1. HEI Modernization Project – 90 Day Review
December 22nd 2016
Reporting Period: 9/12/2016 to 12/22/2016

2. HEI Modernization Project
Background
Ninety Day Checkpoint
Project Challenges
Project Phase Approach
Project Health Metrics
Questions / Feedback
Next steps
Roadmap

3. Background Why do a 90 day review? What will we cover?
Periodic update to sponsors and other ODHE leadership
Share progress / challenges, adjust to feedback
What will we cover?
Quick project recap (for new attendees)
High level accomplishments and challenges
Drill into details if desired
Who is the audience?
ODHE Leadership
Other interested ODHE parties that don’t have operational
involvement with project

4. Sixty Day Checkpoint
Continuing refinement of the data model base on testing and business feedback.
On going programming of data quality validations.
Initial Data conversion program development is complete and ready for user testing
Data Informatica mapping development for data movement and transformation is under way for Facilities, Academic Programs, and Enrollment file submissions.
Data integration tool selection and purchase complete
On going evaluation of Business Intelligence reporting tools
“Don’t borrow my watch to tell me what time it is.”
Avoid stating the obvious. Discovery puts us in a position to deliver on 2 and 3….it is not in and of itself a reason to do the project.
Multiple sessions – Good Cross Section: Tech Functional, LeaderContributor, etc.

5. Ph1: Extract, Transform Load
Phase Benefits
Phased Project Approach
Functional
Technical
Q2 Q3 Q4 Q1 Q2
Ph1: Extract, Transform Load
6/9/17 Q3
Enhanced reliability
Performance Improvements
Faster turnaround on change requests
Risk mitigation (platform and staffing)
Align with state security model (SSO, ID Mgt)
Enhanced user interface
Risk mitigation (platform and staffing)
Improved audit capabilities
Ph2: Report Migration 7/31/17
Contemporary analysis UI
Staffing flexibility (remove SAS dependency)
More efficient analysis operations
Optimized data structure
Logical federation of disparate sources
Data Warehouse
Ph3 8/7/17
Optimization
Public Analytics
Provide Ph3 capabilities to public
Reduce public requests for data
Amortize technology investments over larger user base
Ph4
Data Visualization
Ph5
Leading edge user tools providing next level insights
Increased usage and benefit from existing
data sets

6. Closing Goals for Next Reporting Period
Continue to refine data model based on testing results
Complete development of data movement and transformation for all remaining submission files
Select a Business Intelligence reporting tool
Begin integration and user testing
General Reminders:
- According to plan (effort), we are <1/3 of the way done…
There’s plenty of time to change course / approach to meet your needs!
Solicit honest / open feedback on us…what do you like / not like about how we are going about things.
Next Steps:
Deep dive on plan assumptions and specifics
Deep dive on roadmap assumptions and specifics (resourcing
Pending Action Items:
Still digesting usage stats / Model (JC / Jill & Joel)

7. Roadmap Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 SCOPE:
ETL Implemented
Canned Reporting
Ad Hoc Reporting
Public Analytics
Data Visualization
SCOPE:
New ETL tool used to implement data loads
Decommission legacy ETL programs.
Implement standard, repeatable reports against new staging tables
Capture usage statistics to establish prioritization.
Create ad hoc reporting infrastructure
Internal: SAS Substitute
External: Restricted Queries
Present analysis tools w/safe metadata layer developed in Phase 3 to public.
Select and implement data visualization tool.
Internal tool at launch
Extend to trusted partners or public at large.
BENEFITS:
Foundational element for new DW: enhances agility, enables unified data sources on back end.
Staging tables enhance traceability
Systematic data checks built into solution.
Reports served from new environment
Standardized formats and branding.
Assesses extent of Spreadsheet-ware
Transparent cutover for users
Consistent, safe access to data on demand.
Free IT from report writing business
Enable safe self-service access to data
Free ODHE staff to perform higher order analytics
Gain insights not readily apparent from traditional data presentation techniques
Emerging technology, use cases still evolving.
Three Key Principles in Building Roadmap
Reduce implementation risk
Bring Value Quickly
Continually Learn and Adapt
High Level Features  Benefits (Drawbacks) of This Approach:
Phased implementation  Lower disruption to department ops
Parallel systems  Lower risk, increase ability to validate
Out phases introduce forward technologies on architecturally correct platform  Lower risk, get fundamentals right, then add headline features.
Canned Reporting Phase begins discussions on measures and dimensions…Ad Hoc Reporting Phase cements it.
Next Iteration of this: Adds Dates and
STABILIZE: Phase 1 and 2 STANDARDIZE: Phase 2 and 3 OPTIMIZE: Phases 3, 4, and 5
Next Steps on Roadmap:
Validate / Revise Top Level Phases (based on feedback)
Drive down next level of cost estimates, resource requirements, and schedule (rough cut)
Articulate connection points (and possible overlaps) between Phases.