1. Data Management Concepts and Data Profiling
WORKSHOP
Data Management Concepts and Data Profiling

2. Getting in touch with the STUDENT EXPERIENCE

3. Workshop Agenda Analytics vs. BI Why Data Quality?
1:30
Why Data Quality?
:15
Data Validation – When and Why?
:30
Data Profiling Concepts – Why and How?
1:00
Data Governance Best Practices
Golden Source vs. Master Data
Business Intelligence, BPM, & Analytics
Data Integration, Enterprise Data Warehouse
Data Governance
Master Data Management

4. UF Data Management Pillars
Data Governance
Data Architecture
Master Data Management
Data Warehouse/ Data Integration
Business Intelligence / BPM / Analytics
Data Quality
Meta Data Management
SOA / ESB / API Management

5. Who Needs What Information?
Bursar’s Office
Deans & Dept Heads
Office of Student Financial Affairs
Office of the University Registrar
Office of Research
Revenue & Expense Analysis
Tuition & Financial Aid indicators
Capital Projects Indicators
Enrollment, Instructor, & Course Offering metrics
Faculty hiring and retention metrics
Research Analytics
Student Progress Reporting
Instructor Course Workload
Program Effectiveness metrics
Graduation Rates & Time-to-Degree metrics
Student Holds
Dean’s List
Board of Governors Compliance Reports
Disbursement Reports
Board of Trustees Reports
Fiscal Operations Reports & Indicators
External Agency Reporting
Ad Hoc Reports
Institutional Reporting
Student Holds
Student Fees & Payments
Financial Operations Reports
Financial Operations Metric & indicators
Loan Reconciliation Reports
Cash Account Reporting
Grant Reconciliation reports
Student Holds
Student Enrollment
Registration metrics
Students with Repeat Courses
Board of Trustees Compliance Reports
Federal & State Compliance Reports
Official Student Metrics
Withdrawals – alerts, monitoring
Student Holds
Official Institutional metrics reporting
Grant Funding
Grants Management – alerts & monitoring
Grants Lifecycle Dashboard
Ad Hoc Reporting
Common Data Sets Reporting
Academic Leadership
Fiscal Officers
Dept Admins
Managers
Principal Investigators
Grant Administrators

6. Where is My Information?
Finance HR
Payroll
Research
Accounting
Budgeting
Purchasing
Admissions
Financial Aid
Student Records
Scheduling
Registration
Accounts Receivable
Housing
Institutional Research, Health Services, Sponsored Research, Enterprise Infrastructure & Operations, Environmental Health & Safety, University Relations
Student Portal
UF Online
Registration

7. What Do We Need to Know? Profile of Students most likely to succeed
Faculty Productivity: Research, Publications, Teaching, Advising, etc.
Affordability and Financial Aid: socioeconomic profile of our student body
Early detection of students struggling academically => early intervention; Advising
Cost/Benefits Analysis and Assessment of Academic Programs
Per Student Cost to attract, retain, and graduate
Supply and Demand of course offerings
Which Alumni are likely to make large donations
Research: Proposal success rates, growing areas, funding opportunities, Cost Sharing and Indirect Cost projections/commitments
Libraries: Subscriptions Cost/Benefits and Allocation models
Cost Reduction-Financial and Operational indicators: identifying inefficiencies and duplications, non- value added functions
Space utilization and optimization
Adapted from "Practical Approach to Implementing Business Intelligence in Higher Education," Ora Fish, Executive Director, Program Services Office, New York University
Financial Aid Revenues

8. UF Information Factory
Data is an institution asset and must be managed accordingly
BI & Analytics
Data is a valuable institution resource; it has real, measurable value. Data is used to keep accurate records of operations and aid in operational and strategic decision-making. Accurate, timely data is critical to accurate, timely decisions. Most institution assets are carefully managed, and data is no exception.
Discovery Analysis
Data
Governance
UF Information Factory
Master Data Management
Data Integration (warehouse)
Data Quality
Data Profiling

9. Building Blocks for Data Management
Business Intelligence
Analytics
Business Performance
Building Blocks to achieving a HOLISTIC and INTEGRATED view of the Student
Each Building Block is required to achieve TRUSTED and QUALITY information
SPONSORSHIP
FACULTY
STUDENT
SCHOOL
Data Integration
Master Data Management
Best Practices : Set standards and approach
Choose right tools
Build a customer-centric, Enterprise Data Warehouse
Data Quality Framework
Data Governance

