Results of Manatee County's first GIS Capability Maturity Model in 2019

1. 2019 Geospatial
Maturity Assessment
Mark Murphy
Sept. 11, 2019

2. URISA GIS Capability
Maturity Model
 GIS Capability Maturity Model (GISCMM) is
comprehensive assessment of GIS management
capability maturity.
 The assessment measures your organization’s GIS
maturity on a wide range of geospatial capabilities.
 People, Data, Processes, Policy, Technology & Legal
 Urban and Regional Systems Association, URISA has
been at the forefront of government and
business applications of GIS for almost
50 years. GISCMM was developed 2010.

3. How the GISCMM Works
 The GISCMM is divided into two main areas called
Enabling Capability (EC) and Execution Ability (EA).
 (EC) - The technology, data, resources, and related
infrastructure that can be bought, developed, or
acquired to support enterprise GIS operations.
 (EA) - The degree to which an enterprise achieves faster,
better, cheaper results through clarity in planning.
Ensures that the investment decisions and project
deliverables are connected and monitored for
sustainable business results.

4. Methodology
 Stakeholders complete self-assessment survey.
 ITS compiled all completed surveys into a combined
result.
 The combined result reviewed and analyzed.
 Evaluate Results.
 Survey results posted online at URISA’s GIS
Management Institute site.

5. Participants Surveyed
 Engaged Stakeholders from:
 Building and Development Services
 Community Services
 Information Technology Services
 Neighborhood Services
 Property Appraiser’s Office
 Property Management
 Public Works
 Public Safety
 Redevelopment and Economic Opportunity
 Utilities

6. Demo Spreadsheets

7. Analysis Performed
What are we looking at?

8. Analysis Performed
 Standard Deviation – a quantity calculated to
indicate the extent of the deviation for a group of
values.
 A low standard deviation indicates little variation in
survey responses while a high standard deviation
indicates a wider variance in survey responses.
 Average – a number expressing the central or
typical value in a data set.
 A low average indicates poor performance in
a specific metric while a higher average
indicates higher performance.

9. Performance Matrix
Using the Std. Dev. and average we developed a
performance matrix.
Std. Dev. Average Result Benefit / Resource
Low Low = Everyone doing poorly Can only improve / no local
knowledge (ESRI Credits)
High Low = Most doing poorly, a few
doing well
Lots of benefit / Some local
help available
High High = Most doing well, a few doing
poorly
Some benefit / Lots of local
help available
Low High = Everyone is doing well None / Desired maturity

10. (EC) Analysis Performed
 Calculated the average standard deviation to be
0.253 and used that to delineate between a low
and high std. dev.
 Calculated the average of the averages to be .79
and used that to delineate between a low and high
average.
 Applied Low / High ratings to the various (EC)
metrics based on our performance matrix

11. (EA) Analysis Performed
 Calculated the average standard deviation to be
1.21 and used that to delineate between a low and
high std. dev.
 Calculated the average of the averages to be 3.25
and used that to delineate between a low and high
average.
 Applied Low / High ratings to the various (EA)
metrics based on our performance matrix

12. Demo Analysis In Spreadsheet

13. Evaluate Results
So what?

14. Enabling Capability (EC)
Results
 There were issues in respondent’s interpretation of
questions groups EC1 – EC4 which dealt with
specific data layers.
 Focused on EC5 – EC23

15. Negative (EC) Results
 Bad
 EC5 – GIS Data Coordination
 EC6 – Metadata
 EC20 – GIS Linked to Strategic Goals
 EC22 – GIS Funding
 EC23 – GIS Financial Plan
 Very Bad
 EC19 – GIS Governance Structure

16. Positive (EC) Results
 Good
 EC15 – GIS Application Portfolio O&M
 EC21 – GIS Budget
 Very Good
 EC7 – Spatial Data Warehouse
 EC8 – Architecture Design
 EC9 – Technical Infrastructure
 EC10 – Replacement Plan
 EC11 – GIS Software Maintenance
 EC12 – Data backup and security
 EC13 – GIS Application Portfolio
 EC14 – GIS Application Portfolio Management
 EC16 – Professional GIS Management
 EC17 – Professional GIS Operations Staff
 EC18 – GIS Staff Training and Professional Development

17. Spider Diagram
Enterprise (EC)

18. Execution Ability (EA) Results
 There was more opportunity for growth on this side
of the GISCMM
 EC was stronger due in large measure to the
maturity of supporting IT processes like:
 Tech refresh
 Data backups and recovery
 Robust networking
 Virtualization
 Network Accessible Storage

