JARED HOLZMAN

Built a centralized analytics platform tracking 55,000+ employees and resources across sites — measuring headcount growth in new talent markets and reductions in Northbrook against targets, with roles, relocations, and severance tracked across 20+ business units. Partnered directly with the CFO to translate that consolidated picture into location and organizational planning decisions. During COVID-19, was selected for an SVP-led task force to coordinate a structured enterprise-wide forecasting exercise across every business unit. Designed and deployed a forecasting template pre-loaded with workforce data, giving each business unit leader everything they needed to evaluate and categorize their resources by on-site requirement. Led information sessions across business units to ensure consistent methodology, then aggregated all responses into a single enterprise view — determining that only 3–5% of employees required on-site presence. That finding provided the data foundation for the location consolidation analysis across 150+ corporate sites that enabled a 67% reduction in office footprint, $244M in annual real estate savings, and the $230M+ monetization of the Northbrook headquarters.

Built the organization's first end-to-end Agile delivery analytics platform in Power BI — starting with an interim automated scorecard in Excel and PowerPoint that served as a critical lifeline for leadership, providing the first meaningful cross-program visibility while the full platform was being built. Scaled to a full platform integrating backlog, velocity, testing, defect, and release data across 100+ cross-functional teams and 10,000+ technology users — covering every layer of the Agile delivery lifecycle from planning through release. Agile methodology is not designed to produce fixed delivery dates — yet in a heavily regulated industry where product launches require state-level regulatory approval, leadership needed exactly that. Marketing needed a date to plan campaigns. Law and regulatory needed a date for state filings. The board needed a date for planning. Built a forecasting model that translated probabilistic Agile signals — velocity, throughput, and backlog burn — into reliable delivery projections that could align the entire enterprise around a single launch timeline without abandoning the Agile model the organization had committed to. The depth of Agility data flowing through the platform unlocked an unexpected capability — by leveraging work items, defects, and other activity data already being tracked, developed a novel methodology to automate project timekeeping, forming the foundation for an enterprise-wide automation solution projected to save $3M+ annually.

Building the platform also surfaced a significant data quality problem. Agile tooling data was inconsistently maintained across teams, undermining the reliability of delivery metrics and forecasting. Worked with the tooling team to enforce required fields in Digital.ai Agility and built a dedicated data validation dashboard for Scrum Masters to surface and resolve their own data quality issues in real time. The result was cleaner data flowing into the platform and a culture of data ownership at the team level that hadn't existed before.

Built an enterprise-wide Resource Roster platform that gave leadership their first consolidated view of the full ASC workforce, tracking 1,000+ contributors across 100+ teams by role, capability, and business unit. Integrated Excel-based RTE roster templates with HR and Digital.ai Agility data in Power BI to create a single governed source of truth, enabling leadership to identify staffing gaps, understand team capabilities, and know who to engage for each workstream.

The platform evolved through three stages as capabilities matured: Excel templates feeding Power BI, then SharePoint for more structured data collection, then fully automated feeds from Digital.ai Agility once the data was mature enough to support it, progressively eliminating manual effort at each stage.

Working in a rapid development model with daily 1-on-1 demos and iterative builds, designed and delivered Allstate's first enterprise analytics platform for the ASC product. Built a KPI framework of 10+ metrics covering policy performance, growth, and market penetration, and built ZIP code-level geospatial analysis in ESRI ArcGIS that enabled the first direct comparison of ASC performance against legacy products across markets and product lines. For the first time, executives had a clear, data-driven picture of how the new product was performing in the field — and how it compared against the existing book of business across markets and product lines.

Budget planning was driven by historical spend with an inflation adjustment — no one knew what specific products were being purchased, what the contract terms were, whether licenses were duplicated across the company, or whether enterprise agreements were being leveraged. Dug into raw purchase order data to build structured asset portfolios from the ground up — mapping every charge to a specific product, vendor, contract term, and renewal date. That foundation gave senior leadership their first real picture of what they were actually spending on software and why, enabling accurate forecasting, contract optimization, and the identification of $4.4M in cost savings through software usage analysis and licensing consolidation in partnership with Procurement.

Also built a multi-year financial forecasting model that leveraged EPM project plan data — remaining work, start dates, end dates, and resource assignments — integrated with SAP actuals to produce more accurate spend projections than traditional financial analyst estimates alone could provide. Project managers and portfolio managers were closest to the work and carried the most current view of delivery reality. By pulling that signal directly into the financial model, leadership got a more accurate picture of where the $1B+ technology portfolio was actually headed. A byproduct of the model was immediate: it exposed that a significant number of project plans were not being properly maintained — surfacing a data quality and governance issue that had been invisible until the financial model depended on the data being right.