Business Intelligence Initiative

AIRPO ConferenceJune 18, 2008

Presented by : Helen ErnstHelen.Ernst@SUNY.edu

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.

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).

Analytic Culture

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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.

Strategy

Mission, Goals, Strategy ->Defining measurable outcomes (KPI)

Planning

Budgets, Plans, Forecasts, Models -> Set Targets

Monitor/Analyze

Dashboards Business Intelligence

Act/Adjust

Alerts -> Actions, Decisions, Adjust plans

Integrated

Information

Strategy

Execution

Transactional vs.. Analytical Systems

1. Organized and managed to support transaction processing

2. Organized data based on specific business operations (registration, alumni, giving)

3. Efficient, inserts and updates

4. Standards and consistency within each operational area

5. Data is constantly changing6. Stable systems7. Requires high level of

computing skills.

1. Organized and managed based on reporting & analytical needs

2. Integrating and Organizing data into subject areas across business operations

3. Efficient, fast retrieval 4. Enforces standards and

consistency in data across functional areas

5. Preserves historical and current information

6. Adaptive systems 7. Appeals to wide range of

computing skills

Operational/Transactional Analytical and Reporting

• 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

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Establishing Analytical Culture

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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

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.

Proof of Concept Projects

SUNY Proof of Concepts• UB: Strategic Information Reporting Initiative

(SIRI)– Contact: Joe Kerr jkerr@buffalo.edu

• OSA: University Business Intelligence Data Management– Contact: Helen Ernst Helen.Ernst@suny.edu

• OSA: Banner Reporting and Analytics– Contact: Ron Brownron@itec.suny.edu

SUNY OBIEE ProjectsBuffalo UniversityProject Lead – Joe KerrStrategic 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.

SUNY OBIEE ProjectsSystem AdministrationProject Lead – Helen ErnstConsultants on site starting 1/27 for 6 weeksProof 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.

SUNY OBIEE Projects

System Administration Project Lead – Ron BrownImplement 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)

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

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.

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.

Security Requirements

– Integrate with Single Sign-on– Use Initialization Blocks (Queries)– Session Variables & Header Records

– Longer Term Vision, Shibboleth Identity & Federated Model

Lessons Learned

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

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?

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?

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.

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

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….

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….

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.

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.

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.

Challenges

• Expectation Management / Communication

• Convince people to think strategically• Politics• Culture • Maintain Support• Scope and Project Management

Challenges

• Individual Resistance To Change• Departmental Autonomy• Long Switching Time And Resources

(users)• Executive Sponsorship / Support• Negotiating Pricing With Vendors

Challenges

• Data Quality:– Issues from source systems– Lack of consistency– Fragmentation – many sources– Reliability

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

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

Oracle Business Intelligence Enterprise

Edition

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)

Answers

• Ad Hoc Reporting Tool• Provide ‘power users’ with ability to

access data directly• Requires basic training (1 day)

OBIEE ComponentsDelivers. 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 email 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.

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

Demo & Questions ?

Thank you

AIRPO ConferencePresented by : Helen Ernst

Helen.Ernst@SUNY.edu