Our Journey Implementing Business Intelligence

Our Journey Implementing Business Intelligence | October 21, 2010 | Page #2
Introductions
 Blackbaud Business Intelligence & Performance Management Practice
– Alan Eager, Principal Consultant
 Minnesota Medical Foundation at the University of Minnesota
– Dan Lantz, Application Development Manager,
Information Services
– Margie Zenk, Senior Data Manager, Information
Services

Minnesota Medical Foundation
The Minnesota Medical Foundation (MMF) is a
nonprofit organization that raises millions of dollars
annually to help improve the quality of life for the
people of Minnesota, the nation, and the world by
supporting health-related research, education, and
service at the University of Minnesota. Founded in
1939, the Minnesota Medical Foundation is the oldest
of four foundations recognized by the University of
Minnesota’s board of regents.

Minnesota Medical Foundation History
 Founded in 1939
 Separate 501(c)(3)
 First staff hired in 1959
 Rapid growth in the 1980s
 Raised one-third of the University total during
Campaign Minnesota
 Brought in 3 of the largest gifts in University history:
 $65M gift for cancer research
 $50M gift for U of M Amplatz Children’s Hospital
 $40M gift for diabetes research

You

Terms Business Intelligence (BI)
– Concept of making use of information already available in your company to help decision makers make decisions
better and faster. BI typically includes both an ETL process for pulling and modifying data into a data warehouse
and an OLAP process for providing the warehouse data to users.
 Cube
– A collection of one or more related numeric values (measure groups) and their related data (dimensions). For
example, a cube might contain the split gift amount (measure) with date and gift data (dimensions).
 ETL
– Acronym for Extraction, Transform and Load. Describes the processes within Integration Services which pull and
modify data from the Raiser’s Edge or Financial Edge database and load the data into another database, in our
case a data warehouse.
 OLAP
– Acronym for Online Analytical Processing. Describes the processes performed by tools such as Analysis Services
that provide warehouse data to users usually in the form of a cube.
 Data Warehouse
– Database designed to store data that used for analysis purposes. A data warehouse often integrates data from
different data sources. A transactional database such as RE and FE are often concerned with now; a data
warehouse is concerned with activity over a span of time.
 Denormalization
– Process of storing all of the attributes related to a dimension in a single dimension table. Tables that have been
denormalized are typically referred to as flattened. This results in redundant data but greatly speeds up the
ability to extract data during analysis and reporting.
 SQL Server
– Database server produced by Microsoft. Analysis Services, Integration Services and Reporting Services are
included services in SQL Server.
 The Information Edge (TIE)
– Blackbaud’s proprietary Business Intelligence software. The precursor to the current BI tools.

 Choosing Business Intelligence (BI)
 Executing the plan
 Developing reports

Technical Inventory
 Product knowledge and experience using
Raiser’s Edge (RE)
 Experience with report development
 Product knowledge and experience using The
Information Edge (TIE)

Experience with RE and Reports
Margie Zenk
• Raiser’s Edge
• Campaign and Development Report building
using Crystal and RE
Dan Lantz
• Database Application Development
• Web Development

Development Summary Report

Experience with TIE
The Information Edge (TIE) provided:
 Data warehouse
 Cubes for data analysis by Finance
Department
 Data for Web-based development and
financial reporting and an application to
calculate monthly investment allocation

Why BI Fit Our Goals
 Core could be implemented quickly
 Based on industry standard tools
 BI concepts could be extended to build
other projects
 Increased confidence and flexibility in data
 Power users could quickly generate reports
using pivot tables

Help Needed
 Knowledge about how other organizations
had implemented BI
 Experience using SQL Server tools to
develop a BI-based solution
 Experience building a reporting solution

Plan 1
 Gain application development knowledge
and experience with SQL Server BI tools
 Have Blackbaud install SQL Server BI
packages
 Gain application development experience
with BI by working with Blackbaud
 Develop reports using web-based platform

Plan 1 Revised
 Gain application development knowledge and
experience with SQL Server BI tools
 Have Blackbaud install SQL Server BI packages
 Have Blackbaud take lead in modifying and
enhancing the BI implementation
Gain application development experience with BI by
working with Blackbaud
 Develop reports using web-based platform Microsoft
Excel

First steps
 Took classes in BI
 Created a IS team to tackle the integration
(systems, data, and application development)
 Worked with Blackbaud to remotely install BI
 Met with current customers to get perspective
 Blackbaud consultant came onsite and worked
directly with team

Advancing Academic Medicine
Promoting Health
Cancer $190M
Children’s Health $175M
Diabetes $150M
Heart and Lung $135M
Neurosciences $135M
Scholarships and $100M
Medical Education
Special Initiatives $115M
Priorities Strengths
Technologies
and Innovations
Imaging Science
Transplantation
Drug Discovery
Stem Cell and
Regenerative Medicine
Genomics
Promoting Health

Life Cycle of Report Building
1. Design
2. Data warehouse and cube preparation
3. First draft
4. Reconciliation
5. Finishing touches
6. Launch

Design
 Assemble working group of end users
– Small group: 2 – 6 people
– Experts in the report topic
– Often, currently building reports manually
 Create mock-up of the finished report
– End users can react to look and feel early in the
process
 Data definitions
– What should be excluded?
– How should data be grouped?
– Are we currently capturing this information?

Design Decisions
 Row Definitions
– Use definitions already used by development reports
 Column Definitions
– Corridor
– Solicitation Method
– Constituency
– Fund Use
 Filters
– Date
– Corridor

Data Warehouse and Cube Preparation
 Filters
– Gifts with a particular attribute should not appear on
the report
 Yes/No fields
– Solicitation Method report – each column is defined
by different rules
 Attributes
– Available in the cube, but grouped together. To make
them more usable, de-normalize them onto the
parent dimension

First Draft
 Filters
– Start a pivot report to set up filters
 Main formulas:
– =Cubemember and =Cubeset: define rows, columns, and filters
Example: =CUBEMEMBER("Fundraising OLAP","[Campaign].[Campaign
Identifier].[C]", "Cancer")
– =Cubevalue: totals
Example: =CUBEVALUE("Fundraising OLAP",$C$5, $B$6, $B$7, $B16,
$A$5, $A$9, $D$7, $C$6, $A$6, $A$4, C10)
 Hiding formulas
– Embed formula in the row and column names
– Set up formulas in column A of your spreadsheet, then hide it.

First Draft Sample

Finishing the Report
 Reconciliation
– Check totals against Raiser’s Edge reports and queries
– Pivot reports are useful for drilling into the detail
 Finishing Touches
– Logo, formatting
– To display a 0 instead of an empty cell, use formula
=if(A1="",0,A1)
 Launch
– How will users get to the report?
– How do you prevent accidental changes?

Campaign Report

Campaign Report by Solicitation Method

Campaign Pyramid Report

Major Gift Officer Reports

Major Gift Officer Report Detail

Development Report

Lessons Learned
 Be flexible
 Allow plenty of time
 Build strong teams
 Report progress regularly
 Listen carefully to needs
 Set realistic goals

Questions

Thank you
 Contact Information
– Blackbaud Business Intelligence & Performance
Management Practice
•Alan Eager - alan.eager@blackbaud.com
– Minnesota Medical Foundation at the
University of Minnesota
•Dan Lantz – d.lantz@mmf.umn.edu
•Margie Zenk – m.zenk@mmf.umn.edu

Our Journey Implementing Business Intelligence

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