10. UF Data Principles – The Foundation
Aspirational Description
Primacy of Principles
These principles apply to all faculty, staff, functions, data, and processes within the institution
Executive Mandate
Institution is mandated to adopt a data culture and motivate people to apply data principles
Our Data is an Asset
Data is an institution asset and must be managed accordingly
Our Data is Shared
Data is shared across business units in order to avoid duplication and enable reuse
Our Data is Accessible
Enterprise data assets are available to business users when and where they need it within predefined business constraints
Our Data is a Manufactured Product
Data is a product made from the large-scale operation of business processes
Our Data is Governed
Enterprise data assets require specific ownership, stewardship, governance and quality controls
Our Data is Meaningful
Enterprise data is defined consistently using a common vocabulary that is managed and accessible across the institution
Our Data Quality Must Continuously
Improve
Critical data elements are continuously identified, controlled and monitored according to a selected set of quality dimensions
Data is Protected
Sensitive data is identified and protected according to firm policies, and monitored and controlled for compliance to the policies
Our Data Architecture is Change Enabled
Our data architecture framework is sufficiently flexible to meet current and future business requirements
Our Data has a Lifecycle
The institution creates, reads, updates, archives, deletes, and purges data throughout the information lifecycle guided by industry best practices and regulatory requirements
Our Data Must be Harmonized
The institution guards against distrust, loss of value, and misuse due to misinterpretation and ambiguous meaning
Our Data Must be Authoritatively Sourced
Data is consumed from the most appropriate, governed sources
Data Management Education is Essential
As a valuable asset, the institution as a whole has ongoing education and training to manage data in a way that optimizes the value of the asset
9/16/2018

11. Business Intelligence
Analytics vs. BI
1:30
Business Intelligence, BPM, & Analytics
Business Intelligence

12. Business Intelligence Overview
Interactive, discovery, investigative analytic approach that recognizes the cycle of analysis: each answer brings new questions
Question
Scorecards & KPIs
Analytics & Reporting
Operational Reporting
Summary Data
Official Revenue
Official Student Counts
Performance Dashboards & Alerts
Answer
Question
KPI Drill Down
Slice and Dice
Discovery Analysis
Algorithms, trends, predictions
Answer
Drill-to Detail
Operational Alerts & Dashboards
Operational Integration
Structured, consistent views
Question
Answer
UFIT Enterprise Data Warehouse
Atomic Data

13. BI / Analytics Capabilities Maturity Model
Analytic Technique
Critical Business Question
Optimization Modeling
How can we achieve the best outcome?
Predictive Modeling
What will happen next if ?
Forecasting
What if these trends continue?
Simulation
What could happen .. ?
Statistical Analysis
Why is this happening?
Scorecards / KPI
Did we meet our goals?
Dashboarding (Alerts)
What actions are needed?
Discovery Analysis (Query / Drill-down )
What exactly is the problem?
Ad-Hoc Reporting
How many? How often? Where?
Standard Reporting
What happened?
Analytics
Predictive and Prescriptive
Support new business models and opportunities
Competitive Advantage
Business Intelligence
Discovery and Diagnostics
Support ongoing business operations
Data Cleansing and Integration (Data Warehouse and MDM)

14. Example Dashboard for Higher Education
KPI’s
Score
Card

15. Recruiting Metrics – Example Dashboard

16. What Can BI Do for Higher Education?
"Imagine leveraging the historical data already stored in your data repositories to determine which students are most likely to drop out or not pay their tuition bill on time, which are likely to switch majors or become alumni who generously give back to the campus, or predict where crimes may occur on campus, thus allowing you to staff campus security accordingly.”
-- Scott Cupach, Senior Consultant, Sungard Higher Education
"Our philosophy with business intelligence is to enable as much transaction-system content as possible to our end users and to empower departments to do a lot of reporting and dash boarding on their own through training”
- Michael Barrett, CIO, Florida State
Three core principles for successful BI in Higher Education:
“ Start simple and evolve, minimize variables, and link insight to action to provide continuous institutional effectiveness.”
- Selim Burduroglu, Oracle Education & Research
"The BI tool helped us use data and trend analysis to prepare for larger classes and increased enrollment. We also were able to educate stakeholders, like academic affairs, so they could hire to prepare for increased enrollment.”
- Denise Groves, Dean of Enrollment, Tarleton State University, Texas 1 2 3

