Case Studies — CognyraData

Summary

Overview — consolidated, auditable, and decision-ready

Status

Production-ready patterns

Below each case opens into a focused narrative: Business Problem → Solution (architecture & process) → Key Deliverables → Results & Impact → Next Steps. Click any case to expand; use the right-side navigator to jump sections.

Data Center Construction — Cost Management

Portfolio financial control, monthly MBR consolidation, multi-currency FX conversion, Monte-Carlo QRA

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Business Problem

A Fortune 500 software company running hundreds of global data center projects could not efficiently consolidate monthly financials. Cost managers and schedule managers were distributed globally; each project (~$500M average) produced siloed MBRs, making EAC/BAC reconciliation and portfolio-level executive reporting extremely manual and slow. Risk capture was fragmented, so monthly Quantitative Risk Assessment (QRA) was inconsistent.

Solution — Architecture & Process

Automated ingestion: Excel cubes, ERP extracts, and unstructured artifacts (images) into an Azure data lake.
Processing: Azure Databricks pipelines to normalize cost elements, map currency → USD (monthly FX table) and compute EAC/BAC rollups per project and portfolio.
Risk platform: Power Platform app to centralize risk entries (local currency recorded; calc'd to USD), attachments, mitigations, and monthly QRA orchestration.
Monte-Carlo: Parameterized Monte-Carlo simulations run in Databricks to produce P50/P80 EAC distributions and integrated into Power BI dashboards.

Deliverables

End-to-end ingestion + transformation specs.
Power BI executive dashboard with drill-to-project pages and exportable MBR packs.
Power Platform risk capture app and risk-to-dashboard connector.
Monte-Carlo simulation module and documented param set.

Results & Impact

Automated repetitive monthly tasks (eliminating manual consolidation work), standardized risk capture and QRA process, and provided SLT with unified, auditable EAC/BAC reporting. The solution reduced time-to-report and improved confidence in forecast ranges through probability bands.

Power BI Azure Databricks Power Platform Monte-Carlo

Next Steps (recommended)

Operationalize monthly automated anomaly checks and SLA alerts for large forecast deltas.
Integrate procurement spend-flows for earlier COGS visibility.

Data Center Construction — Schedule Management

XER ingestion, baseline vs current consolidation, portfolio-level schedule analytics

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Business Problem

General contractors used Primavera P6; PMs manually compiled monthly schedule reports and presented leadership slides. No centralized owner-perspective solution existed to compare baseline vs current across projects at a portfolio level.

Solution — Architecture & Process

Upload interface for XER exports (secured upload) with metadata tagging per project.
Metadata-driven ETL to normalize activities, calendars, constraints and baselines into a schedule data model.
Analytics layer providing baseline vs current heatmaps, critical path changes, and schedule risk overlays.

Deliverables

XER ingestion service & uploader (with validation and parsing).
Schedule data model & comparison reports.
Dashboard widgets: heatmaps, delay trees, and drill-through activity lists.

Results & Impact

Leadership gained portfolio-level schedule visibility, reduced PM overhead for monthly reporting and received consistent baseline-change governance.

XER Parser Schedule Heatmaps

Next Steps

Integrate resource leveling and cost-loaded schedule overlays for finance alignment.

Data Center Construction — Equipment Commissioning

ML-based velocity prediction for staged commissioning (L1→L5)

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Business Problem

Each data center contains 1,500+ equipment items requiring commissioning. The business needed reliable projections of completion based on current velocity and to model what velocity would be required to meet target dates.

Solution — Architecture & Process

Historical commissioning datasets were used to train models that learn velocity per equipment class and per commissioning stage (L1→L5).
Model outputs supply projected completion dates and 'required uplift' scenarios (e.g., increase daily completions by X%) to meet targets.
Dashboard surfaces percentiles (P50/P80) and scenario toggles for leadership to evaluate resource trade-offs.

Deliverables

Predictive velocity model and inference service.
Commissioning dashboard with scenario manager and recommended resource changes.

Results & Impact

Enabled better resource planning and reduced surprise schedule overruns by surfacing realistic completion windows and resource uplift recommendations.

ML Models Scenario Manager

Data Center Construction — Project Cost Estimation

Secure pipeline, permissioned analytics and vendor comparison

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Business Problem

Vendor estimates lived in secure Excel silos and were not centralized. Leadership needed a secure way to build realistic early-stage project cost estimates and perform vendor comparisons while preserving data confidentiality.

Solution — Architecture & Process

Secure ingest pipeline (tokenization & field-level encryption) for sensitive estimate spreadsheets.
Permissioned analytics surface so only authorized users can run comparisons and scenario analysis.
Vendor-comparison module allowing normalized cost metrics without exposing raw source files.

Deliverables

Secure staging + tokenization library.
Normalized vendor estimate dataset and controlled analytics endpoint.
Authorized dashboards with audit trails.

Results & Impact

Provided realistic early-stage estimates, improved vendor negotiation leverage and allowed leadership to compare bids consistently while preserving confidentiality and auditability.

Secure Pipeline Vendor Analytics

How I approach engagements (short)

Start with a 2-week diagnostic to inventory systems, owners and data quality.
Deliver a minimal viable ingestion + dashboard in 6–8 weeks to prove value.
Iterate with SLT and PMO: automate, secure, and scale.