1. What is Business Intelligence?
A broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions.

2. History of BI: Past, Present, & Future
“Knowing the past is important because it provides context and helps in understanding many of today’s practices.”
Hugh Watson, 2009

3. BI definition, frameworks, and concepts
History of BI
BI origins
BI definition, frameworks, and concepts
BI trends & resources

4. BI Origins Much of the early work was done at MIT & Harvard
In his dissertation work (1967), Morton built, implemented, and tested a “management decision system” to support the coordination of production planning for laundry equipment
Michael Scott Morton

5. BI Origins
Many “decision support systems” were developed from late 1960s to mid 1970s
Sprague’s framework
Alter’s typology
Power’s taxonomy
Ralph Sprague, Jr.
Steve Alter
Daniel J. Power

6. Sprague’s Framework
Sprague, R. & Watson, H “Bit by Bit: Toward Decision Support Systems,” California Management Review, p. 64.

7. Alter’s Typology
Alter, S “A study of computer aided decision making in organizations,” Doctoral Dissertation: MIT, p. 5.

8. Power’s Taxonomy Communications Data Document Knowledge Model
Source:

9. Power’s Taxonomy Communications Data
Support collaboration and communication
Audio conferencing
Bulleting boards
Data
Emphasize access to and manipulation of a time-series data stored in a data warehouse
Dashboards/scorecards
Business reports
Source:

10. What is a data warehouse?
A database designed to support decision making in organizations. It is batch updated and structured for rapid online queries and managerial summaries. Data warehouses contain large amounts of data. A data warehouse is a subject-oriented, integrated, time-variant collection of data in support of management's decision making process

11. Power’s Taxonomy Document Knowledge
Support the search and retrieval of unstructured documents
Transcribed conversations
Memos
Knowledge
Incorporate knowledge, experience, and judgment of experts in a particular domain and recommend courses of action
Expert system
Source:

12. Power’s Taxonomy Model
Emphasize access to and manipulation of a model, for example, statistical, financial, optimization and/or simulation
Revenue management
Production planning
Source:

13. BI Origins— The more things change…
Internet and web-based applications changed how people search for information
Encyclopedia in physical library vs. Google search
Yet, much remains the same
Critical success factors (1979) vs. Key performance indicators (1997) vs Dashboards/Scorecards (now)
Iterative, evolutionary design (1980) vs. Agile design (now)
Names and some nuances may change, but many of the fundamental concepts are the same

14. What is Business Intelligence?
A broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions.
The term was coined in 1989 by Howard Dresner (Gatner analyst)

15. Three BI targets A single BI application Enterprise-wide BI
A point solution for a departmental need
A data mart is created to provide data
Enterprise-wide BI
The creation of infrastructure supporting current and future BI needs
A data warehouse is created
Organizational BI transformation
BI is used to fundamentally change how a company competes in the marketplace
Supports a new business model and enables the business strategy
3AM = Its divisions operated independently and had separate decision support platforms
Harrah’s = created a customer loyalty program encouraging cross-casino play. It needed a BI infrastructure collecting data across various customer touchpoints (slot machines). Using this data, Harrah’s became an industry leader because of its ability to reward loyal customers and reach out to them in a personal way

16. How is a data mart different than a data warehouse?
A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales or Finance or Marketing

17. A generic BI environment— “Getting the data in” and “Accessing the data”

18. Source systems “Getting the data in”
All systems that provide BI with data
Single-source vs. enterprise-wide
Type of source system is chosen based on the business need motivating BI. Yet, a variety of systems can be used
ERP
Operational systems
Customer demographic data

19. Source systems “Getting the data in”
Typically use different platforms (IBM & Oracle) and store data in different formats (relational vs. hierarchical)
Profile data to verify documentation quality
Additional issues
What is the best source system?
How granular is the data?

20. Data integration “Getting the data in”
Extract, transform, and load (ETL)
Extraction can be performed by hand-written code (SQL), yet it is usually performed by commercial software
Transformation based on business rules
Use of Male and Female to denote gender instead of 0 and 1 or M and F
Loading occurs during a “window”
The window is narrower (real-time)

21. Storing data—Architecture “Getting the data in”
Independent data marts
Do not provide a “single version of truth”
Enterprise data warehouse
Dependent marts are created in iterative manner
Provide a “single version of truth”
Centralized
Data mart bus (Kimball Bus Architecture)
Dimensional data model
Federated
Data integration through shared keys

22. Storing data—Architecture “Getting the data in”
Enterprise data warehouse = Data mart bus > Federated > Independent data marts (Ariyachandra & Watson 2006)

23. Who are the users? “Accessing the data”
Information producers
IT developers
Analysts
Information consumers
Managers and executives
Front line workers, suppliers, customers, regulators

24. What are the applications? “Accessing the data”
SQL
IT developers & analysts
Business reports
Front line workers
Excel (most popular)
Dashboards/Scorecards
Data visualization
Predictive analytics

25. MicroStrategy Dashboard

26. Metadata “Accessing the data”
Data about the data (important!)
IT personnel who “get data in” need to know
What data is stored where
Table and attribute names
Users need metadata to support “getting data out,” including
Timeliness and data quality
Who has access to specific data and reports
Do not use Excel!

27. Data quality “Accessing the data”
Major problem in most companies
Data profiling is a start but correcting at the source is a desired solution
“There isn't a “quick fix” for data quality problems” – Think of Nestle!
Data must be accurate enough
Consistently well defined and used
Necessary data must be available and timely
Easily accessible, understandable, and usable

28. Governance— Multi-level & cross functional
Strategic level
VPs often provide strategic direction that is aligned with company objectives
Tactical level
Directors ensure projects are on time and schedule
Operational level
Users address metadata and quality issues

29. BI benefits

30. Note to Carolina: Stop here!
Finish material on Wednesday!