Session about types of analytics. Descriptive, diagnostic, predictive and prescriptive analytics. Conference DATA ANALYSIS DEVELOPMENT 2016 by RZECZPOSPOLITA.

1. Prescriptive Analytics
Łukasz Grala
Architect Data Platform & Business Intelligence Solutions
lukasz@tidk.pl

2. Łukasz Grala
Architect Data
Platform
& BI Solutions |
Microsoft MVP Data
Platform
 Architekt rozwiązań platformy danych i BI
 Projektant, trener i konsultant
 Wykładowca na wyższych uczelniach
 Autor artykułów, publikacji i webcastów
 Związany naukowo z Wydziałem Informatyki Politechniki
Poznańskiej (architektura baz i hurtowni danych, uczenie
maszynowe, eksploracja danych)
 Zawodowo architekt freelancer w TIDK, trener i ekspert w
SQLExpert, współpracownik technologiczny Microsoft
 Członek PLSSUG, PTI
 Prelegent na licznych konferencjach
lukasz@tidk.pl

3. Agenda
 Types of Analytics and Business
Intelligence
 Prescriptive analytics
 Summary
lukasz@tidk.pl

4. Types of
Analytics
Difficulty
Value
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
What
happened?
What did it
happen?
What will
happen?
How can we
Make it happen?
lukasz@tidk.pl

5. Descriptive and
Diagnostic Analytics

6. Business
Intelligence
lukasz@tidk.pl
ETL
BI
Applications
Enterprise Data
Warehouse (EDW)
 Normalized tables
(3NF)
 Atomic data
 User queryable
ETL
Presentation Area:
 Dimensional (star
schema or OLAP
cube)
 Atomic and summary
data
 Organized by
business process
 Uses conformed
dimensions
Enterprise DW Bus
Architecture
Source
Transactions
Back Room Front Room
Source: Hybrid Hub-and-Spoke and Kimball Architecture (R.Kimball, M.Ross „The DataWarehouse Toolkit” 3ed - 2013

7. Self-BI
lukasz@tidk.pl

8. Self-BI
lukasz@tidk.pl

9. Self-BI
lukasz@tidk.pl

10. Self-BI
lukasz@tidk.pl

11. IoT – Big
Data
lukasz@tidk.pl

12. BI Solutions
ETL Tool
(SSIS, etc) EDW
(SQL Server, Teradata, etc)
Extract
Original Data
Load
Transformed
Data
Transform
BI Tools
Ingest (EL)
Original Data
Scale-out
Storage &
Compute
(HDFS, Blob Storage,
etc)
Transform & Load
Data Marts
Data Lake(s)
Dashboards
Apps
Streaming data
lukasz@tidk.pl

13. Dashboards
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14. Dashboards
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15. Mobile
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16. Predictive Analytics

17. Key concept
– Machine
Learning
lukasz@tidk.pl
Data
Model
Parameters
Learning Prediction
Decision Making
Utility Function

18. Classes
Learning
Problems
lukasz@tidk.pl
 Classification: Assign a category to each item (Chinese | French
| Indian | Italian | Japanese restaurant).
 Regression: Predict a real value for each item
(stock/currency value, temperature).
 Ranking: Order items according to some criterion
(web search results relevant to a user query).
 Clustering: Partition items into homogeneous groups
(clustering twitter posts by topic).
 Dimensionality reduction: Transform an initial representation of items
into a lower-dimensional representation while preserving some
properties (preprocessing of digital images).

19. Steps to build
Machine
Learning
Solution
lukasz@tidk.pl

20. Azure
Machine
Learning
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21. Azure
Machine
Learning
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22. Prescriptive Analytics

23. Prescriptive
Analytics
lukasz@tidk.pl
e.g. business rules, regulatory requirments,
technolgoy requirments, HR policies
e.g. profit per item, per unit,
throughput per hour
Find best solutions (variable values) to meet objectives
e.g. maximise profit, minimise cost, minimise downtime

24. Models
 Mathematicals and statistics
 Computional science:
 Machine Learning
 Image Processing
 Operations Researech
 Natural Language Processing
 Text Mining
 Pattern Recognition
 Applied statistics
lukasz@tidk.pl

25. Optimistation
lukasz@tidk.pl
Quantity
Cost
Linear optimisation

26. Optimistation
lukasz@tidk.pl
Price
Demand
Non-linear optimisation

27. Optimisation
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Passengers
Airplanes
Discrete - integer optimisation

28. Microsoft
Solution
(example)
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29. Summary
Source: Gartner (October 2014)
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30. lukasz@tidk.pl Question?