Business Intelligence (BI) involves transforming raw transactional data into meaningful information for analysis using techniques like OLAP. OLAP allows for multidimensional analysis of data through features like drill-down, slicing, dicing, and pivoting. It provides a comprehensive view of the business using concepts like dimensional modeling. The core of many BI systems is an OLAP engine and multidimensional storage that enables flexible and ad-hoc querying of consolidated data for planning, problem solving and decision making.

1. Business Intelligence
(BI)
Lecturer: PhD Taras V. Panchenko
Associate Professor
@ Theory and Technology for Programming Chair
@ Cybernetics Faculty
@ National Taras Shevchenko University of Kyiv

2. Introduction
Computers are useless.
They can only give you answers.
Pablo Picasso
… Meaning that asking questions and true
creativity are things that computers aren't
capable of yet.

3. BI – Def(s)
Business intelligence (BI) is the ability to apprehend the
interrelationships of presented facts in such a way as to
guide action towards a desired goal.
Hans Peter Luhn, IBM, 1958
BI is the transformation of raw data into meaningful and
useful information for business analysis purposes.
Wikipedia

4. BI – Def(s)
Business intelligence (BI) BI is the transformation of raw
data into meaningful and useful information for business
analysis purposes.
• BI can handle enormous amounts of unstructured data
to help identify, develop and otherwise create new
strategic business opportunities
• BI allows for the easy interpretation of volumes of data
• Identifying new opportunities and implementing an
effective strategy can provide a competitive market
advantage and long-term stability
Wikipedia

5. BI – Def(s)
Business intelligence (BI) is an umbrella term that
includes the applications, infrastructure and tools, and
best practices that enable access to and analysis of
information to improve and optimize decisions and
performance.
Gartner
BI is a set of methodologies, processes, architectures,
and technologies that leverage the output of information
management processes for analysis, reporting,
performance management, and information delivery.
Research coverage includes executive dashboards as well
as query and reporting tools.
Forrester

6. The Problem
• Try to model and analyze activity of the bank
– Develop the model: clients, accounts, currency,
transactions, …
– Possible questions to system/model from analyst
• Performance (analytical query speed)
• Dynamic reports & ad-hoc analysis
• or: analyze sales of products by regions in time
• Is RDBMS the best solution?
… for Multidimensional model …

7. Multidimensional Analysis
• Multidimensional (hyper-)cube:

8. Problem of Relational Database Model
• Most notably lacking has been the ability to
consolidate, view, and analyze data according
to multiple dimensions, in ways that make
sense to one or more specific enterprise
analysts at any given point in time. This
requirement is called “multidimensional data
analysis.”
E.F. Codd

9. Limitations: lack of … analytics
• Until recently, the end-user products that had
been developed as front-ends to the relational
DBMS provided very straightforward simplistic
functionality. The query/report writers and
spreadsheets have been extremely limited in the
ways in which data (having already been
retrieved from the DBMS) can be aggregated,
summarized, consolidated, summed, viewed, and
analyzed.
E.F. Codd

10. BI (or – partially – OLAP)
• Is the solution
• The only one “point of truth”
– Contains all information about business
(… or any subject area) in one place
• Gives analytical & reporting means
– Speed (performance)
– Flexibility (many instruments)

11. BI is about
• Decision Support Systems
• Business Analytics
• Complex & Comprehensive, Intelligent
Reporting
• Multidimensional Analysis (real-time)
• “OLTP -> OLAP” – is the part of strategy
– OLAP is the core of BI

12. BI core: Multidimensional engine
(model, storage)
• Multidimensional (hyper-)cube:

13. ETL = OLTP  OLAP
• OnLine Transaction Processing System
– accounting of transactions
(E)xtract
(T)ransform
(L)oad
• OnLine Analytical Processing System
– gives analytical, intelligence (to transactional data)

14. OLAP (~Def.)
• Is an approach to answering multi-dimensional
analytical queries swiftly
• Technology for information processing for
quick answering on multidimensional
analytical queries
• Allows consolidation and analysis of data in a
multidimensional space
• Is not stand-alone!
– but based on OLTP data

15. OLAP Applications
• Business reporting
– Sales
– Marketing
– Management etc.
• Financial Reporting
• Budgeting
• Forecasting
• Planning
• Business Process Management
… in any business (any subject area)

16. The Difference
• OLTP: Operations
– RDBMS
• Large number of
short transactions
• 3NF, ER-model
– ACID
Atomicity, Consistency,
Isolation, Durability
– Business Process
– Online, real-time info
• OLAP: Information
– Multidimensional
• Complex queries
involve aggregations
• Sparse n-dim. spaces
– Aggregates
precalculated
– Analytical Data
Warehouse
– Large historical data
storage

