Enterprise Data & AI Case Studies for Healthcare, Finance, Government

Explore real-world enterprise data, AI, and analytics case studies across healthcare, finance, government, retail, and non-profit sectors

Enterprise Data & AI Case Studies

These case studies demonstrate how Ataira designs and deploys enterprise data, cloud, and AI solutions across analytics modernization, automation, governance, and platform engineering initiatives.

Each engagement highlights business context, architectural approach, measurable outcomes, and implementation patterns that can be adapted to similar enterprise environments.

Healthcare Analytics Platform Case Study

Executive level monitoring of Traditional MIPS and Pathology MVP performance

Customer Background

A regional pathology group supporting multiple hospital and ambulatory sites needed a single view of Merit-based Incentive Payment System (MIPS) performance. Historically, Quality, Improvement Activities, Promoting Interoperability, and Cost results were tracked in separate spreadsheets, registry portals, and billing reports, making it difficult for leadership to understand incentive readiness or compare Traditional MIPS with emerging pathology-specific MVP options.

Objective

The objective was to build an executive dashboard that consolidates pathology MIPS performance at the practice level, supports a full switch between Traditional MIPS and the Pathology MVP, and clearly shows how readiness in each performance category contributes to the projected MIPS Final Score. The solution needed to be transparent, auditable, and aligned with College of American Pathologists guidance while preserving strict de-identification of patient data.

Approach

  • Data Integration and Normalization - Combined EHR, LIS, billing, and registry extracts into a curated analytics model keyed to MIPS numerators and denominators, low volume thresholds, and CAP pathology measure specifications.
  • Measure Mapping - Mapped pathology quality measures, Improvement Activities, Promoting Interoperability measures, and CMS cost indices to a consistent semantic layer, supporting both Traditional MIPS and the Pathology MVP scoring model.
  • AI-enabled Analytics - Implemented an AI agent that scans de-identified data for measure logic anomalies, missing synoptic fields, and scoring edge cases, providing coaching insights directly in the dashboard.
  • Executive Dashboards - Delivered the executive view shown above, with KPI cards, six visualizations, and an AI insights panel, all designed to help clinical and operational leaders understand where to invest effort before the submission deadline.

Outcomes

  • Consistent, practice-level view of pathology performance across Quality, IA, PI, and Cost in a single dashboard.
  • Improved clarity on the tradeoffs between remaining on Traditional MIPS versus moving to the Pathology MVP.
  • Reduction in manual MIPS reconciliation work through AI-assisted data validation and measure coaching.
  • Increased confidence that projected MIPS Final Scores accurately reflect underlying pathology quality and operational performance.

Related Services:

MIPS Pathology Requirements Tracking | Executive Dashboard

Aggregated MIPS readiness across Quality, Improvement Activities, Promoting Interoperability and Cost

Reporting Context: PY 2025 | Mode: Traditional MIPS
Projected MIPS Final Score
86.4
+5.2 vs prior year
Performance threshold: 82 points
Quality Performance
44.0 / 55
6 measures
Weighted decile based scoring across pathology quality measures
Improvement Activities
13.5 / 15
4 activities
Focus on care coordination, patient safety, and engagement
Promoting Interoperability
22.0 / 25
Active
HIE participation, public health reporting, and registry connections
Cost Performance
6.9 / 30
1 cost measure(s)
Indexes relative to CMS national benchmarks
Eligible Clinicians Meeting Low Volume Threshold
8 / 9
On track for full group participation
Based on PFS allowed charges and Medicare Part B volume
Annual Pathology Case Volume
18,420
37% complex cancer
Workload mix used to normalize quality and cost expectations
AI Data Quality Risk Index
Moderate
11 open, 63 resolved in 30 days
AI agent monitors numerator and denominator anomalies, missing fields, and measure logic drift
Quality Measure Decile Trend
Key pathology measures trended over rolling 12 months
Turnaround Time Compliance by Specimen Type
Cases meeting target reporting time compared with benchmark
Improvement Activities Completion Radar
Completion and documentation quality across required IA domains
Promoting Interoperability Score Breakdown
PI base score and remaining headroom across electronic exchange measures
CMS Cost Measure Index vs Benchmarks
Relative cost performance normalized to national average of 1.0
MIPS Final Score Trajectory
Historical and projected scores against CMS thresholds
AI Insights and Measure Coaching

An embedded AI agent continuously reviews de-identified, aggregated MIPS data for this pathology practice. It monitors numerator and denominator trends, compares measure performance to CMS benchmarks, and inspects structured fields such as synoptic cancer checklists, SNOMED coding, and critical value flags.

