How EVA Helped a U.S. Consumer Research Company Validate & Unify Unstructured Data

Client Snapshot

• Industry : Consumer Research
• Organization Type : Established research agency (top 20 in the U.S.)
• Client Scale : Serving Fortune 500 companies & major brands
• Location : United States
• Engagement Duration : 12 months (initial contract period)

Business Challenge

Before partnering with EVA, the client faced:

Data Accuracy & Usability Issues

• Unstructured and inconsistent survey data formats (text, spreadsheets, PDF, scanned forms)
• Frequent data errors requiring manual rework
• Data professionals were spending 60% or more of their time cleaning and organizing data, which significantly limited the time available for insights and strategic analysis

Operational Bottlenecks

• Long processing times (7–14 days average turnaround per survey batch)
• Internal teams were overloaded with manual validation tasks
• Limited ability to scale during peak project cycles

Why the Client Chose EVA

The client selected Express Virtual Assistant (EVA) because:

• EVA delivered specialized data operations support tailored to research workflows
• EVA's team demonstrated expertise in structured validation and collation
• Outsourcing promised cost savings vs. building an internal team

Scope of Engagement

Tasks handled by EVA included:

• Validating field data: Manual checking of entries and automated validation routines
• Collating multi-format inputs: Converting documents into structured datasets
• Standardizing formats: Transforming disparate sources into unified, clean datasets

Proper validation ensures data consistency and reliability for analytics and reporting.

Tools & Methods Used

• Hybrid validation workflows (AI-assisted tools + expert manual QA)
• Standardized data capture and reconciliation processes
• Routine quality checks at multiple validation stages

Onboarding & Execution Process

EVA's delivery included:

1. Process definition: Clear workflows for unstructured data handling
2. Team alignment: Consistent coordination with internal research staff
3. Quality frameworks: Built reusable validation rules, checkpoints, and templates

These steps ensured:

• Seamless integration with client operations
• Standardized, repeatable procedures
• Error reduction and an increase in usable data quality

Results & Outcomes

Within the 12-month engagement period:

Efficiency & Quality Gains

• Data validation accuracy improved to ~99%
• Turnaround times reduced from 10 days to ~3–4 days
• Operational workload decreased by ~40–60%

Cost Savings

• Cost reduction: Estimated 30–50% lower cost compared to internal hiring + tool licensing
• Savings driven by:
  • Lower labor and overhead expenses
  • No recruitment or software investment
  • Avoided rework costs

Strategic Impact

• Unstructured data reliably converted into structured, analysis-ready formats
• Internal team redirected ~50% of time to insights and interpretation
• Faster reporting cycles boosted responsiveness for Fortune 500 clients

Security, Compliance & Risk Management

During the project:

• Survey data were handled with documented, repeatable processes
• EVA integrated multi-stage quality checks to minimize errors
• Sensitive inputs were reconciled consistently and securely

Engagement Status & Continuity

• Engagement remains active with expanded data operations support
• Ongoing transition of operational tasks to EVA specialists
• Continued delivery of structured reports for stakeholder consumption

Key Takeaways

This case demonstrates how a data-heavy U.S. research organization achieved:

• Reliable structured datasets from unstructured inputs
• Internal team capacity reclaimed for strategic work
• Significant cost and time savings without full-time hiring

Need help validating and structuring complex datasets? Talk to a data operations specialist today.

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