17. Transaction vs. Analytical Approach
Transaction Systems Analytical Systems
Technology OLTP OLAP
Data visualization Grid (Table) Pivot Table
End-user visual querying QBE Cube browsing
(drill-down, slice & dice)
Query language SQL MDX + XMLA

18. OLTP vs. OLAP
OLTP System – Online Transaction Processing
(Operational System)
OLAP System – Online Analytical Processing
(Data Warehouse)
Source of
data
Operational data; OLTPs are the original source of
the data.
Consolidation data; OLAP data comes from the various
OLTP Databases
Purpose of
data
To control and run fundamental business tasks
To help with planning, problem solving, and decision
support
What the
data
Reveals a snapshot of ongoing business
processes
Multi-dimensional views of various kinds of business
activities
Inserts and
Updates
Short and fast inserts and updates initiated by
end users
Periodic long-running batch jobs refresh the data
Queries
Relatively standardized and simple queries
Returning relatively few records
Often complex queries involving aggregations
Processing
Speed
Typically very fast
Depends on the amount of data involved; batch data
refreshes and complex queries may take many hours;
query speed can be improved by creating indexes
Space
Require-ments
Can be relatively small if historical data is
archived
Larger due to the existence of aggregation structures and
history data; requires more indexes than OLTP
Database
Design
Highly normalized with many tables
Typically de-normalized with fewer tables; use of star
and/or snowflake schemas
Backup and
Recovery
Backup religiously; operational data is critical to
run the business, data loss is likely to entail
significant monetary loss and legal liability
Instead of regular backups, some environments may
consider simply reloading the OLTP data as a recovery
method

19. BI includes
• ETL procedure (= Extract – Transform – Load)
– often: Data Warehouse, via Data Marts
• OLAP Multidimensional Storage & Engine
– Ad-hoc questions & multi-purpose querying
• Reporting
– flexible, interactive, dynamic, effective, …
• Data Mining
– clustering, associations, trends (time analysis),
predictions, …

20. BI core is OLAP
(engine & storage)
• Multidimensional (hyper-)cube:

21. OLAP Concepts
• Flexible Information Synthesis
• Multiple Data Dimensions /
/ Consolidation Paths
(i.e. Multidimensional Conceptual View)

22. Data Consolidation
• Dimension hierarchy

23. OLAP Characteristics
• Dynamic Data Analysis
• Four Enterprise Data Models
– Categorical, Exegetical, Contemplative, and
Formulaic Models (укр.: накопичення фактів –
інтерпретація – аналіз – висновки,
моделювання, прогнози, …)
• Common Enterprise Data
• Synergistic Implementation

24. OLAP Server Mediating Role

25. OLAP Product Evaluation Rules
1. Multidimensional Conceptual View
2. Transparency
3. Accessibility
4. Consistent Reporting Performance
5. Client-Server Architecture
6. Generic Dimensionality
7. Dynamic Sparse Matrix Handling
8. Multi-User Support
9. Unrestricted Cross-dimensional Operations
10. Intuitive Data Manipulation
11. Flexible Reporting
12. Unlimited Dimensions and Aggregation Levels
Codd E.F., Codd S.B., and Salley C.T.,
“Providing OLAP to User-Analysts: An IT Mandate”,

26. Main OLAP features
• Drill-down
– Drill-up
– Drill-through
• Slice and dice
– Pivoting

27. OLAP Multi-dimensional Modes
• MOLAP = Multi-dimensional
– Pure OLAP
• ROLAP = Relational
– OLAP requests -> relational backend
• HOLAP = Hybrid
– Aggregates – MOLAP
– Basic facts – ROLAP
– Inconsistences possible!

28. OLAP cube example

29. OLAP cube slicing (1)

30. OLAP cube slicing (2)

31. OLAP dicing

32. OLAP drill-up  drill-down

33. OLAP pivoting

34. BI Solutions. Client Sales

35. BI Solutions. Sales by territory

36. BI Solutions. Plan/Sales Analysis

37. BI core = OLAP engine & storage
• Multidimensional (hyper-)cube:

38. BI Introduction
• Business Intelligence enhances business (or
any other area) vision and understanding
• OLAP is a core of BI
• BI includes
– ETL (OLTP  Data Warehouse with Data Marts 
OLAP)
– OLAP Multidimensional Storage & Engine
– Reporting (multi-purpose, comprehensive)
– Data Mining (clustering, associations, trends (time
analysis), predictions, …)