17. Master Data Management
Golden Source vs. Master Data
1:00
Master Data Management

18. Benefits of Data Stewardship
MDM Roles and Benefits
Data Trustee Typically core office(s); oversees all business function(s), DDD level, business-driven. Commonly responsible for data content, context, and associated business rules.
Sponsor: Champions the MDM program and promotes business and cross-functional participation. The Sponsor empowers the Data Governance Council and the Data Steward; leads local governance and participates on enterprise wide data governance.
Data Steward Typically core office; day-to-day functions; adding / editing relevant data, data quality assurance, specific business function(s).
Data Custodians Typically IT-focused; can be institutional or local; responsible for the safe custody, transport, storage of the data and implementation of business rules.
Data Users / Recipients The person, under direction of their unit VP or Dean, requesting and who will make use of transferred data, typically for reporting or analysis
Benefits of Data Stewardship
Consistent use of data management resource
Easy mapping of data between computer systems and exchange documents
It has been found that people are more likely to trust and use a system where there is a person they can call with question on each data element

19. What is Master Data? Places People Things
Data that describes the KEY “people, places, things” (nouns) of the University that need to be managed
Data about the people, places, things that will participate in events
Provides contextual data about events and transactions
People
Student
Vendor
Faculty PI
Places
address
building
classroom
Sponsor
Things
Catalog
Degree
Course
Credit
codes
Reference Data

20. What is Transaction Data?
AKA Event data
Describes an action (a verb):
e.g. “buy”
May include measurements about the action:
Quantity bought
Amount Paid
Includes information identifying the nouns that were involved in the event (the Who, What, Where, When, How and maybe Why):
Anna Adams went to the campus bookstore on Friday, December 11, 2015, and bought a book for her Math 101 class. Anna paid $100 cash for the book.
Anna Adams
Math 101 book
Campus Bookstore
Friday, December 11, 2015
Cash
Does not include information describing the nouns:
Anna is female and works for University of Florida IT
Friday, December 11th is a school holiday
The address for the campus bookstore is 232 Stadium, Gainesville, FL

21. What is Master Data Management?
The processes, systems, and procedures used to manage the relationships between master and reference data PI
People
Things
Reference Data
Catalog
Student
Places
Vendor
Faculty
Sponsor
Degree
Course
Credit
address
building
classroom
codes
codes

22. UF MDM HUB - Capabilities
Reference
Catalog
Person
3rd Party Access
Data Query
Workflow
Reporting & Analysis
Data
Governance
Management
Performance & Scalability
Matching &
Merging
Real-Time
Data
Integration
Hierarchy
Management
Enterprise
Data Model

23. Business Objectives
Establish a single version of the truth for data over time
Eliminate unnecessary data duplication and proliferation
Take ownership, responsibility and accountability for the improvements of University information quality, accuracy and consistency
Implement a flexible and scalable data integration framework of high quality and one that supports agile development

24. Key Drivers for Moving to a Student MDM Hub
Do you have Master Data Management best practices implemented in your OSS?
Does each operational domain (School, College) define it’s definition of Student based upon it’s own operational needs, resulting in multiple student definitions across the enterprise?
Does each of your OSS systems pass a single student definition/ID across all systems?
Are your student IDs linked to the operational process or the actual student?
Do you have a single view or 360 of the student for student services or analytics?
If not then you might …
Not know when the same student enrolls at two different campuses or for credit and non- credit courses
Not know when the student has moved off-campus or moved to another dorm
Send multiple invoices to the same student or incorrect billing information
Send duplicate information to the same student
Do you want to send multiple pieces of mail to the same customer
Reduce costs for postal requirements – Accuracy of customer information is critical to effective marketing

25. MDM Benefits Improved Recruiting Effectiveness
More efficient Enrollment and Registration process
Improved tracking of prospects
Improved Student Retention
Know past interaction of student with University
Offer programs, courses, and scheduling based on student demographics and churn patterns
Scalability
Integration of disparate information
Prospect management, admission applications, transcript evaluation, registration, academic history, student holds, fees, financial aid, contact management
Easily support mergers, multi-campus locations, target marketing, academic structure changes
Supports CRM and BI
Integrate with University’s MDM architecture
Master service/product
Facilitate 3600 view of student by having one version of the truth DW BI
MDM
ODS

