1. Data Management Program Overview
5/12/2018
Data Management Program Overview
February 6, 2017

2. Agenda Recap of 12/18 discussion: Key Takeaways and Next Steps
CIO Outreach – what we have heard so far
Implementation Strategy and Plan

3. Recap of 12/18 Discussion

4. Recap of 12/18 Discussion Takeaways from our last discussion:
Improve understanding of the pain points in the schools
There is acknowledged value in simplifying the Data API Architecture
Avoid the perception of “boiling the ocean”, articulate incremental approaches
How will this improve our existing administrative data?
How will this enable strategic programs (SIS, IAM) and other major future initiatives?

5. Recap of 12/18 Discussion Next steps:
Convene a small group in January to focus on: pain points, opportunities
Establish an Executive Committee and otherwise improve representation from/collaboration with schools and departments
Better define the strategy
Simple, clear definition of the strategy
How will this strategy be implemented, and what are the specific benefits to the schools?

6. CIO Outreach: What we have heard so far

7. What we have heard so far
Reporting Tools & Access to Data Models
HLS uses Business Objects, would consider OBI/Qlikview
HMS, GSE, GSAS have made or are considering investments in Tableau
Access to consistent local and central/administrative data is a priority (HLS, HKS, HMS)
HLS would like to have better access to central administrative data generally
Accurate, well-defined Administrative Data
HMS would like to better understand Faculty data and where it is sourced from to better manage data quality.
HDS and HKS expressed that many of the schools use the same data differently, and this impacts efficiency and productivity.
Simplified Data Exchange Architecture
HLS and HBS may be able to use existing Student APIs, but it is not clear what data is available and if it meets the needs. Discussions are ongoing.
HMS pulls back data into it’s data warehouse that takes hours to run each day
All so far have acknowledged how time consuming and expensive it is to connect our systems

8. Implementation Strategy and Plan

9. Review: Vision and Strategic Objectives
Program Vision
To harness the significant value of Harvard’s data assets:
We strive to reduce costs and enable improved decision-making for our staff, faculty, and students through the effective use of data management services.
Strategic Objectives
Provide people with better reporting tools and access to data models
Deliver accurate, well-defined administrative data that are easy to find and understand
Simplify the way we exchange data between our systems
Program Strategy
Invest in a “Data Management Infrastructure” to improve the value of future investments
Fully implement tools from we already own
Apply tools to existing priority backlog (SIS, IAM, Finance) for quick wins
Partner with new projects to deliver end-to-end ROI
Continued engagement with schools and central units to identify pain points and solutions

10. Strategic Objective 1 – Implementation Strategy and Plan
Provide people with better tools and access to the data they need to do their jobs
Make the BI tools we have today (OBI, Qlikview) available to everyone
Make the data we have today more readily available
Train staff to use BI tools and data models
Implementation Plan
Stand up university BI tool platform service
Select one school and conduct OBI/Qlik pilot
Develop provisioning process for administrative data models
Select one administrative data model and provide new access to select users
Pilot with one school to make its local data sources available in OBI/Qlik
Develop BI training program for business power users and IT Staff
Pilot BI training for business power users
Pilot BI training for IT staff

11. Strategic Objective 2 – Implementation Strategy and Plan
Deliver accurate, well-defined administrative data that are easy to find and understand
Implement a common, shared data glossary
Implement streamlined data access provisioning
Document the data that we have today
Work with initiatives during their projects to document new data
Implementation Plan
Stand up a university data glossary service
Implement a tool and service to streamline the provisioning of access to our data
Document our existing administrative data assets
Pilots with 1-2 administrative data teams (Stu, Res. Admin, HR, Fin, Alumni, IAM) to implement existing data documentation into the business glossary
Pilot with one school to implement a targeted local data source into the business glossary
Develop and implement standards on data documentation for new initiatives
Engage with Position Management, Buy-to-Pay, Contingent Workforce, and others
Establish a data glossary “Community of Practice” with users across the university

12. Strategic Objective 3 – Implementation Strategy and Plan
Simplify the way we exchange data between our systems
Implement a common, shared data API platform
Make API’s that we already have today available to everyone
Work with initiatives during their projects to use this platform
Retire current point-to-point solutions over time
Train IT staff on how to use this API platform
Implementation Plan
Stand up a university API platform service
Build new APIs and register existing APIs
Pilot one new “enterprise” data service (ie. Person) and incrementally build API solution
Partner with schools on a pilot to build APIs on this new platform
Register existing APIs (ie. Student, HR, Finance) in the API Gateway
Develop and implement EA standards on APIs for new initiatives
Engage with Position Management, Buy-to-Pay, Contingent Workforce, and others
Catalog legacy point-to-point solutions and prioritize for conversion to new platform
Convert 2-3 central and 2-3 school point-to-point solutions
Develop and implement API development training program for IT staff in schools and center

13. Next Steps
Collibra Data Glossary and Mulesoft/API “Working Groups” are being formed
CIO outreach is ongoing, further engagement?
Second “Small Group” meeting planned for next week
Early discussions on Gartner Roundtable to be hosted at Harvard
Outreach to other institutions is ongoing
Presentation to ITCRB on 2/22

14. Appendix

15. Business Benefits and High Level Timeline

16. Data Management High Level Business Benefits
Today
Data Management
Tomorrow
Data is hard to find and access
Too many tools that cost too much
Limited access to existing data models
People don’t have data they need
Data is difficult to understand
What does this data mean?
Where did it come from?
Is it the right data for my needs?
Data is expensive to exchange
One-off point-to-point solutions
Duplicated effort to rebuild integrations
Too much time and money spent
Reporting tools and data models
On-demand, self service reporting tools
Access to administrative data models
Improved end-user skills, competencies
Data asset library
Intuitive business data glossary
Report catalogs, data dictionaries
Well-defined, discoverable data assets
Data exchange architecture
Data exchange and integration framework
Reusable data exchange services
Reduced cost and faster delivery