Permitted based on data custodians approval, data element usage, and compliance standards.
Uses the information found in the data lake to enhance the end user experience in their applications. Works in conjunction with the Information super users and Functional Subject Matter Experts to provide additional interfaces to the data lake to collect and merge additional information.
Data Delivery Architecture Committee
A collaboration consisting of the Office of Information Technology’s database, developer, data warehouse, and security teams.
Provides oversight to the data access process flow
Serves as a clearing house for data distribution and delivery in a way that eliminates direct connections from interactive applications to the data infrastructure.
Enterprise Data Modeling
Imposes structure on yet-to-be structured data. Architects and optimizes physical storage models, working alongside Functional Subject Matter Experts and Informational/Strategic Reporting authors to understand customers’ business processes and reporting requirements. Deploys structured datasets in a way that meets all customer needs and minimizes resource requirements for retrieval of data.
Enterprise Data Pipeline
<p>A philosophy to guide data infrastructure design decisions that address the strategic goals of the institution. Designed as a response to business requirements expressed at various campus-level discussions about the role of data in the organization.</p>
Enterprise Data Warehouse
Unified data repository that holds historical information in the organization and makes that information consistently accessible across the institution. The data is available for analysis and planning purposes to meet the informational needs of the institution and to support strategic planning and institutional initiatives.
Enterprise Service Modeling
Encompasses access and presentation of information in the Operational Data Lake, as well as the development of defined services that are exposed in the Data Virtualization layer.
Extract, Transform, and Load (ETL)
ETL integrations pull, or extract, entire datasets from Systems of Record and either overwrite or append data to a destination. ETL jobs are sometimes required to transform the physical layout of data as it copies records to their destination.
Functional Subject Matter Experts (SME)
Are experts in the workflow of the data. Sees the overall relationships between data components and how they affect each other. Provides important knowledge about the Systems of Record and are critical in providing input for the application and reporting layers to ensure data quality.
Extracts pertinent information from the data to provide concise, consolidated answers to pertinent questions. Responsible for business intelligence and strategic analytic reports, as well as campus operational reporting.
The final stop in the pipeline. Consumes information/knowledge to merge with other areas to accomplish work or inform decisions.
Information Super User
Amalgamates information from the Operational Data Lake to add additional value to the data. Provides insight into current and future data needs in order to inform Informational/Strategic Reporting.
Collection of tools and processes required to ingest data from the various Systems of Record into the Enterprise Data Pipeline.
Focuses on extraction, transformation, and loading (ETL), as well as real-time replication of data. Creates and maintains data and workflow connectors between the data lake and authorized third-party applications.
Operational Data Lake (ODL)
Used to store information spanning a defined short period of time in order to answer pertinent near real-time questions.
Orchestration is a combination of many different integrations that are executed in series to complete a single integration process.
A collection of data items organized as a set of tables from which data can be accessed or reassembled in many different ways.
Database credentials used for access to service data stores via systems of engagement. (e.g., custom applications, business intelligence & analytics solutions, log aggregators, etc.)
Service Data Stores
Serves multiple purposes including, but not limited to, application usage and data transformations. Service data stores are positioned to strategically use data from the Systems of Record to provide integrated information that enriches the user experience. They also allow for institutional services to merge, transform, and potentially synthesize new value-added information. This new information can be reviewed by Data Governance for promotion into an existing System of Record or creation of a new System of Record.
Usually resides in relational databases and is comprised of clearly defined data types making this data easily searchable.
Data generated by the tracking of the actions users take in the course of navigating an application itself. These data are stored as unstructured data in system logs or in special tables in the applications’ data-stores.
Systems of Engagement
Those tools through which the campus community can access their data. Data being presented has been synthesized into either operational, structured, cross-functional, and non-temporal information, or into strategic, structured, cross-functional, and trans-temporal knowledge (e.g. SAS, Tableu, SPLUNK, OBIEE, etc.).
Systems of Record
Sources of Institutional Data that are delivered by the Enterprise Data Pipeline and utilized by Data Consumers via Systems of Engagement. These systems can be sourced from cloud service providers (e.g., Workday, Salesforce, and Canvas) or internally hosted systems (e.g., PeopleSoft and Blackboard Learn). Data may be a combination of structured, unstructured, user generated, and/or system generated.
Transactional Data Personnel
Charged with creating, maintaining, and validating data elements within the Systems of Record.
Does not follow a specified format, or conform to a predefined data model. Unstructured data files often include text and multimedia content such as email messages, mobile data, website content, audio, and video files. Unstructured data also includes machine-generated data such as scientific, sensor, and satellite imagery data. The term big data is closely associated with unstructured data and refers to extremely large datasets that are difficult to analyze with traditional tools.
Data generated and/or collected by customers and business users outside of business workflow processes. Research data is the most prominent of the user generated data at UNLV. These data can be collected and utilized independent of institutional data, or they can be combined with institutional data to answer a broader range of questions.