26. UF Data Management Reference Architecture
Transactional Layer
Integration Layer
Distribution Layer
UF Standard User
Enterprise Systems
Enterprise Bus. Intelligence
Click Commerce
D I R E C T
BI Platform
Meta Data _
Dashboards
UF Advanced User
People Soft
Reporting
Scorecards
College / 3rd Party E S B
Canvas
ETL
ODS
Enterprise Data Warehouse
Developer / Advanced Tools DQ
Visualization
Research Computing
Analytics
UF Developer
A P I
Big Data
SQL / Direct
Analytics
MDM Data Steward
MDM HUB
API
MDM Work Flow
Data Architecture

27. MDM | Seven Building Blocks of Success
Master Data Management
Data Migration and Integration. ETL tools for Person data loads, distribute, replicate and monitor
Data quality processes for MDM practices. Profile, Analyze, Cleanse and monitor.
Metadata for business and technical documentation
Different Person Views catering to Enterprise Wide departments, colleges, and affiliates
Enable functionality to create and manage Person Hierarchies at all levels
Data Governance and Stewardship. Business Process for data stewardship over hierarchies and user-managed data.
MDM Vision
MDM Strategy
MDM Metrics
MDM Governance | MDM Organization
MDM Processes
MDM Technology Infrastructure
Gartner

28. MDM Maturity Model Maturity Stages Level of MDM Maturity Non-existent
MDM is the way we do things around here. Managing master data as an asset.
Continuing to learn and improve.
A unified vision emerges with high-level sponsorship. Enterprise-wide MDM program.
Level of MDM Maturity
No vision. Firefighting is the answer. Isolated, bottom-up initiatives.
Okay, let’s do something at the silo level. Silo-oriented solutions.
No vision;
But, “yes, we do have a problem”
Problem?
What
Non-existent 1
Initial 2
Developing 3
Defined 4
Managed 5
Optimizing
Maturity Stages
Gartner

29. The MDM Process Life Cycle
Author
Store
Pub/ Synch
Enrich
Consume
Archive
Collaborate
Enrich
Enrich
Enrich
Enrich
Enrich
E2E
Marketing
Procurement
Operations
Logistics
Sales
Service
Example Life Cycle for Product Master Data
Questions to Answer:
What processes will we need to ensure the creation, management, publishing and leveraging of high-quality master data across our organization?
What business processes will the master data life cycle processes support?
Gartner

30. The Basis of Achieving Buy-In and Creating the Metrics
Organizational Structures, Roles, and Responsibilities
Executive-Level Sponsor
Information Governance Board
MDM Team
Centralized or Distributed
MDM Infrastructure Team
Security Privacy
System Mgmt.
App.
Dev. Integ.
Modeling / Metadata
Info. / Architect
Data Quality
Monitoring / Reporting
Data Steward
Business IT
Questions to Answer:
Who creates and consumes master data? What are their roles?
Do we have data stewardship roles and is it seen as a business responsibility?
What organizational structure do we need to manage master data?
How will we manage the change that comes with new ways of working?
Gartner

31. Data Integration, Enterprise Data Warehouse
Why Data Quality?
:15
Data Validation – When and Why?
:30
Data Profiling Concepts – Why and How?
1:00
Data Integration

32. Pop Quiz
Putting all of your data in a single place (platform) mean your data is integrated.
True
False

33. Data Integration Framework
Dimensional modeling (DM) is the name of a logical design technique often used for data warehouses.
Dimensional modeling consists of conformed dimensions and fact tables.
A conformed dimension is defined and implemented one time, so that it means the same thing everywhere it's used.
Fact tables that should be conformed include those that derive expenses, enrollment, courses, prices, and adds / drops.
CONFORMED DIMENSIONS

