Data Quality Case Study

Case 3.2

Business Case: Dirty Data Jeopardize University Fundraising Efforts (included below)

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Case 3.2 - Business Case: Dirty Data Jeopardize University Fundraising Efforts

Founded in 1861, the University of Washington (UW) is a multi-campus public research university located in Seattle, Tacoma, and Bothell, as well as a world-class academic medical center. The university has a total enrollment of more than 47,000 students in 16 colleges and schools. It offers 1,800 undergraduate courses each quarter to more than 32,000 students and annually confers more than 12,000 bachelor's, master's, doctoral, and professional degrees through 140 academic departments.

Beware of Dirty Data

Universities like UW know that fundraising can mean BIG money. At UW, however, dirty data hampered their fundraising efforts and instead of a significant opportunity they had a recipe for disaster: UW collects contact information from multiple sources and this was resulting in copious data errors and duplicate records in their huge donor database that houses the names, addresses, and other relevant information about more than 900,000 student, faculty, staff, alumni, and sports event attendees. They also knew that the contact information in UW's donor records quickly became outdated, especially for students and younger alumni who tend to be transient (relocating to new jobs, marryingchanging names).

Taken all together, the University had a plethora of dirty data that resulted in large volumes of undeliverable mail, wasted postage and excessive production costs, and worst of all a loss in fundraising opportunities. For example, in one direct mail test, UW discovered that almost 10% of its mail was not delivered resulting in thousands of dollars in waste and lost funding. Mike Visaya, Associate Director of Information Management (IM) Strategic Technology Initiatives, lamented that because of the huge amount of data they had to handle UW knew it was important to bring in an expert on data quality.

Improving Data Quality

To improve the quality of its donor database the University worked with Melissa Data (https://www.melissa.com/), a data collection and verification group. Together they implemented an extensive pro-active data quality program to address the inaccurate data issues and ensure the consistency of student and alumni records in their primary donor database. Using Melissa's Data Quality Suite, UW updated the way in which it handled address, phone, and email verification and name parsing in such a way that caused Mike Visaya to proclaim that now our data makes sense. To find and prevent data duplication, UW used Melissa Data's advanced record matching and deduplication solutions.

Melissa Data's geocoding service also helped UW analyze location-specific relationships in its data. For example, UW wanted to know if its football season ticket holders were big contributors to university academic programs and were surprised to discover that they are. Shawn Drew, Director of IM for the Office of Development, was impressed. He thought the database solutions allowed UW to connect the dots, find new relationships and capitalize on the many ways our supporters wanted to contribute.

Identifying New Opportunities

Improvements to data quality also allowed the University the opportunity to go after international donors. Before implementing the solution, the University rarely mailed internationally. Now, with their new technology, UW can easily standardize and collect international contacts to increase their global fundraising efforts.

Thanks to their new data quality solution, UW went from the nightmare of dirty data quality and lost fundraising opportunities, to fulfilling their dream and raised a whopping $2.7 billion in one fundraising effort.

Questions

Answer questions from each of the sections below. Note that you do not need to all of the questions from a section unless instructed to do so.

You must include a section number and question number in your answer or you will not be given credit for the answer.

Use short-answer essay format for your answers. This means you should explain your answer and provide some explanation as to why you think your answer is correct. A short, one or two sentence answer is not adequate.

Question Set 1 - answer 1 question from below

Why was there dirty data in the UW database? Who do you think was responsible for this dirty data? Explain your answer.

What were the consequences to UW of the dirty data? Could UW have continued using the dirty data? Explain your answer.

The University of Washington is a not-for-profit. Are the problems they experienced with a donor database analogous to problems a for-profit might have with a particular data set? Identify a similar data set in a for-profit organization and explain how dirty data with that data set might impact their operation..

Question Set 2 answer all questions

How did UW address the problem of dirty data?

What were the benefits they experienced from improving the quality of their donor database?

What new opportunity was identified by the new system?