19. Negative (EA) Results
 Bad
 EA1 – New Client Services Evaluation and Development
 EA3 – Service Delivery Tracking and Oversight
 EA4 – Service Quality Assurance
 EA5 – Application Development or Procurement
 EA7 - Quality Assurance and Quality Control
 EA13 – Operation Performance Management
 EA16 – Resource Allocation Management
 Very Bad
 EA2 – User Support; Help Desk and End-User Training
 EA11 – Regional Collaboration
 EA12 – Staff Development
 EA13 – Client Satisfaction Monitoring and Assurance

20. Positive (EA) Results
 Good
 EA9 – Process Event Management
 EA14 – Individual GIS Staff Performance Management
 Very Good
 EA6 – Project Management Methodology
 EA8 – GIS System Management
 EA10 – Contract and Supplier Management
 EA17 – GIS Data Sharing
 EA18 – GIS Software License Sharing
 EA19 – GIS Data Inter-operability
 EA20 – Legal and Policy Affairs Management
 EA21 – Balancing minimum privacy with max data usage
 EA22 – Service to the community and profession

21. Spider Diagram Enterprise
Execution Ability

22. Summary Conclusions
This GISCMM effort and the resulting analysis confirm
what felt we knew anecdotally and provides the data to
back that up.
 Major Pain Points
 Lack of overall governance (EC)
 Lack of Data Coordination (EC)
 Lack of User Support; Service Desk and End User Training (EA)
 Service Delivery Tracking and Quality Assurance (EA)
 Staff Development (EA)
 Client Satisfaction Monitoring (EA)

23. Summary Conclusions Cont.
 Our Enabling Capability (EC) is stronger than our
Execution Ability (EA). In other words, we have more
capability than we can effectively use.
 Distributed GIS has inherent disadvantages regarding
(EA). GIS being federated within different organizations
creates inconsistencies in how work is tracked, projects
managed, resources allocated etc.
 Lack of Governing body creates a lack of general
direction for the enterprise.
 GIS provides good service despite the challenges
which I believe is a testament to the
professionalism of GIS staff in all quarters the
enterprise.

24. Summary Conclusions Cont.
 Departments look to ITS to fill gaps in maintaining
data that is not specifically “owned” by another
department, providing technical GIS leadership or
assistance when needed and plugging gaps in
funding by passing the hat as needed.
 This first run of the GISCMM will help us better
understand the questions and goals improving
result consistency on future GISCMM surveys.

25. Recommendations
 Conduct customer satisfaction surveys regularly.
 Brainstorm other ways to monitor customer
satisfaction.
 Coordinate some form of in-house training.
 Consider adding a Learning and Service Credits to the
next ESRI ELA.
 Engage with ESRI to develop centralized governance of
Portal, AGOL and the overall Enterprise
 Consider replacing hardware utilized by GIS personnel
throughout the county at one time vs. on a
by dept. rotation more frequently.

26. Recommendations Cont.
 Work with CSC to try create greater GIS issue
resolution capability with the CSC.
 Consider consolidating GIS resources under
centralized governance with an eye towards
alignment with strategic business goals.

27. Results Posted
http://gmidev.dtsgis.com/Subscriber/Home

28. Sad Footnote - 8/23/19
I did want to announce to you the end of the GIS Management Institute. One of
the most important tasks of the Board of Directors is to constantly evaluate all of
URISA’s programs to ensure we are providing the best opportunities for our
members to reach their top potential. When the GIS Management Institute (GMI)
was created, the original intent was to provide a comprehensive program that
would facilitate and support GIS managers with the online GIS Capability Maturity
Model and several more initiatives to help organizations with their GIS
management operations. These include:
 Development of GIS Management Best Practices and Body of Knowledge
 GIS Management-related professional education (GIS Leadership Academy and a robust
GIS Leadership & Management program track at GIS-Pro each year)
 Expansion of GIS Management information dissemination
 Promotion of awareness and adoption of GIS management best practices by organizations
and GIS professionals
Sincerely,
Kim McDonough, GISP, Tennessee Department of Transportation
URISA President

29. The End!
Questions?
Special Thanks to:
John Sharp – Excel Jedi
Jeff Pace – Stats Man
Sharon Wallace – Respondent Wrangler
Bryan Townsend – GISCMM Coach