34. Conformed Dimensions Methodology
Achieve Data Integration through Conformed Dimensions and Facts
Conformed Dimension Management is based upon best practices of Master Data Management and Data Warehouse Bus Architecture by providing a framework for the warehouses to grow and integrate through a common set of conformed dimensions and defined conformed fact entities and metric definitions across the enterprise
Also provides advanced update management and metadata management features ensuring timely content management and control over strategic hierarchies for mission critical business processes
CDM is based on the Data Warehouse BUS Architecture described below
A BUS is a common structure to which everything connects and from which everything derives power. By defining a standard bus interface for the data warehouse environment, separate data marts can be implemented and can be plugged together and usefully coexist if they adhere to the standard.
DATA WAREHOUSE BUS ARCITECHURE
Drops/Adds
Credit Hours
Student Counts
Term
College
Program
Campus
Instructor
Course
Location

35. UF Data Management Reference Architecture
Transactional Layer
Integration Layer
Distribution Layer
UF Standard User
Enterprise Systems
Enterprise Bus. Intelligence
Click Commerce
D I R E C T
BI Platform
Meta Data _
Dashboards
UF Advanced User
People Soft
Reporting
Scorecards
College / 3rd Party E S B
Canvas
ETL
ODS
Enterprise Data Warehouse
Developer / Advanced Tools DQ
Visualization
Research Computing
Analytics
UF Developer
A P I
Big Data
SQL / Direct
Analytics
MDM Data Steward
MDM HUB
API
MDM Work Flow
Data Architecture

36. Dimensional Modeling Reference Architecture
Staging
Conformed
Dimensions
Facts
Person ID
College
Course
Num
Program
Dept

37. Data Model Creation Process
1. Conceptual Model
2. Logical Model
3. Physical Model
Indexes, Partitions, Optimization
Student
Student ID
Name Type Status ….
Student
Student
Student ID
Name Type Status ….
Schedule
Date Course ID
Term ID
Student ID …
Course
Schedule
Schedule
Date Course ID
Term ID
Student ID …
Term
Catalog
Governance Council Approval
Data Steward Group Approval
Architecture Group Approval

38. Business Process Flows
Student Lifecycles
Retained
Financial Aid
Business Process Flows
Advisement
Recruitment
Registration
Degree-Seeking
Student
Admitted
Alumni
Bursar
Admission
Instruction
Returns
Next Term
Returning
Left (not retained)
Registration
Student Life
Non-Degree Seeking
Student
Admission
Bursar
Finished
Graduated
Returns Later
Instruction

39. Bursar Past Due Payments Student Disbursements Bursar Payments
Account Status
From
Past Due Payments
Collects
Full Name
Student To
Disbursements
Bursar
Distributes
Payments
Makes
Permanent Address/Contact Info
Program Indicators
Tuition Status
Account Status

40. Financial Aid Need/Eligibility Financial Aid Officer Student FAFSA
Need Based Eligibility
determines
Need/Eligibility
Financial Aid Officer
Student
FAFSA
Submits
Entered/
Validated by
Award Letter
sends
Application
UFID
Emergency Contact
Full Name
Maiden/Former Name
SSN
Gender
Permanent Address/Contact Info
Date of Birth
Ethnic Origin
Race Indicators
UF Address/Contact Info
Approved by
Academic Department
Permanent Address/Contact Info

41. RAPID ACCESS INSTANT Student View Student 360
Consolidate students across colleges, departments, degree programs, affiliates
Student View
ACCESS
RAPID
Identify students in the same household
Identify students with multiple billing accounts or mode of payment
Allow attribute-sharing across sources for consolidated students
MDM:CDI Party Concept - Party Identifier allows studens, faculty, staff, etc. to be identified as one party having multiple roles
Student
360
Holds
Interests
INSTANT
Degree Programs
Extracurricular

42. MDM Data Model – Party Concept (Student)
Consolidate students across campuses, programs, colleges, etc.
Identify students in the same household
Identify students with multiple billing accounts or mode of payment
Allow attribute sharing across sources for consolidated student
SSN might be in source 1 only but after consolidation shared with all sources
Party Identifier allows students, faculty, staff, etc. (roles) to be identified as one party having multiple roles
Students within the same household can be identified with one party id.
Students with multiple billing accounts can be identified with one party id.

