{"id":580237,"date":"2025-12-01T17:54:37","date_gmt":"2025-12-01T17:54:37","guid":{"rendered":"https:\/\/www.esri.com\/en-us\/industries\/blog\/?post_type=blog&p=580237"},"modified":"2025-12-01T19:23:22","modified_gmt":"2025-12-01T19:23:22","slug":"from-good-intentions-to-good-data","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/en-us\/industries\/blog\/articles\/from-good-intentions-to-good-data","title":{"rendered":"From Good Intentions to Good Data"},"content":{"rendered":"

A Practical Playbook for Technology Adoption in the Fire Service<\/h2>\n\n

The fastest way to improve community risk reduction (CRR)<\/a> outcomes isn\u2019t conducting one more inspection\u2014it\u2019s fixing the data you already rely on. Standardizing occupancy information with verified geospatial data sources, partnering with third-party force multipliers, and wiring updates back into your record management system (RMS) closes the loop from CRR to computer-aided dispatch (CAD)<\/a>. This guide distills a candid conversation among practitioners into concrete steps you can implement now.<\/p>\n\n

Why This Matters\u2014Now<\/strong><\/h2>\n\n

If you\u2019ve ever chased duplicate addresses like \u201c123 N Main St.\u201d versus \u201c123 N. Main Street,\u201d you already know that bad data is a silent budget killer. Jill Cotton<\/a>, President of InspectionReportsOnline (IROL), described years of \u201cbad data entry habits\u201d that splinter occupancy records and erode confidence across systems. Her team moved to using verified sources and implemented guardrails so that contributors can change the name of a location without breaking the underlying address record. This approach ends the spiral of duplicate data and preserves audit trails.<\/p>\n\n

Will Brocious<\/a>, Deputy Chief at Charlottesville (VA) Fire Department, emphasized the operational reality: No department has unlimited staffing or budget. Without technology\u2014and partners\u2014you fall behind. Third-party help doesn\u2019t replace your people; it multiplies them.<\/p>\n\n

The Problem We Can Actually Solve<\/strong><\/h2>\n\n

Inconsistent occupancy entries create duplicates and misroutes across RMS, inspection software, and downstream systems. Cotton\u2019s approach enforces location integrity by locking down addresses while allowing safe edits to business names or contacts. High-change environments such as malls and big-box suites shift tenants monthly, making it difficult to keep up. Brocious noted the value of offset inspections\u2014where the department and third-party partners alternate visits\u2014so that sites are touched twice a year, keeping contact and system information fresher than traditional cycles.<\/p>\n\n

Another challenge is orphaned buildings. When cities adopt validated geospatial data capture, they often discover thousands of occupancies missing from legacy databases. These new records have real implications for community risk reduction.<\/p>\n\n

What Verified Geospatial Data Really Means<\/strong><\/h2>\n\n

This isn\u2019t just about using a map; it\u2019s about creating a workflow that enforces accuracy. It starts with an authoritative basemap and jurisdictional boundary, so contributors know what\u2019s inside your footprint and what isn\u2019t. Immediate detection of duplicate data prompts users with \u201cDid you mean this?\u201d checks, preventing variants at the point of capture. Guardrails on edits allow name changes while blocking address mutations, preserving data lineage and auditability. Finally, secure identities and logins aren\u2019t friction\u2014they\u2019re protection. You get what you pay (and sign in) for.<\/p>\n\n

\"\"<\/figure>\n\n

Cotton\u2019s caution is clear: Open, crowdsourced tools are great for consumers but not for life safety. Pins can be moved by anyone, and sources can be wrong. Verified inputs and identity controls are nonnegotiable when data guides response.<\/p>\n\n

Closing the Loop: From CRR to CAD<\/strong><\/h2>\n\n

An unsung step is the work of the gatekeeper, the designated person who validates inbound updates so that they match your data model. From there, your RMS pushes authoritative changes to CAD and dispatch platforms. When a call drops, verified addresses and site intel are already in the response stack. That\u2019s not back-office convenience; that\u2019s life safety.<\/p>\n\n

Virtual Isn\u2019t Verification (but it can be useful)<\/strong><\/h2>\n\n

Video walk-throughs kept work going during COVID-19 and remain handy for narrow fixes like confirming an exit sign. But a virtual walk-through is not a substitute for validated data about systems, addresses, or building conditions. Use it as a tool\u2014not the source of truth.<\/p>\n\n

A Field-Tested Adoption Road Map<\/strong><\/h2>\n\n

Start by defining the problem before buying a solution. Inventory pain points such as duplicates, missing occupancies, out-of-date contacts, and RMS-to-CAD mismatches. Standardize your schema by locking down authoritative address fields while allowing safe edits to names and contacts. Appoint a gatekeeper to triage inbound third-party updates, enforce standards, and own the round trip into RMS and CAD.<\/p>\n\n

Next, equip your remote sensors by training contractors and partners on understanding why precision matters and giving them a map-first interface with duplicate detection and clear jurisdiction boundaries. Integrate the stack so that accepted updates flow from your GIS to RMS and onward to CAD and dispatch. Pilot the process, measure results, and iterate. Track metrics such as duplicates prevented, new occupancies discovered, update-to-dispatch time, and the percentage of calls with verified locations.<\/p>\n\n

\"\"<\/figure>\n\n

Finally, share the wins. Show responders and the community how verified data improves safety and reduces wasted responses, and celebrate partner contributions\u2014their data is part of your mission.<\/p>\n\n

What Success Looks Like<\/strong><\/h2>\n\n

Success means duplicate data prevention at the point of entry, rapid discovery of missing inventory, and biannual touches on high-change occupancies by offsetting internal and partner inspections. It also means verified addresses in CAD before wheels roll, complete with current contacts and key system information.<\/p>\n\n

Success also means applying\u00a0governance<\/a>\u00a0to ensure the effective and efficient use of information technology and location intelligence to enable an organization to achieve its business goals. Understanding your\u00a0geospatial strategy<\/a>\u00a0can help establish a vision and path for organizational success and help guide planning, prioritization, and the execution of projects.\u00a0<\/p>\n\n

Final Word<\/strong><\/h2>\n\n

Technology adoption isn\u2019t about the next shiny tool\u2014it\u2019s about trustworthy information moving where it must, when it must. Standardize the data, empower your partners, and wire the loop back to dispatch. Do that, and you\u2019ll feel the difference at 2:00 a.m., not just in a dashboard.<\/p>","protected":false},"author":191,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[],"tags":[],"class_list":["post-580237","blog","type-blog","status-publish","format-standard","hentry","industry-fire-rescue-and-ems"],"acf":[],"yoast_head":"\nFrom Good Intentions to Good Data<\/title>\n<meta name=\"description\" content=\"From Good Intentions to Good Data distills a candid conversation among fire service practitioners into concrete steps you can implement now.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/en-us\/industries\/blog\/articles\/from-good-intentions-to-good-data\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta 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