Data is the lifeblood of colleges and universities. Much like the circulatory system that carries our blood to all parts of our bodies, data flows through our institutions, in and out of the hands of the many individuals and departments that collect it and use it. Just as you want your blood to transport the right nutrients and face no blockages, it is critical to ensure data can move efficiently throughout your institution to those who need it, and that it does not fall into the hands of those who don’t.

Taking this analogy one step further, the transfer of data is relatively easy when assisted by a “heart”—an enterprise-wide application such as a constituent relationship management system (CRM). These systems are intended to provide a central location where all departments can input and access critical centrally-stored data, making the data flow merely “data in/data out.” For institutions without such systems, sharing data across departments is more challenging and often less secure. In these cases, departments must follow a series of sometimes manual steps to share data with one another, leaving many without the information they need to most effectively perform their duties.

A recent Eduventures survey of 60 advancement leaders sheds light on the many challenges institutions face when sharing data across departments. It found that few (17%) report having access to an enterprise CRM:

Types of Constituent Management Systems Used by Advancement

Apart from the absence of technology to efficiently and securely enable this process, our survey also identified that many institutions face:

  • A lack of mutual understanding of the meaning of data. Often located within a “data governance committee,” this oversight function establishes and enforces a shared understanding of the data, usually through a data dictionary. Over half (58%) of those leaders surveyed stated that they had no such function at their institutions, and 60% said they did not have data dictionaries.
  • A lack of an efficient and precise data sharing process. Our survey indicated that one or more factors often hinder data sharing. These include missing formal procedures (70%), non-existent data sharing culture (57%), and technological limitations (55%). In fact, only 3% of respondents stated that there were no barriers at all to sharing data.
  • Insecure methods of sharing sensitive data. Lastly, our survey showed the primary method of data sharing carried the risk of exposing sensitive data, with 82% percent of respondents stating that they shared data by emailing flat files.

How to Assess Data Flow Challenges

Overcoming these challenges requires that institutions first assess how they are currently sharing data. The example below depicts a data flow based on the results of our survey, where three institutional departments share data with an advancement team to inform an email fundraising campaign following three steps: data selection, data sharing, and campaign preparation.

At first glance, some steps seem unclear. For example, one might ask whether choosing the appropriate data to share (“select data”) requires extracting data from systems. Also, the exact process of sharing data (“share data”) may need some investigation to ensure that it is following the most efficient way to do so. Likewise, the outcome from sharing data (“prepare campaign”) may seem as if it is simply one step, but it may entail significant activities or business rules.

More critically, however, this example shows that the success of this entire flow depends on two factors: all departments must have a common understanding of the meaning of the required data and must willingly adhere to the steps involved in the data sharing. Without these two factors, departments run the risk of sharing incorrect data or of breaking down the process altogether.

Example of Data Flow

While our survey focused on data sharing within the advancement office, we strongly feel that there are some universal lessons that apply to all departments and all institutions seeking to develop efficient data flows. In addition to developing data management authorities, data dictionaries, and processes for sharing data, we recommend institutions also take the following tactical steps:

  • Develop data flow diagrams: A data flow diagram, like the one pictured above, is a visual representation of the processes, roles, data, and boundaries involved in how data moves within an institution. These diagrams can highlight and help resolve any unclear process steps. To ensure the success of your data flow, you should develop diagrams in conjunction with those involved in the data flow. This has the added benefit of promoting team buy-in for the data flow as a whole.
  • Include data flows in data governance: Data flow diagrams are often associated with the world of system analysis, where they are used in the integration design of different applications and systems. In reality, data flow diagramming can be very effective for data governance. Such a diagram can help identify areas of redundancy across flows, expose unknown data business rules (such as those required for the “Prepare Campaign” step in the example), and help with the ongoing management of data flows across institutions.