Reduce/Eliminate GIS Silos Take Confusion Out of Complexity GIS Address Data Management Process Resource Center

Transcription

1 Reduce/Eliminate GIS Silos Take Confusion Out of Complexity GIS Address Data Management Process Resource Center

2 Presenter Henry Draughon How I Think - Remove the Confusion From Complexity Old School U.S. Naval Flight Officer F-4 Phantom Naval Air Station Air Traffic Control Officer Checklist, Checklist Process, Process - Metrics, Metrics Not a Data Management Expert But I ve learned a lot and learn fast! Over 30 Years IT, Process, and Project Management Experience

3 Why Serious Planning and Commitment is Required Historically High IT & GIS Project Failure For decades a very high percentage of data projects fail to meet user expectations 68% of IT and GIS projects fail These factors are acknowledged by the National Association of State Chief Information Officers (NASCIO) Lack of executive sponsorship, sufficient domain business analysis, and project planning Poor stakeholder collaboration Investments in silos: enterprises paying repeatedly for the same functionality. Not fully appreciating GIS sophistication and data sharing implications

4 How to Increase the Likelihood of GIS Project Success Executive Commitment, Domain Expertise, Plan & Execute Roger Tomlinson (considered father of GIS), Strategic purpose is the guiding light. National Association of State Chief Information Officers (NASCIO) recommends formal data management Strategic Purpose The system that gets implemented must be aligned with the purpose of the organization as a whole. Agency-Wide Stakeholder collaboration Strategic investments: Agency paying once for core functionality & leveraging investments. Clear understanding of GIS sophistication & inter-agency data sharing implications

5 Business Purposes Supported by Address Data Elections, Jury Services, School Districts Ensure that voters are assigned to the correct precincts/districts /Emergency Response Ensure that every home and business is included and accurately defined in a dispatch system for rapid emergency response. Land Records Ensure that everyone is paying the correct taxes to the correct entities. Utility Customer Accounts Ensure that everyone is paying the correct bills.

6 Typical State of Address Data within a Regional Agency Several departments within the agency use address data. Address data maintained and used within the agency and the geographic constituents is not from a single source. Departments like Elections and Jury Services have expressed that their address data is inadequate. A Geographic Information Systems (GIS) is being looked to as a solution for this problem. Multiple Address Lists Not Standardized Not Consolidated Poor Quality

7 Readership Appetite has Transformed

8 Traditional Text-Based Digital Documents Do Not Meet the Appetite of Today s Reader Content o Multiple disconnected documents o Hard to access o Constant paging up and down to locate desired content o Walls of text - laborious to comprehend o Inaccurate, inconsistent, out-of-date

9 Include Referenceable Authoritative Sources The Geospatial Data Lifecycle Pursuant to OMB Circular A- 16, sections 8 (e) (d), 8(e) (f), and 8 (e) (g) maintained by the Federal Geographic Data Committee The Geospatial Data Lifecycle has been incorporated into the framework at this point because of it takes into consideration explicit GIS address data factors

10 Explicit Standards Reviewed Federal Geographic Data Committee (FGDC) The United States Thoroughfare, Landmark, and Postal Address Data Standard National Emergency Number Association (NENA) NENA Standard Data Formats for Data Exchange and GIS Mapping GIS Data Collection and Maintenance Standards Commission on State Emergency Communications (CSEC) CSEC NG9-1-1 GIS Data Standard Environmental Systems Research Institute (ESRI)

11 GIS Address Data Management Inherently Complex, Confusing, Non-Standardized

12 Data Management Association s Data Management Body of Knowledge NASCIO and other private and public sector experts strongly recommend adoption of a formal, documented, referenceable data management model The most cited and recommended model for data management

13 DAMA-DMBoK Circle and Environmental Elements

14 Data Management Added Complexity DAMA-DMBoK Context Diagrams

15 DAMA-DMBoK - Well-Designed, Yet Complex DAMA-DMBoK is an intensive study Focus on data as a business asset requires knowledgeable discussions across departmental boundaries (silos) GIS is inherently complex. Introducing Data Management increases the complexity Complexity breeds confusion 430 Pages 260 Pages

16 How Much Data Management Project Knowledge Investment Will Be Lost? Project Complete o Consultants are Gone o Internal Project Leads are Gone Data Management Knowledge Inve$$$tment

17 GIS Data Management Process Resource Center

18 Data Management 1.0 Governance

19 Data Governance Understand Strategic Enterprise Needs

20 Data Governance Identify User or Business Needs SIPOC & RACI Boxes Expanded

21 Master Addressing Repository

22 Master Addressing Repository Process Resource Center

23 Master Addressing Repository New Addressing Unincorporated

24 Master Addressing Repository New Addressing Unincorporated

25 Total Accountability Data Governance Context Diagram Total accountability combines SIPOC and RACI which are complimentary concepts. Every activity or process has inputs and outputs. Ownership and relationships are mandatory for success.

26 Control Variation for Consistently Predictable High-Quality Data The goal is to enable continuous production of high quality data that supports the business. How? Identify and control variation that affects the predictability of data management process and activity outcomes. Data Governance Context Diagram Variation Control Lower Variation Impact on Data Quality: Repeatable High Quality Predictable Outcomes Sustainable Processes Reduced Redundancy Reliable and Trusted Information Lower Costs Higher Variation Impact on Data Quality: Unpredictable Quality Incomplete Incorrect Redundant Questionable and Unreliable Information Higher Costs

27 Effective Data Management Controls Variation Over Time Increasing Predictability and Trust Culture of Effective Collaboration Predictable Outcomes Trusted Information Effective Decision-Making Gain Competitive Advantages Quickly Adapt to Competitive Business Trends Characteristic of Silo Culture Unpredictable Outcomes Unreliable Information Lower Quality Decision-Making Unable to Adapt to Competitive Business Trends Variation Under Control Variation Not Under Control Upper Limit Goal Lower Limit Performance Level Upper Limit Goal Lower Limit Performance Level

28 Data Management Roadmap

29 Navigate Through Complexity Roadmap for the DAMA DMBoK

30 Data Management Process Resource Center Mobility

31 Contact Information Contact: Henry Draughon Process Delivery Systems (972)