  • For Quality, the agent explains which pathology measures are driving the decile gains and highlights specimen types with rising turnaround variance.
  • For IA, it verifies documentation evidence for each Improvement Activity and surfaces gaps that would prevent CMS credit if audited.
  • For PI, it reconciles interface message volumes and registry acknowledgments with measure denominators to validate full PI scoring.
  • For Cost, it compares the pathology case mix and site of service trends against applicable cost measures to explain cost index movements.

Only aggregated, de-identified data is displayed. Patient level detail is never exposed in the dashboard and remains within secured clinical systems.

Current Coaching Focus
  • Tighten documentation for cross coverage of critical results to secure higher Quality deciles.
  • Increase electronic case exchange with tumor registries to sustain PI points under evolving CMS rules.
  • Model impact of additional Improvement Activities on the projected MIPS Final Score before the submission deadline.
*Dashboard simulated to respect customer confidentiality while reflecting realistic MIPS pathology objectives

Financial Portfolio Analytics Platform Case Study

Enterprise lending performance, profitability, and risk analytics for consumer portfolios

Customer Background

A $1B+ bank with a large indirect consumer portfolio needed a consolidated view of lending performance across originations, risk segments, and vendor channels. Rising delinquencies and charge-offs had driven annual consumer loan losses above $8M, significantly compressing margins and ROA.

Objectives

The engagement focused on implementing a Lending Performance Management framework that would: (1) quantify portfolio profitability by credit tier using a robust IRR-based model, (2) validate that scorecards and risk models were correctly ranking loss risk, (3) deploy static pool analysis for vintages, vendors, and collateral segments, and (4) support best-practice scorecards and dashboards for executives and examiners.

Approach

  • Profitability Review & IRR Modeling - Built a portfolio profitability model incorporating origination volumes, weighted yields, life-of-loan loss curves, repayment curves, fee income, and funding costs, with IRR and NPV comparison against alternative investments.
  • Static Pool Performance Analytics - Implemented static pool loss and repayment analysis by origination month, year, credit tier, collateral type, vendor, and member type to identify deteriorating vintages and high-risk segments early.
  • Risk Ranking Validation - Used static pool loss experience stratified by score tier to confirm that FICO and pooled risk models were correctly ranking loss risk and to pinpoint anomalies for underwriting and pricing adjustments.
  • Vendor & Channel Scorecards - Deployed vendor management reports and market- intelligence scorecards to compare vendor performance and benchmark the portfolio against peer lenders.

Outcomes

  • Reduced annual comsumer loan losses from over $8M to approximately $1M while maintaining competitive indirect volumes and portfolio growth.
  • Validated and fine-tuned risk-based pricing so that each credit tier covered expected losses and costs, improving risk-adjusted ROA across nine of ten tiers.
  • Demonstrated to regulators that scorecards and risk models were appropriately ranking risk, supported by static-pool-based risk-ranking validation reports.
  • Established a best-practices reporting program with monthly, quarterly, and annual scorecards for originations, performance, static pools, and vendor quality, enabling proactive portfolio steering rather than reactive policy changes.

Related Services:

Consumer Lending Performance | Executive Overview

Unified view of loan growth, profitability, and risk across direct and indirect lending channels

Total Consumer Portfolio
$1.85B
+9.8% YoY
Previous: $1.68B
Net Yield on Loans
4.3%
+0.35 pts
Previous: 3.95%
Delinquency Ratio (60+)
0.72%
-0.21 pts
Peer Avg: 1.52%
Net Charge-off Ratio
0.48%
-0.37 pts
Prior peak: 1.39%
Annual Loan Losses
$1.1M
Down from $8.0M
Risk-Adjusted ROA
1.15%
Target: ≥ 1.0%
Profitable Credit Tiers
9 / 10
1 sub-prime tier under review
Portfolio Risk Migration
+3.1%
Previous: +2.5%

