1. Business Intelligence Initiative
AIRPO BI Presentation
6/12/2008
Business Intelligence Initiative
AIRPO Conference
June 18, 2008
Presented by : Helen Ernst
by: Helen Ernst

2. What is a Data Warehouse?
The data warehouse is a collection of data that is pulled together primarily from operational business systems and is structured and tuned for easy access and use by information consumers and analysts, especially for the purpose of decision making.

3. What is ‘Business Intelligence’ software?
A set of concepts and methods to improve business decision making by using fact-based support systems. BI is sometimes used interchangeably with briefing books, report and query tools and executive information systems. Business Intelligence systems are data-driven DSS(decision support services).

4. Analytic Culture

5. AIRPO BI Presentation
6/12/2008
The Fundamental Goal
The fundamental goal of the SUNY Data Warehouse Initiative is to integrate administrative data into a accurate, consistent, and reliable information resource that supports planning, forecasting, and decision-making processes at SUNY. 5
by: Helen Ernst 5

6. Strategy Execution Integrated Information Strategy
AIRPO BI Presentation
6/12/2008
Strategy
Strategy
Mission, Goals, Strategy ->Defining measurable outcomes (KPI)
Planning
Budgets, Plans, Forecasts, Models -> Set Targets
Integrated
Information
Act/Adjust
Alerts -> Actions, Decisions, Adjust plans
Monitor/Analyze
Dashboards Business Intelligence
Balanced scorecard ---
Execution
by: Helen Ernst 6

7. Transactional vs.. Analytical Systems
AIRPO BI Presentation
6/12/2008
Transactional vs.. Analytical Systems
Operational/Transactional
Analytical and Reporting
Organized and managed to support transaction processing
Organized data based on specific business operations (registration, alumni, giving)
Efficient, inserts and updates
Standards and consistency within each operational area
Data is constantly changing
Stable systems
Requires high level of computing skills.
Organized and managed based on reporting & analytical needs
Integrating and Organizing data into subject areas across business operations
Efficient, fast retrieval
Enforces standards and consistency in data across functional areas
Preserves historical and current information
Adaptive systems
Appeals to wide range of computing skills
Ora Fish, Rensselaer Polytechnic Institute
by: Helen Ernst 7

8. Establishing Analytical Culture
AIRPO BI Presentation
6/12/2008
Establishing Analytical Culture
Executive sponsorship
Requires marketing and communication to all levels of the institute
Organizational will and accountability – DW Group
Close collaboration between IT and the functional units
Ora 8
by: Helen Ernst 8

9. DW Results: Data Quality
AIRPO BI Presentation
6/12/2008
DW Results: Data Quality
Common definitions  Consistency
Reinforces Institution’s rules and definitions  Integrity
Processes, technology, and people revealing data entry errors  Accuracy
Central repository  ensures “One Version of the Truth”  Reliability
Jeff – People want “magic bullet” technology that provides accurate, reliable, consistent information. It’s not the technology. 9
by: Helen Ernst 9

10. Results: Effectiveness
AIRPO BI Presentation
6/12/2008
Results: Effectiveness
Empowers decision-makers by enabling direct access to accurate, consistent, and non-volatile information – this is the heart of business intelligence.
Redirects costly personnel hours from data gathering, matching, and consolidating to data analysis while reducing the need for information workers to replicate data and maintain redundant tracking (shadow) systems.
Jeff – Testimonials can be found on our project web site.
by: Helen Ernst 10

11. Proof of Concept Projects

12. SUNY Proof of Concepts
UB: Strategic Information Reporting Initiative (SIRI)
Contact: Joe Kerr
OSA: University Business Intelligence Data Management
Contact: Helen Ernst
OSA: Banner Reporting and Analytics
Contact: Ron

13. SUNY OBIEE Projects Buffalo University Project Lead – Joe Kerr
Strategic Information Reporting Initiative (SIRI)
Develop a complete strategy and implementation plan for aggregating and integrating the various sources of information that are important to the strategic, managerial, and operational concerns of the university and its units and departments. Create an easy and straightforward interface that can be used by unit CFO’s, department managers, and central administrators to access, interpret, and report the data.

14. SUNY OBIEE Projects
System Administration
Project Lead – Helen Ernst
Consultants on site starting 1/27 for 6 weeks
Proof of Concept
The goal of the University Business Intelligence Data Management project is to leverage the existing SUNY Data Warehouse located at System Administration to present data and comprehensive management reporting directly to the desktops of Executive and Senior and middle management.

15. SUNY OBIEE Projects System Administration Project Lead – Ron Brown
Implement OBIEE using Banner using Fredonia data stored in ODS (Operational Data Store) at Oneonta, OBI server in Buffalo
Implement OBIEE Plus using a server at ITEC (located at Buffalo State College) and Banner test data stored in ODS (Operational Data Store) at SICAS (located at Oneonta)

16. POC Goals Single Point Of Entry (web or portal) Single Source Of Truth
Single Sign-On: Shibboleth / Federated Identity Model
Security: Table, Column, Row
Multiple Locations - Can access / interconnect multiple OBIEE Plus servers and other IT resources at different locations
Integration of dashboards

17. POC Goals
Assess the end user experience using OBIEE Plus vs. traditional reporting like SQR.
Demonstrate ease of use regardless of skill level; executive, faculty, student, alumni, community ,etc.
Determine if OBIEE Plus can be used for reporting and analytics from other sources, such as: Foundations, Auxiliary Services, Book Store Systems, Parking Systems, other data campus needs BI information.

