Aggregating disparate data sources at a large university in tableau

Aggregating disparate data sources at a large university

Carnegie Mellon University’s Tepper School of Business is renowned for teaching students a scientific approach to management – how to effectively utilize data in decision making and in solving complex business challenges.

Ted Curran is the Executive Director of Finance of the Tepper School of Business. Ted was in charge with reconstructing the business school’s data evaluation information (a system affecting over two hundred and fifty faculty and staff) putting focus on the quality and not just quantity.  The project would potentially uncover  necessary modifications to the school’s reporting and analytics. The university’s ability to generate and collect data far outpaced its capacity to utilize the data to arrive at favourable outcomes. After considering a number of different solutions, Tepper preferred tableau software.

With an ultimate goal of creating a central data warehouse from multiple source systems, and clarifying data for the end user (the dean of the Tepper School of Business), Ted pursued options within Tableau Software after an extensive review of three different business intelligence solutions. 

Getting started with tableau  

Initially, Tepper wanted to use the data to give the dean a high-level view regarding the school’s operations, including admissions, financial aid, marketing, faculty, and course evaluations. There was also a desired scope to consider undergraduate and master’s programs along with career opportunities and other key decision metrics. The project was time-sensitive, and Tepper management felt that tableau offered the best possibility for a rapid deployment. Ted’s team engaged along with consulting help and developed a specific project plans to deliver an initial set of reports.

Prototype report development

Prototyping reports require detailed sessions with ten different business units to identify goals and define the specific content needed to support those objectives. Data had to be corralled from multiple sources to create the first series of reports. Data architects were brought into developing data models that would  be used for the data warehouse design and to extract, transform, and load logic (ETL) that was developed to automate data collection and storage.

In less than ten days a presentation was made to the business school staff and deans, that outlined the project scope and objectives. Most importantly, over twenty interactive dashboards were presented using actual Tepper data. The presentation was focused not only on what was being currently tracked and reported, but also added new information needed for decision-making. This presentation provided a convincing demonstration of rapid results. Keystake holders (the dean and senior associate deans) were convinced of the efficacy of the plan during this meeting and offered extensive praise for accomplishing the work in such a short span of time.

Leveraging tableau further

Soon after the initial prototype designs were implemented, other faculty, departments and student groups around the campus became aware of the project and began to investigate further on how to improve the quality of their reports using tableau within a less time using tableau.

Tepper’s system team was able to leverage the adhoc data models produced  in tableau to create an extensive data warehouse. The warehouse and accompanying ETL processes aimed to centralize data access that had been isolated in separate systems. This made it easier for non-technical users to combine data sets, track changes, and provide a repository for more extensive visual analysis using tableau.

Tepper’s out come and example dashboards

Ted reports that the payback on the project investment was about twelve months. The school uses tableau to provide information on student outcome and instructor evaluations. Figure12.2 includes scatter plots comparing student grades, course evaluations, and instructor evaluations.

Source: Case study and figure provided by Ted Curran, Tepper School of Management, Carnegie Mellon University.


Figure12.2 Scores of student and instructors

Faculty research and editorial visibility are also accessible via tableau as can be seen in figure 12.3.

Source: Case study and figure contributed by Ted Curran, Tepper School of  Management, Carnegie Mellon University.


Figure12.3 Faculty editorial visibility

This dashboard displays, interactive content from the internet as well, that can be viewed after making selections from the bar chart and crosstab panes.

Financial metrics are also reported visually in dashboards. Figure12.4 shows an example of revenue and expense analysis for academic, administrative, and research centers.

Tepper achieved their goal of centralizing key reports and providing critical information needed to improve-decision-making. The ability to summarize key items in the budget process quickly helps managers make much better decisions. Tableau has also enabled Tepper to maintain sound data governance and provide consumption on personal computers and tablet devices.

The college still continues to work on opportunities to advance its academic mission while increasing revenue and capacity, decreasing costs, and increasing efficiency. Tableau has proven to be a key component in the school’s toolset for bringing together a diverse mix of best of breed ERP, custom and legacy systems-to better understand their data for decision-making.

Source: Case study and figure provided by Ted Curran, Tepper School of Management, Carnegie Mellon University.

When specific areas are selected in the bar chart, the transaction details are displayed as follows.


Figure12.4 Financial performance

In a university setting like CMU’s Tepper School of Business (and a variety of other businesses as well), the priority is to align the business intelligence plan with university protocol, utilize the data that was readily available, and implement Tableau as a decision-support tool. In a time of obsession with “big data”, CMU and Ted Curran recognized that more often than not, it is not only about the quantity of data, but the quality as well. Ted also understood the importance of judgment by managers that utilize business intelligence, putting safeguards in place to avoid rash decisions based on limited information.

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