Portfolio Yield & Loss Trend

Last 8 Quarters

Risk-Based Pricing

Profitability by Credit Tier

Static Pool Analysis

Cumulative Loss by Origination Year

Risk Ranking Validation

Loss Ratio by Score Tier

Credit Tier Performance Matrix

Profitability vs. Risk across credit segments

Static Pool Margin Heatmap

Credit Tier × Origination Year
*Dashboard simulated to respect customer confidentiality though based on actual project objectives

Non-Profit Analytics Platform Case Study

Aggregated Giving, Attendance, and Engagement Dashboard

Customer Background

A network of small and mid-sized non-profit organizations operating nationwide needed a unified and consistent way to measure financial health, giving trends, attendance patterns, and donor engagement. Each organization used different management and accounting systems, resulting in fragmented reporting and limited cross-organization insight.

Objective

The objective was to build a scalable cloud-based business intelligence platform capable of integrating diverse non-profit data sources-including QuickBooks, SQL Server, and non-profit management systems-into a standardized enterprise data model. The platform would enable leaders to monitor trends, benchmark performance across organizations, and drive improved donor engagement and attendance consistency.

Approach

  • Data Integration - Connected QuickBooks, membership databases, and legacy non-profit systems using secure API integrations and Azure Data Factory pipelines.
  • Standardized Data Model - Unified attendance, giving, donor segmentation, and retention metrics into a cross-organizational schema.
  • KPI Framework - Defined consistent measures for weekly attendance, giving growth, donor tiering, digital giving ratios, and year-over-year comparisons.
  • Interactive Dashboards - Built Power BI dashboards supporting forecasting, cohort analysis, and multi-organization benchmarking.
  • Multi-Phase Expansion - Two-year iterative delivery including new datasets, automation improvements, and additional KPI categories.

Outcomes

  • Single source of truth for giving, attendance, and engagement data across multiple non-profits.
  • Enhanced donor insight with tier segmentation and multi-year contribution trends.
  • Improved decision-making through standardized KPIs and real-time visibility.
  • Operational efficiency by eliminating manual QuickBooks and attendance reporting.
  • Scalable onboarding allowing new non-profits to join the platform with minimal setup.

Related Services:

Organizational Cross Functional Performance | Executive Overview

Aggregated insights across Attendance, Giving, and Donor Engagement

Avg Weekly Attendance
2,430
+6.1%
vs prior year
Total Active Donors
482
+3.4%
Rolling 12-month count
YTD Giving
$1.18M
+8.7%
Across all giving channels
Percent Online Giving
29%
+1.2%
Digital engagement indicator
Donor Retention Rate
84.3%
+2.1%
Retained donors vs prior period
New Donors (YTD)
74
+15.5%
Based on onboarding growth
Recurring Giving Share
21%
+0.8%
Online recurring commitments
Engagement Index
78
+5 pts
Composite of 5 engagement pillars

Attendance Trend (Adult vs Youth)

Rolling 12 months with seasonal lift

Giving Trend vs Goal

Monthly goal attainment with variance bands

Donor Tier Segmentation

Distribution of givers by engagement tier

Donor Retention vs New Donors

Month over month view of relationship health

Giving Channel Mix

Online, in-person, and recurring giving by month

Engagement Index Radar

Holistic view of attendance, serving, and digital reach
*Dashboard simulated to respect customer confidentiality though based on actual project objectives

Retail Financial Analytics Platform Case Study

Unified financial insight across consolidated multi-entity retail operations

Customer Background

A national retail cooperative consisting of luxury brands required a unified way to view consolidated financial performance across their independently operated entities. Fragmented reporting, disconnected systems, and inconsistent data quality created barriers to enterprise-level financial transparency.

Objective

The objective was to build an internal financial analytics platform capable of aggregating sales, cost, and profitability data across dozens of entities. The platform needed to enforce role-based access, support multi-entity reporting, and deliver standardized KPIs and P&L metrics.

Approach

  • Data Integration - Connected accounting systems and operational sources using secure connectors.
  • Data Modeling - Designed a unified semantic model supporting financial, sales, and profitability KPIs.
  • Power BI Dashboards - Delivered iterative dashboards for sales variance, margin analysis, and benchmarking.
  • Governance - Implemented access controls, metric definitions, and validation workflows.