43. Student 360 Overview Diagram
Semester Flow
Degree Program
Course Registration
Enroll
Register
Session Begins
Attend Class
Mid-Term Grade
Final Grade
Session Ends
Drop
Add
Integrated
Person
Hub
Course Schedule
Course Credit
Location
Time
Instructor
Revenue Metrics
Registration Fees
Course Fees
Credit Hours
Student Fees
Financial Aid
Rich Data
Enrollment Date
Demographics
Credit Hours
Revenue
Propensity to Churn
Firmagraphics
Scores
Profitability
Class Location
Student Holds
Campus
Building
Classroom
Type of Hold

44. Data Integration, Enterprise Data Warehouse
Why Data Quality?
:15
Why Data Quality?

45. Pop Quiz Does data quality matter more in your : Master Data
Data Warehouse
Reporting Tools

46. ALL OF THE ABOVE ..and the Answer is….
Does data quality matter more in your :
Master Data
Data Warehouse
Reporting Tools
ALL OF THE ABOVE
Data Quality Matters

47. One is its inherent quality, and the other is its pragmatic quality.
What is Data Quality?
There are two significant definitions of information quality.
One is its inherent quality, and the other is its pragmatic quality.
Inherent information quality is the correctness or accuracy of data.
Pragmatic information quality is the value that accurate data has in supporting the work of the enterprise. Data that does not help enable the enterprise to accomplish its mission has no quality, no matter how accurate it is.
“Experience is revealing that more than half of data warehouses built fail to meet expectations because of poor information quality.”
-Improving Data Warehouse and Business Information Quality,

48. Data Quality Framework
Process Quality
Information Quality
Systems Monitoring
Performance Monitoring
Mainframe Audit
File Management
ETL Audit
Load Audit
Data Mart Audit
Reporting Audit
Data Procurement Analysis
Feed Validation
Data Integration Methodology
ETL Data Validation
Data Certification
Quality Reporting
Conditional Reporting
Source System Loop Back
Multi-Source System Validation
Work Flow
Subject Matter Expertise
Architecture & Integration
Issue Tracking

49. Data Element Maturity Levels
Best of Breed Data Asset
Better Data Asset
Quality Data Asset
Data Asset
Understood Data
Identified Data
Confidence and Benchmarking
Critical
Data
Elements
Consistent Improvement and Usage
Achieving Quality Expectations
Policy and Rules Defined
Owned, Modeled and Defined

50. Data Profiling 101 Basic Column-Level Analysis
Distinct count and percent
Zero, blank, and NULL
percent
Minimum, maximum, and average string length
Numerical and date range analysis
Key integrity
Cardinality (e.g. one-to-one, one-to-many, many-to-many, etc.)
Pattern, frequency distributions, and domain analysis (e.g.
Data Profiling
Profiling normally examines areas such as data values, value ranges, frequency distributions, metadata mismatches, various statistics, non-standard record formats, etc

51. Data Profiling Worksheet
Column Name
# of Records
Inferred Data Type
Distinct Count
Null Count
% Null
Maximum
Minimum
# of Patterns
Mean
Median
Standard Deviation
Column Profiling
Frequent values, outliers, maximum, minimum, nulls, patterns, overloaded use
Table and Cross-Table Profiling
Dependencies, candidate primary keys, candidate foreign keys, cardinality of relationships, referential integrity
Additional Analysis
Mean, median, standard deviations, uniqueness, ranges, reasonableness

52. Importance of Advanced Data Quality (Matching Algorithms)
Better data hygiene drives better data matching
Better matching drives better student identification and modeling
Better identification and modeling drives better student interactions
Better interactions and campaigns drive higher retention rates
Higher retention rates drive more revenues.
Not doing MDM may be more expensive than doing it!

53. Sample Student Data - Today
*Fictitious Student Information
Cust. Id
First Name
William
Middle
James
Last Name
Sosulski
DOB
April 12
Phone
Address
123 Oak St., Eves, IL 30319
Recruiting
Registrar
Library
Campus Security
Financial
Aid
Cust. Id
First Name
William
Middle J.
Last Name
Sosulski
DOB
Phone
Address
123 Oak St., Eves, IL
Cust. Id
14239
First Name
Bubba
Middle J.
Last Name
DOB
April 12
USER #
vz1234
Address
Cust. Id
3721B
First Name
Willaim
Last Name
Corp
Userid
vz1234
DOB
04/12/1939
Account
Address
3224 Pkwy G, Los Osos
Cust. Id
First Name
William
Middle
James
Last Name
Sosulski
DOB
04/12/1939
Account
Address
123 Oak St., Eves, IL 30319

