Information Self-Service

Information Self-Service

Leveraging university data as a strategic asset to securely answer Student, Staff and Faculty questions.

Information Self-Service is about enabling members of the university community to answer their questions. The university is a complicated institution where getting answers is often dependent on institutional knowledge of who, where and how to ask questions. Those without institutional knowledge are then at a disadvantage in getting their questions answered in a timely manner.

Information Self-Service solves this problem by connecting people with questions to the people, data and systems that hold the answers. This will be done in a way that is simple and quick to use much like Google search or virtual agents like Siri, Alexa and Google Assistant.

Creating Information Self-Service

The university’s data will power Information Self-Service. First, the IT Division will create a Data Catalog to answer some foundational questions about the university’s data. Second, the project will develop a university ontology to organize the data and make it human and machine accessible.

Data Catalog - Phase 1 (Completion Date is Scheduled for 9/1/2021)

A Data Catalog will answer these foundational questions about the university’s data:

  1. What data do we have?
  2. Where does the data originate?
  3. Where is the data stored?
  4. Who stewards the data?
  5. Who should have access to the data?

To this end, the IT Division’s Data Architecture team is leading a collaboration with IT departments across the university to create a Data Catalog. This collaboration is focused on using the Global IDs software to collect metadata from databases. The metadata will be analyzed and cataloged to show what data we have, where the data originates, where the data is stored and who stewards the data. This information can be used to understand the data’s lineage, which shows where the data originates and then where it flows and how it is accessed. The Data Catalog, augmented by data stewards, subject matter experts and the IT Security Office, can then help determine who should have access to the data.

University Ontology - Phase 2

Once the foundational questions are answered, the Data Architecture team will focus on the creation of a university ontology. Our Ontology is the key piece of data infrastructure that will help intelligently surface the answers to people’s questions. An ontology defines critical elements and concepts of the university and organizes them based on their relationships to one another. These elements and concepts are then used to align university data and make it interoperable and federated, which will free the data from being siloed. The university can then leverage Artificial Intelligence and Machine Learning to process the data on the ontology and provide insights. These insights are then made accessible via the ontology and systems that tie into it.


RACI matrix for the Information Self-Service project. Responsible means Assigned to complete the task or deliverable, accountable means has final decision-making authority and accountability for completion, consulted means an advisor, stakeholder or subject matter expert who is consulted before a decision or action is taken, and informed means must be informed after a decision or action is taken. The tasks and deliverables are Data Catalog, Ontology, Knowledge Graph and User Interface and User Experience. The Chief Data Architect is accountable for all deliverables. The Data Architecture and DevOps team is responsible for all deliverables. Human Resources is consulted for the Data Catalog and the Knowledge Graph. Enterprise Information Systems is responsible for the Data Catalog. The university community is informed for all deliverables. IT Security Office is consulted for the Data Catalog, Ontology and Knowledge Graph. Data Stewards are responsible for the Data Catalog.


Project Milestones for 2021 are Data Discovery starting June scheduled with completion in September. Data Profiling starting June and continuing through December. Data Quality starting September continuing through December. Data Integration starting October continuing though December. Data Analysis starting November continuing through December.


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