Outcomes

  • Standardized enterprise-wide visibility across financial and operational metrics.
  • Accurate variance reporting and real-time profitability monitoring.
  • Improved decision-making for executives, finance, and entity owners.
  • Scalable foundation for future cloud-native financial data integration.

Related Services:

Aggregate P&L Financial Performance | Executive Overview

Unified financial insight across multi-entity retail operations

Sales $
$203,874,082
+15.72%
Previous: $176,184,835
Costs $
$140,318,314
+16.51%
Previous: $120,436,176
Gross Profit $
$63,555,768
+14.36%
Previous: $55,574,865
Gross Margin %
31.2%
-0.40%
Previous: 31.6%
Sales $ Variance
$27,689,247
Costs $ Variance
$19,882,138
Gross Profit $ Variance
$7,807,109
Gross Margin % Variance
-0.5%

YTD Variance - Gross Profit & Margin %

Rolling 12-Month Profitability Trend

Company comparison vs consolidated performance

Cost Efficiency Index vs Sales Productivity

High performing and underperforming entities
*Dashboard simulated to respect customer confidentiality though based on actual project objectives

Government Analytics Platform Case Study

Statewide Sales Performance and Forecasting Dashboard

Customer Background

The state lottery commission is responsible for generating supplemental funding for public education through lottery sales. Multiple operational systems, legacy reporting tools, and departmental data silos limited the ability to get a unified, timely view of sales performance, retailer effectiveness, and product mix across the state.

Objective

The engagement objective was to design and implement a statewide business intelligence platform that consolidated disparate source systems into a centralized data warehouse and delivered transparent analytics to internal departments and executive leadership. The platform needed to support mission critical decisions aligned with the lottery commission's mandate to maximize contributions to public education.

Approach

  • Requirements and stakeholder alignment - Led workshops with Sales, Finance, Retail Operations, and Executive teams to validate reporting needs, KPIs, and forecasting requirements.
  • Data warehouse and architecture design - Built a scalable SQL Server data warehouse and semantic model to support sales, validation, product portfolio, and retailer performance analytics.
  • Integration and ETL - Implemented robust data ingestion processes to unify transactional, historical, and retailer data into a single governed platform.
  • Interactive dashboards - Delivered executive and departmental dashboards for statewide sales visibility, performance trending, and scenario based forecasting.
  • Governance and lifecycle management - Established data quality rules, metric definitions, and release processes to ensure accuracy and long term sustainability.

Outcomes

  • Centralized, trusted view of statewide lottery sales and product performance.
  • Improved forecasting accuracy using unified historical and near real time datasets.
  • Retailer coverage and performance analytics supporting targeted outreach and optimization.
  • Executive dashboards aligned with the lottery commission's mission to maximize education funding.
  • A scalable analytics platform capable of supporting future initiatives and new game offerings.

Related Services:

State Government Lottery | Executive Overview

Unified insight across validation sales, retailer performance, portfolio mix, and education funding

Total Validation Sales (YTD)
$15.3M
+8.4%
Previous YTD: $14.1M
Avg RoS per Retailer
$74.20
+2.1%
Based on active retail locations
Top Game Sales (YTD)
$3.8M
+11.6%
Flagship scratcher game performance
Retailer Coverage
96.3%
+1.1%
Counties with active lottery presence
Scratchers Sales (YTD)
$9.4M
+9.2%
Instant-win portfolio performance
Draw Game Sales (YTD)
$5.9M
+7.1%
Lotto and daily draw products
Prize Payout Ratio
62%
+0.8 pts
Prizes as a share of sales
Education Contribution (YTD)
$3.7M
+6.5%
Net transfers to education funds

Monthly Validation Sales and Rolling Average

Monthly Sales vs Target and Variance %

Scratcher Game Portfolio Mix (YTD)

Retailer Performance by Quartile

Regional Sales Intensity by Channel

YTD Allocation of Lottery Sales to Key Uses

*Dashboard simulated to respect customer confidentiality while reflecting realistic lottery performance and funding objectives.