breaking silos and building a university data community - sponsored by skillsurvey (263245761)
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8/9/2019 Breaking Silos and Building a University Data Community - Sponsored by SkillSurvey (263245761)
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Breaking Silos And Building a University
Data Community
Craig Rudick Malar Perumalraja Associate Director of IR Database Administrator
University of Kentucky Lynn University
Craig.rudick@uky.edu mperumalraja@lynn.edu
This presentation leaves copyright of the content to the presenter. Unless otherwise noted in the materials, uploaded content
carries the Creative Commons Attribution-NonCommercial-ShareAlike license, which grants usage to the general public
with the stipulated criteria.
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Why is Data Important?
Because SCIENCE!• It’s how we know stuff
If we drop two students froma tower, which onegraduates first?
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Image source:www.dishful-thinki
ng.comReference: https://www.ted.com/talks/kenneth_cukier_big_data_is_better_data
Why is Data Important?
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source: http://activerain.trulia.com/blogsview/4509000/pumpkinfest-october-13--2014
Why is Data Important?
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Why is Data Important?
MORE DATAMORE
ACCURATEANALYSES
MORE CONFIDENTDECISIONMAKING
BETTER
DECISION MEANS* Student Success
* Greater Operational
Efficiency
* Reduced Risk
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Q & A
How is lack of access to data hamperingdecision-making on your campus?
• Is there a single piece of data you most wishyou had?
• Are institutional siloes preventing data access?
How do you empower people to make betterdata-informed decisions?
• Do you have the tools and skills required tounderstand and analyze data?
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Challenges
source: http://www.making-sense.org/page.php?page=1
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Lynn University Challenge = DataChaos!!!
Organizational Silos
Lack of Communication and Cooperation
Multiple and Incompatible Data Systems Conflicting Processes
Conflicting Definitions
Conflicting Numbers
Duplication of Efforts
Source: http://www.ektron.com/Blogs/Chris-Denaro/Why-Silos-Are-Bad-In-Higher-Education/
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Organizational ChartIT Department
Office ofCIO
InformationSystems
CardSystems
Networkand Telecom
SupportServices
InstitutionalResearch
Library
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IT Challenges
Lack of Coordination among IT Units
Growth in systems and services (Service
Catalog) Audits
Informal Governance
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Data Challenges
Consistency and Reproducibility
• IR vs. BI + siloed systems
• Poor data archival
• Never the Same Business Rule Twice
I need data. Who do I ask?
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Analysis Capabilities
Data is not information
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Analysis Capabilities
The Underpants Gnomes Theory of Big Data
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Analysis Capabilities
The Underpants Gnomes Theory of Big Data
Underpants=
Data
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Group Activity #1: Voting
Identify and explore thechallenges at your Institution1. Each table divides into pairs
2. Vote for similar challenges
3. Write any other challenges on a sticky noteand place it under “other” column on theposter board.
4. Discussion
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Source: http://www.bluleadz.com/blog/the-top-5-email-marketing-challenges-and-how-to-overcome-them
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Lynn UniversityIT Governance
IT UNITCommittee
Office of theCIO
Networking
Telecom
HelpdeskLibrary
InformationSystems
Card Office
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IT Governance
Project Management
Hurricane Plan
Identity and AccessManagement
Change Management
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Change Management
Implemented a process
Established a workflow
Set overall Goal
Controls + Communication + Collaboration =
Coordination
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Change Management Workflow withTimelines
Submits RFC to
CMT by noonon Monday
The RFC sent torequestorsDirector for
Approval
Director
Approved?
CMT
ReviewsRFC
ChangeImplemented
YES
NO
CMT
Approved?
NO
YES
Requesterwill benotified
If denied sent back to
requester
If denied sent back to
requester CIOOverride
? NO
YES
Prior to Mondayat Noon
Tuesday
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CMT Video
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CM: Lessons Learned
Lots of work and dedication
Creating and Implementing the process involvedcosts
- 6 months for 8 FTEs, meeting 2-3 hours a week
- Primary for CMT 7 hours a week in preparation of requests
- CMT 2 hours a week to review and approve requests
Purpose – Reporting or Informational
Start small Change is Hard
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Data Governance
source: http://www.kid.co.za/kid_mdm-serv-gov.htmlEDUCAUSE CONNECT San Antonio 2015
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Source: https://www.pinterest.com/pin/310607705519762660/
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Data Governance Committee
The Mission – To Establish and oversee the
university’s Data Governance process.