18. POC Goals
Determine viability of the level of reporting and analytics that can be reasonably done by users. I.e., Ad hoc reporting and analytics.
Determine viability to create custom reports and dashboards with minimal IT support.
Integrate with SUNY preferred applications.
Provide campus access to public data
Provide public access to appropriate data.

19. Security Requirements
Integrate with Single Sign-on
Use Initialization Blocks (Queries)
Session Variables & Header Records
Longer Term Vision, Shibboleth Identity & Federated Model

20. Lessons Learned

21. Lessons Learned
Senior Management Commitment, translated into organization commitment is critical - Without Management you get nothing
Build a good project team, including a Steering committee and a working group - Without people you get nothing
Provide leadership for the initiative and its projects
Thank people for the great job they are doing - include end users

22. Lessons Learned
Collaboration and communication cannot be an afterthought and don’t loose site of it for the initiative and each of its projects.
Making the sale never stops
Focus on management, customers, team, vendor, data owners, etc.
Formal milestones tied to estimate and timeframe reviews / updates
Will your Data Authorization process survive ODS / DW / BI?

23. Lessons Learned
Data is the most critical piece of the solution, once that is in place and done well the rest is considerably easier, though not without challenge.
Having the “data” correct and accessible then allows the “information” to be meaningful.
Balance data quality solutions at the source vs. in the ETL vs. time.
Real solutions happen at the source
Did you count on having to address the data issues you find during the ETL process? And, fast?

24. Lessons Learned
You will need more time for testing than you put in your plan.
Ask yourself periodically, how am I making my customer’s lives better? Am I?
Address issues and problems as soon as they appear
Deploy Business Intelligence tools and solutions in a systematic and consistent manner.

25. Lessons Learned Partner with consultants, make them part of the team
Consultants need ownership and risk as well.
Make sure each project has an end. The initiative will go on.
Plan who will support what you build

26. Lessons Learned
Provide a stream of deliverables, and gradually scale up the audience.
It will take longer and more resources than you think.
Weekly status updates, open communication channels, and trust.
You need to be able to openly discuss problems and challenges.
Technology is not the hard part….

27. Lessons Learned
This is NOT an IT Project, it is Everyone’s Project! Management to end users and vendors.
Weekly status updates and open communications lead to trust. It must be earned.
You need to be able to openly discuss problems and challenges.
Technology is not the hard part….

28. Lessons Learned
The key to standardizing Business Intelligence tools is to make them conform to the way your users work / think, and not vice versa.
Fit Business Intelligence tools to the user and the different roles they play.
Monitor Usage - Shows effectiveness of a Business Intelligence environment and training programs to monitor usage.

29. Lessons Learned Tool of choice for many power users and managers.
Excel can be a legitimate Business Intelligence tool when used as a front end to an analytic server.
Business Intelligence vendors are now embracing Excel and other Microsoft Office tools.
Greatly aids BI standardization efforts.

30. Lessons Learned
Don’t deploy Business Intelligence in a haphazard manner
Management wants consistency across departments, reports, and measures to facilitate communication and decision making.

31. Challenges Expectation Management / Communication
Convince people to think strategically
Politics
Culture
Maintain Support
Scope and Project Management

32. Challenges Individual Resistance To Change Departmental Autonomy
Long Switching Time And Resources (users)
Executive Sponsorship / Support
Negotiating Pricing With Vendors

33. Challenges Data Quality: Issues from source systems
Lack of consistency
Fragmentation – many sources
Reliability

34. Next Steps Create a plan to train SUNY users
Server Architect
Repository/Meta Data
Dashboard/Report Development
Dashboard User
Provide Campuses with BI Support – BI Compentency Center
SUNY BI User Group
Confluence site
Banner ODS/EDW Repository Development
Banner ODS/EDW Dashboard/Report Development
Library
Distance Learning Center
Campus access to System Admin BI Dashboards

35. Next Steps Develop SUNY BI best practices
Work with Campuses to adopt standards for BI reporting
Work towards Shibboleth/Trusted Federation to provide single sign on for BI SUNY wide.
Begin to convert existing DW reporting into BI environment

36. Oracle Business Intelligence Enterprise Edition

37. Dashboard Concept Personal Dashboard (My Dashboard) Shared Dashboards
AIRPO BI Presentation
6/12/2008
Dashboard Concept
Personal Dashboard (My Dashboard)
Shared Dashboards
Training helpful, but not required
Multiple requests per dashboard
Can group requests as we design
by functional office (Finance)
content area (Cohort)
by interest (Community College Data)
by: Helen Ernst

38. Answers Ad Hoc Reporting Tool
Provide ‘power users’ with ability to access data directly
Requires basic training (1 day)

39. OBIEE Components
Delivers. Schedules queries to run on specific cycles. Can be used to trigger alerts. An alert can be created that will notify the user through delivery options, such as or cell phone.
Disconnected Analytics Oracle BI Disconnected Analytics allows you to view analytics data, Oracle BI Interactive Dashboards, and queries when you cannot connect to the network to access the Oracle Business Intelligence application.
BI Publisher offers a reporting solution available for complex, distributed environments. It provides a central architecture for generating and delivering information—securely and in the right format.

40. Administration Tool Repository Development Technical users
Requires Training
Provides a rich assortment of tools to enable access to DW stars and Relational or ODS environments.
Security Administration

41. Demo & Questions ? Thank you
AIRPO Conference
Presented by : Helen Ernst