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A leading financial services firm with over $5 trillion in assets under management transforms their real estate investment process with data-driven security intelligence.
A financial institution with major regional offices and a large real estate investment arm needed to evaluate five different neighborhoods across a major US city for investment opportunities. Previously, their security analysts required a full week to complete each assessment, making the one-week deadline for all five sites impossible to meet using traditional methods.
The security team implemented Base Operations to conduct radius-based analyses that provided hyperlocal threat assessments for all five locations. The BI team leveraged the Base Operations API to ingest change detection and crime type data into their internal risk models, while establishing direct communication between security analysts and the real estate team.
The financial institution maintains a significant portfolio of real estate investments and office locations throughout the United States. The organization's corporate security team regularly provides threat assessments to various internal stakeholders, including their real estate investment division.
These comprehensive assessments analyze multiple threat hazards, with crime and civil unrest being critical components that influence investment decisions. The assessments help validate the risk profile of potential properties before finalizing leasing or development commitments.
When the real estate team requested security assessments for five different neighborhoods in a major US city within a one-week deadline, the security team faced a significant challenge. Using their previous methods:
This reactive approach meant the security team was often the bottleneck in the investment decision process, limiting their strategic value to the organization.
The security team transformed their assessment process using Base Operations:
Phase 1: Immediate Analysis Deployment The security analyst leveraged Base Operations' radius-based analysis feature to conduct hyperlocal threat assessments for all five locations simultaneously. This provided standardized, granular intelligence down to the sub-mile level for each target neighborhood.
Phase 2: Data Integration Concurrently, the BI team used the Base Operations API to:
Phase 3: Cross-Departmental Collaboration The Director of Security established direct communication channels between security analysts and the real estate team. This enabled:
Operational Efficiency: The security analyst delivered comprehensive threat assessments for all five neighborhoods within one week—a task that would have previously required five weeks and multiple analysts. This 500% improvement in efficiency immediately positioned the security team as an enabler rather than a bottleneck.
Increased Business Demand: Following this successful implementation, requests from the real estate team increased by 300% in the subsequent month. The dramatically improved speed-to-insight transformed how the investment team perceived security's value in their decision-making process.
Enhanced Risk Modeling: The BI team created sophisticated new risk models incorporating Base Operations data:
Strategic Organizational Impact: The Director of Security successfully elevated the security function from a cost center to a strategic advantage. By providing data-driven insights that directly supported business objectives, the security team strengthened relationships with key stakeholders and demonstrated measurable value to the organization.
The financial institution continues to expand its use of Base Operations:
This proactive approach to security intelligence has transformed how the organization evaluates real estate investments, making security an integral part of business strategy rather than just a compliance function.
A tier-1 management consultancy with 75+ regional offices and 280,000 employees transforms their global security operations from reactive to proactive.
A major discount retailer with 16,000+ locations transformed their security approach by leveraging local threat data to identify patterns, optimize resource deployment, and reduce incidents at high-risk locations.