Composition – This committee is advisory tothe university cabinet. The Director of IRPAwill serve as chair of the Data GovernanceCommittee. Membership of the committee will
consist of the primary data stewards of allacademic and non-academic universitydivisions engaged in collection, validation, andreporting of data.
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Data Governance Committee Charge
System and Process Documentation
Data Quality and Integrity
Training, Communication, Decision Supportand Outreach
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Goals
Primary:
Identify & Document Data related processes
Identify Major Data Elements Define Major Data Elements
Secondary:
Identify Systems of Record
Identify & Assign Data Owners
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Accomplishments !!
Identify the Need for Process Improvement• Redundancy or gaps in process
• Communication between departments
Identify Opportunities for Possible ProcessAutomation• Data bridges or imports between systems
Identify Possible Improvements in UniversityReporting Processes• How, who, what and from where?
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Accomplishments in 1 year !!
Identified around 19 functional units
Identified & documented 56 processes
Identified 71 Microsoft Access databases
Identified 42 possible recommendations
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DG New Challenges
Maintain subcommittee momentum,commitment & documentation integrity
Creating awareness, while gainingexcitement about data governance
Limited IT resources
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Priority Matrix
High Medium
Medium Low
3 or more
Functional units
1 or 2
Functional units
CriticalBusiness process
Non Critical
Business process
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Next Steps
InformationTechnology
DataGovernance
ChangeManagement
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IT DGC
CM
Next Steps
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Building A Data Community
Merger of IR and BI
Total Data Integration Strategy
Community of Practice Maximize Analysis Capabilities
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Building A Data Community
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IR + BI = AA
UK Analytics andTechnologies
IR and BI merged into
Advanced Analytics (AA)
AA does all regulatoryand operational reporting
Data Science (DS) group
established within AA todo analysis
Data Science is core ofthe new IR
BI IR
AA
AA(DS)
AA IR(DS)
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One Data Warehouse toRule them All
SAP HANA as Data Warehouse
• In-Memory RDBMS
Data Models as final products
• Regulatory and Operational reporting
• Designed for Analysis
Integrate as much data as possible
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And in the DarknessBind Them
Available
Enrollment
Retention
Majors, minors
Degrees
Courses/grades
Applicants
Demographics
Student Financials
Peer Tutoring
Instructors Faculty stats
Building utilization
In-Progress
Housing
Greek
Holds
Alerts
Advisors
Time-to-degree
Test scores
Micro-surveys
Transfer stats
HR details
Next Up
Surveys
Teacher/courseevaluations
Degree audit
High Schools
LMS
Campus recreation
Faculty research andsponsored projects
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Analysts across University have direct access tounderlying data
• Start with a core group of early adopters
• “Crowd-Source” basic reporting At least one Super User in every College or unit
• Data access and analysis with Tableau
• Point-and-click interface
• Bi-weekly training seminars regularly attended by 30+
Community of Practice
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Built by COP
Student Demographics Recruiting Map
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Built by COP
Section Fill Rates Class Grade Distributions
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IR Certified
IR designs “official”dashboards withcommon standards
• COP feedback anditerations
• COP has access tounderlying data and
definitions
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Data definitions are multiple, iterative andconsensus-driven
• IR acts as unbiased arbiter
• Data Stewards are identified, but no formal group Units are finding us to ask to have their data put
into HANA
• “You can see mine if I can see yours”
When data becomes important, so does dataquality
• Missing instructors have fallen from ~20% to ~3%
Data Governance?
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Group Activity #2
How Can You Get Your OrganizationProgress Along the Data Governance
Maturity Curve?
What Is Data Governance?
Why Is Data Governance Necessary?
Who should Be Involved, and How?
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Reference: www.virtusa.com
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Craig - Group Activity #3
1. Make a dashboard using your assigned datasource
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Craig - Group Activity #3
1. Make a dashboard using your assigned datasource
2. Give your dashboard to the table next toyou
• Make a new dashboard using your original datasource plus this one
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Craig - Group Activity #3
1. Make a dashboard using your assigned datasource
2. Give your dashboard to the table next toyou
• Make a new dashboard using your original datasource plus this one
3. Rotate again
• Make a dashboard using any data you canimagine!
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Craig - Group Activity #3
1. Did you get new ideas from the othertables?
2. Did you agree on definitions? Multipledefinitions?
3. Did you use any statistics?
4. What do your data tables need to look like?
5. How will this help you make betterdecisions?
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Final Discussion
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