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Our Journey Implementing Business Intelligence

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Our Journey Implementing Business Intelligence Our Journey Implementing Business Intelligence Presentation Transcript

  • Our Journey Implementing Business Intelligence
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #2
  • 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. Our Journey Implementing Business Intelligence | October 21, 2010 | Page #3
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #4
  • You Our Journey Implementing Business Intelligence | October 21, 2010 | Page #5
  •  Business Intelligence (BI) Terms – 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. Our Journey Implementing Business Intelligence | October 21, 2010 | Page #6
  •  Choosing Business Intelligence (BI)  Executing the plan  Developing reports Our Journey Implementing Business Intelligence | October 21, 2010 | Page #7
  • Technical Inventory  Product knowledge and experience using Raiser’s Edge (RE)  Experience with report development  Product knowledge and experience using The Information Edge (TIE) Our Journey Implementing Business Intelligence | October 21, 2010 | Page #8
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #9
  • Development Summary Report Our Journey Implementing Business Intelligence | October 21, 2010 | Page #10
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #11
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #12
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #13
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #14
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #15
  • 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 Our Journey Implementing Business Intelligence | October 21, 2010 | Page #16
  • Advancing Academic Medicine Priorities Strengths Cancer $190M Technologies and Innovations Children’s Health $175M Imaging Science Diabetes $150M Transplantation Heart and Lung $135M Drug Discovery Neurosciences $135M Stem Cell and Regenerative Medicine Scholarships and $100M Genomics Medical Education Special Initiatives $115M Promoting Health Promoting Health Our Journey Implementing Business Intelligence | October 21, 2010 | Page #17
  • Life Cycle of Report Building 1. Design 2. Data warehouse and cube preparation 3. First draft 4. Reconciliation 5. Finishing touches 6. Launch Our Journey Implementing Business Intelligence | October 21, 2010 | Page #18
  • Design  Assemble working group of end users – Small group: 2 – 6 people – Experts in the report topic – Often, currently building reports manually  Create mock-up of the finished report – End users can react to look and feel early in the process  Data definitions – What should be excluded? – How should data be grouped? – Are we currently capturing this information? Our Journey Implementing Business Intelligence | October 21, 2010 | Page #19
  • Design Decisions  Row Definitions – Use definitions already used by development reports  Column Definitions – Corridor – Solicitation Method – Constituency – Fund Use  Filters – Date – Corridor Our Journey Implementing Business Intelligence | October 21, 2010 | Page #20
  • Data Warehouse and Cube Preparation  Filters – Gifts with a particular attribute should not appear on the report  Yes/No fields – Solicitation Method report – each column is defined by different rules  Attributes – Available in the cube, but grouped together. To make them more usable, de-normalize them onto the parent dimension Our Journey Implementing Business Intelligence | October 21, 2010 | Page #21
  • First Draft  Filters – Start a pivot report to set up filters  Main formulas: – =Cubemember and =Cubeset: define rows, columns, and filters Example: =CUBEMEMBER("Fundraising OLAP","[Campaign].[Campaign Identifier].[C]", "Cancer") – =Cubevalue: totals Example: =CUBEVALUE("Fundraising OLAP",$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. Our Journey Implementing Business Intelligence | October 21, 2010 | Page #22
  • First Draft Sample Our Journey Implementing Business Intelligence | October 21, 2010 | Page #23
  • 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? Our Journey Implementing Business Intelligence | October 21, 2010 | Page #24
  • Campaign Report Our Journey Implementing Business Intelligence | October 21, 2010 | Page #25
  • Campaign Report by Solicitation Method Our Journey Implementing Business Intelligence | October 21, 2010 | Page #26
  • Campaign Pyramid Report Our Journey Implementing Business Intelligence | October 21, 2010 | Page #27
  • Major Gift Officer Reports Our Journey Implementing Business Intelligence | October 21, 2010 | Page #28
  • Major Gift Officer Report Detail Our Journey Implementing Business Intelligence | October 21, 2010 | Page #29
  • Development Report Our Journey Implementing Business Intelligence | October 21, 2010 | Page #30
  • Lessons Learned  Be flexible  Allow plenty of time  Build strong teams  Report progress regularly  Listen carefully to needs  Set realistic goals Our Journey Implementing Business Intelligence | October 21, 2010 | Page #31
  • Questions Our Journey Implementing Business Intelligence | October 21, 2010 | Page #32
  • 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 | October 21, 2010 | Page #33