54. After Matching – Master Person View
Cust. Id
First Name
William
Middle
James
Last Name
Sosulski
DOB
April 12
Phone
Address
123 Oak St., Eves, IL 30319
Cust. Id
First Name
William
Middle J.
Last Name
Sosulski
DOB
Phone
Address
123 Oak St., Eves, IL
Cust. Id
14239
First Name
Bubba
Middle J.
Last Name
DOB
April 12
USER #
vz1234
Address
Cust. Id
3721B
First Name
Willaim
Last Name
Corp
Userid
vz1234
DOB
04/12/1939
Account
Address
3224 Pkwy G, Los Osos
Cust. Id
First Name
William
Middle
James
Last Name
Sosulski
DOB
04/12/1939
Account
Address
123 Oak St., Eves, IL 30319
Person Hub
1001
William
James
Sosulski
04/12/1939
123 Oak Street
Eves CA
91403
*Fictitious Student Information
Recruiting
Registrar
Library
Campus Security
Financial Aid

55. Data Governance Best Practices
:30
Data Governance
Data Governance

56. Data Governance Operating Model Overview
Data Trustees
Data Trustees & Sponsors
Typically core office(s); oversees all business function(s), DDD level, business-driven. Commonly responsible for data content, context, and associated business rules.
Standards & Procedures
Common communication and process mechanisms used to guide efforts and decisions
Work Groups (by Focus)
Data Governance Council
Data Governance Council
Establishes and manages governance team structures
Potential issue escalation path
Student Data Governance Council
Data Custodians (IT)
Data Stewards
Data Custodians (adhoc)
Typically IT-focused; can be institutional or local; responsible for the safe custody, transport, storage of the data and implementation of business rules.
Data Stewards
Typically core office; day-to-day functions; adding / editing relevant data, data quality assurance, specific business function(s).

57. Data Governance Council
The Data Governance Council is comprised of key stakeholders across the University who play an active role in the development and management of research and sponsored project information.
Establishes overall policy and guidelines for the development of standards, definitions, classification and use of EM’s master data;
Charters Working Groups to review and document definitions, hierarchies, taxonomies, data standards, business rules, sources of truth, and other meta-data associated with master data under the stewardship of EM.
Coordinates EM’s interest in the definition, standardization and classification of enterprise-wide master.
Monitors quality and accountability to standards.
Student Data Governance and Master Data Management Charter, page 6

58. Data Ownership vs. Data Stewardship
Data stewards take care of the data
Data stewards know the content
Stewards are responsible for the quality
Operate on behalf of the organization
A Business function, not IT
The farmers take care of the land
Nobody owns the data or its use
The owner is the organization
It is an abstract concept
The King/Queen owns the land

59. Data Stewardship Roles and Benefits
Data Steward generically refers to the four types of data stewardship committee roles:
Executive Sponsor Any initiative that cuts across a company's lines-of-business must have executive management support onboard.
Chief Steward
Responsible for the day-to-day organization and management of the data stewardship committee.
Business Steward
Responsible for defining the procedures, policies, data meanings and requirements of the enterprise.
Data Steward (Technical)
A technical person that is a member of the organization's IT department.
Benefits of Data Stewardship
Consistent use of data management resource
Easy mapping of data between computer systems and exchange documents
It has been found that people are more likely to trust and use a system where there is a person they can call with question on each data element

60. Data Stewardship Responsibilities
A data steward ensures that each assigned data element:
Has clear and unambiguous data element definition.
Does not conflict with other data elements in the metadata registry (removes duplicates, overlap etc.)
Has clear enumerated value definitions if it is of type Code.
Is still being used (remove unused data elements)
Is being used consistently in various computer systems
Has adequate documentation on appropriate usage and notes
Documents the origin and sources of authority on each metadata element
Data Stewardship Framework by David D. Marco

61. Building the Winning Team
Work Groups
The Work Groups will be accountable for defining standards and imparting data-centric knowledge, business representation, and data quality.
Work Groups may consist of Data Stewards who will be responsible for communicating and imparting the decisions made on data domains data quality and data usage.
The Work Groups will make recommendations on data changes and will bring the recommendations to the DGC for review, approval and execution.
Building the Winning Team