We are living in an era where changes are taking place very quickly. Imagine a scenario where we can use Machine Learning to identify changes that are associated with incidents and problems. Then classify them using the Cynefin framework to determine the context of change according to the 5 domains: simple, complicated, complex, chaotic and disorder (natural science). Come up with a Decision Intelligence model that can provide the best answer to the potential problems caused by this ever evolving environment. Finally add the Requests to the Incident Model, as ITSM focus, on IT4IT Value Stream Network!

1. Guttenberg Ferreira Passoshttps://www.linkedin.com/posts/robakershoek_de
vops-it4it-itil-activity-6616362686705876992-K1Fo
Image: Rob Akershoek
Problem Prediction Model
with Changes and Incidents

2. Agenda
1. Challenges
2. Preprocessing Model
3. Analysis Model
4. Clustering model
5. Training Model
6. Prediction Model
7. Predictions
8. Conclusion

3. Challenges
1. Identify the most relevant key words in
problems, changes and incidents (ITIL) in an
Information Technology company;
2. Evaluate changes using various artificial
intelligence algorithms to anticipate
potential problems or incidents.

4. Guide to getting started with AI
https://medium.com/@kozyr_91350/o-guia-definitivo-
para-come%C3%A7ar-com-ia-e7e7dc68f376
Fonte: Cassie Kozyrkov

5. Orange Canvas
• Sandeco
https://www.youtube.com/watch?v=5bjY_WyZKm4
• Ajda Pretnar
https://www.youtube.com/watch?v=HXjnDIgGDuI&t=10s

6. Preprocessing Model

7. Read File

8. Preprocess Text

9. Bag of Words

10. Word Cloud

11. Corpus Viewer

12. Data Table

13. Analysis Model

14. Clustering model

15. Multidimensional Scale - MDS

16. Training Model

17. Test & Score

18. Confusion Matrix

19. Area Under the Curve - ROC

20. Prediction Model

21. Predictions

22. Classifiers
 AdaBoost
https://en.wikipedia.org/wiki/AdaBoost
 KNN
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
 Logistic Regression
https://en.wikipedia.org/wiki/Logistic_regression
 Naive Bayes
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
 Random Forest
https://en.wikipedia.org/wiki/Random_forest

23. Classifier
https://www.youtube.com/watch?v=Q8l0Vip5YUwFonte:

24. Improvements incorporated
1. Identify the keywords (Tokens & Tags) of changes and problems to
properly feed Bag of Words
2. Replace the contents of the Impact field of the problem file with
another type of identification (Token & Tag)
3. Replace the contents of the Customers field from the problem file
with the customer related to the Control Item - CI
4. Identify problem-causing keywords (Tokens & Tags)
5. Develop Incident Model
6. Address narrow scope with database-related issues

25. The Problem Prediction Model has the function of evaluate the
changes (ITIL 4), using various artificial intelligence algorithms,
to anticipate possible problems and incidents.
The idea proposed is to address a narrow scope with issues
related to the database theme, involves the migration of
approximately 100 databases to an Exadata environment, with
incidents already incorporated.
We conclude that the Model is an important tool for change
analysis aiming to identify the main occurrences and anticipate
possible problems and incidents, using the Naive Bayes classifier
from Orange software.
Conclusion

26. Next steps
1. Initially maintain Change Management with ITIL 3
2. Establish a pilot project implementing Change Enabling with ITIL 4
3. Develop a Decision Model based on the Cynefin framework to
determine the context of change according to the 5 simple,
complicated, complex, chaotic and disorder realms
4. Add the Requests to the Incident Model, as ITSM focus, on IT4IT
Value Stream Network

27. Next steps - Cynefin

28. Next steps - IT4IT

29. Thanks!
Guttenberg Ferreira Passos
gut.passos@gmail.com