.4, 0.6]], evidence=[], evidence_card=[]) ... pr_health_cpd = TabularCPD(variable='Project health', variable_card=2, values=[[0.95, 0.5, 0.4, 0.1], [0.05, 0.5, 0.6, 0.9]], evidence=['Project budget', 'Time schedule'], evidence_card=[2, 2])
  17. 17. Bayesian Model with pgmpy pr_health_cpd = TabularCPD(variable='Project health', variable_card=2, values=[[0.95, 0.5, 0.4, 0.1], [0.05, 0.5, 0.6, 0.9]], evidence=['Project budget', 'Time schedule'], evidence_card=[2, 2]) print pr_model.get_cpds('Project health') +------------------+-----------+------------+------------+-------------+ | Project budget | match | match | exceed | exceed | +------------------+-----------+------------+------------+-------------+ | Time schedule | in_time | exceed | in_time | exceed | +------------------+-----------+------------+------------+-------------+ | success | 0.95 | 0.5 | 0.4 | 0.1 | +------------------+-----------+------------+------------+-------------+ | fail | 0.05 | 0.5 | 0.6 | 0.9 | +------------------+-----------+------------+------------+-------------+
  18. 18. Bayesian Model with pgmpy pr_model = BayesianModel([('Customer maturity', 'Requirements elicitation'), ..., ('Time schedule', 'Project health'), ('Project budget', 'Project health')]) pr_model.add_cpds(c_maturity_cpd, pr_complexity_cpd, time_cpd, req_elicitation_cpd, req_management_cpd, res_availability_cpd, c_budget_cpd, pr_budget_cpd, pr_importance_cpd, pr_health_cpd)
  19. 19. Bayesian Model with pgmpy res = BeliefPropagation(pr_model).query(variables=["Project health"]) print res["Project health"] +------------------+-----------------------+ | Project health | phi(Project health) | |------------------+-----------------------| | success | 0.6891 | | fail | 0.3109 | +------------------+-----------------------+
  20. 20. Observations: Customer maturity: mature Customer budget: large Project health: success = 73.82% (↑4.91%)
  21. 21. Bayesian Model with pgmpy res = BeliefPropagation(pr_model).query( variables=["Project health"], evidence={'Customer budget':1, 'Customer maturity':0}) print res["Project health"] +------------------+-----------------------+ | Project health | phi(Project health) | |------------------+-----------------------| | success | 0.7382 | | fail | 0.2618 | +------------------+-----------------------+
  22. 22. Observations: Customer maturity: mature Customer budget: large Resource availability: available Project complexity: small Project importance: very important Project health: success = 85.73% (↑11.91%)
  23. 23. Observations: Customer maturity: mature Customer budget: large Resource availability: available Project complexity: complex Project importance: very important Project health: success = 79.45% (↓ 6.28%)
  24. 24. Maturity level Lead time Team composition Staffing time Story point time variance Risk management Granularity of stories Stakeholders substitution Acceptance criteria rating RotationsLegal risks
  25. 25. Events: None Project health: success = 87.36%
  26. 26. Cooperation model: fixed bid Project stage: stable Cycle time: decreasing Number of the opened and reopened bugs: decreasing Finance: fit the company’s KPI values Environment: tools are set and in-use Soft- and hardware: no blocking requests Human resources: no open vacancies Customer satisfaction index: high Number of tasks in the in-progress state: increasing Predicted release date: out of the schedule Hypothetic Project observations
  27. 27. Observations: Listed on previous slide Project health: success = 12.71% (↓ 74.65%)
  28. 28. Latest date: 2017-02-14 Project health: success = 53.18% Major degradation happened on: 2017-02-10 Reason: Outage in Quality Reason: Descending of Project Effectiveness Reason: Cumulating of tasks in Progress Real project
  29. 29. • New communication tool for all levels of the company which is already in use on every day basis • For C-level provides understanding: • Of the department • Of each project in particular • For Project Manager it is an instrument: • For project organization and control • For the Client: • Fair presentation of the project work Benefits
  30. 30. • Improve metrics for measuring project performance • Build even more intuitive dashboard • Introduce the model to our customers • Improve the reflection of the domain by fitting the model to data Future work
  31. 31. Inspired by Technology. Driven by Value. eleks.com

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