Our Journey Implementing Business Intelligence

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 FoundationThe 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 $100MMedical Education

Special Initiatives $115M

Priorities Strengths

Technologiesand 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 filtersExample: =CUBEMEMBER("Fundraising OLAP","[Campaign].[Campaign Identifier].[C]", "Cancer")

– =Cubevalue: totalsExample: =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 - [email protected]

– Minnesota Medical Foundation at the University of Minnesota

•Dan Lantz – [email protected]

•Margie Zenk – [email protected]

Technology

Our Journey Implementing Business Intelligence