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Of Forests and Trees...
Transcript of Of Forests and Trees...
and Decision Support Of Forests and Trees: Data Dictionary Identify the informational elements. Christina Drum
Information Architect & Metadata Manager
Office of Institutional Analysis & Planning
University of Nevada, Las Vegas
RMAIR Conference - Albuquerque, NM
October 27, 2011 Context is Everything Large * Public * Urban
~2,900 Faculty & Staff
In Nevada * NSHE Overview New ERP System - Student Information
Reporting/Data Warehousing lagged behind the implementation
Our IR office tasked with the enterprise DW and BI platform Our Situation Our Resources Collective skills and experience IR understanding of data needs and institutional reporting priorities Our Immediate Priorities
Critical reporting needs
Enrollment and Admissions data
Solution for point-in-time snapshots
Tomorrow at 10:15
Mike Ellison Our Understanding Accessible, reliable
data definitions are fundamental to successful information delivery. Decision Support Information Delivery
UNLV Data Warehouse
Campus Collaboration Provide knowledgeable campus users with access to data and information for decision-making Central Data Warehouse
Distributed Reporting Central Data Definitions How? Data Warehousing In the Old World... Transactional
Systems Data Warehouse Information
Delivery Data definitions were organized around a physical data structure. Transactional Context Informational Context Informational Elements and their Implementations... ...as a column in a relational database
...as a dimension in a data model
...as a presentation element in the BI tool
...as on a chart, in a report
...etc. Metadata Data Mart Development Metadata Repository Model Data Governance Data Cookbook
Consensus Christina Drum
http://ir.unlv.edu Resources Reflections Definition Management (About Us > Professional Presentations) Staff of 6 + 2 additional positions "data about data" Institutional Data Definitions Institutional Data Definitions Technical Database Metadata (schemas, tables, columns) Business Process Metadata (operational processes in functional areas) Technical Process Metadata (nightly processing, application feeds) ETL Metadata (data warehousing jobs) BI Platform Metadata (physical, logical modeling, presentation) Reports/Dashboards Metadata (reporting systems, institutional dashboards) Data Stewardship Metadata (people responsible for accuracy, integrity) Lineage Metadata (ancestor/descendent relationships) We had several types of metadata that we eventually wanted to bring together... We knew it would take years to build it all out.
While we started with what was most immediate, we knew that we would want the flexibility to:
1) associate metadata across these different areas
2) connect institutional data definitions with any type of metadata
So, we designed a numbering system that would ensure any element in any of these areas would have a unique ID.
That way, we could build out relationships in the future, by associating one global ID with another.
"data about the containers of data" "data about data relationships" We Built Our Own SQL Server database
Looked at some products We knew what we wanted.
Had the relevant skills and tools.
Would have to learn/customize any purchased option.
What we were doing initially was limited in scope. We started with: Data Definitions (informational elements) Common ID
Definition Status Implementations (of data definitions) Relational Database Table Columns
BI Presentation Elements
ETL Jobs Examples Started with data elements in the old system, and what we knew about requirements for enrollment reporting.
Used a GoogleDocs spreadsheet, initially, for tracking.
Collaborated with staff in Enrollment & Student Services to verify transactional source data and logic. Write and review the defintions. Reviewed methods and standards for writing definitions
Developed some "model" definitions
Established a workflow process and divided up the work Initially assigned to a team of two
Internal review within our office
"Final" data steward review
One person managing the process Metadata Management Application Front-end web application that presents data definitions to users of the BI tool Additional Data Marts... Degrees Conferred
Retention & Graduation
Financial Aid Data Definition/Data Mart Project Phases involves a team comprised of data stewards, data users and IAP development staff 1. List Informational Needs and Elements.
2. Draft Data Definitions.
3. Review and Refine Data Definitions.
4. Implement Data Definitions in the UNLV Data Warehouse.
5. Construct the Data Mart.
6. Review and Refine the Data Mart.
7. Open the Data Mart for Institutional Use.
a structure for strategic collaboration around the institution's informational assets Strategic data elements Transactional data elements from which informational elements are extracted/derived
Communication around business process changes
A next step: Lineage metadata Navigate the trees; understand the forest. Adjust and manage expectations. Become more agile. Make progress before understanding it all. How our IR office leads a collaborative effort to
a new enterprise data
warehouse and BI platform. "metacontent" Definition Methods Genus and Species
Active student - A student who has a Career Program with an "active" status.
Units taken - The number of student credit hours for the enrollment.
Term of Enrollment - A four-digit code indicating the semester and year of
enrollment, as in:
'2108' = Fall 2010, where
2xxx = century (2000)
x10x = year
xxx8 = fall (2/5/8 -> spring/summer/fall)
Race/Ethnicity - IPEDS Reporting - A code associated with one of the
following mutually exclusive IPEDS reporting categories:
1) Nonresident alien
2) Hispanics of any race
3) American Indian or Alaskan Native
5) Black or African American
6) Native Hawaiian or Other Pacific Islander
8) Two or more races
9) Race/ethnicity unknown
Definition Standards Describe essential features concisely, with precision and accuracy
Avoid language that is...
Vague - "The person in the room."
Ambiguous - "The person sitting next to you in this room."
Obscure - "The hominid with the utmost propinquity to you."
Metaphorical - "The life of the party."
Not too broad - "Humans are two-legged animals."
Not too narrow - "Humans are religious animals."
Employ principles of classification
(e.g., consistent, mutually exclusive, jointly exhaustive)
Attend to details; don't stay stuck in minutiae. Make better mistakes tomorrow. HEDW - Higher Education Data Warehousing Forum (http://hedw.org) TDWI - The Data Warehousing Institute (http://tdwi.org) Oregon University System - Student Centralized Administrative Reporting File (SCARF) - (http://www.ous.edu/dept/ir/scarf) Cal Poly - Student Administration Business Rules (http://www.polydata.calpoly.edu/business_rules/SA_BusinessRules.html) Inmon, William, Bonnie O'Neil and Lowell Fryman, _Business Metadata_. Burlington, MA: Elsevier, 2008. Marco, David, _Building and Managing the Meta Data Repository: A Full Lifecycle Guide_. New York, NY: John Wiley & Sons, 2000. Organizations Examples Books