Data Foundations for AI
4–12 weeks • $25k–$150k+
Build the data infrastructure that enables AI success. Clean data, reliable pipelines, and proper governance are essential for effective AI implementations.
Why Data Foundations Matter
"Garbage in, garbage out" - AI models are only as good as the data they're trained on. Poor data quality, inconsistent pipelines, and inadequate governance lead to unreliable AI systems and failed implementations.
Our data foundation services ensure your AI initiatives are built on solid, reliable data infrastructure.
Core Services
Data Pipeline Development
- ETL/ELT pipelines for data ingestion and transformation
- Real-time streaming for immediate data processing
- Batch processing for large-scale data operations
- Data warehouse/lake architecture and implementation
Data Quality & Governance
- Data quality monitoring and validation
- Master data management and deduplication
- Data lineage tracking and documentation
- Governance policies and compliance frameworks
Data Architecture & Storage
- Cloud data platforms (AWS, Azure, GCP)
- Database optimization for AI workloads
- Data lake/warehouse design and implementation
- Performance tuning and cost optimization
Observability & Monitoring
- Data pipeline monitoring and alerting
- Quality metrics and dashboards
- Performance tracking and optimization
- Incident response and troubleshooting
Data Sources We Handle
Business Systems
- ERP systems (SAP, Oracle, Microsoft Dynamics)
- CRM platforms (Salesforce, HubSpot)
- Financial systems (QuickBooks, accounting software)
- HR and payroll systems
Operational Data
- IoT sensors and device data
- Transaction logs and event streams
- Customer interaction data
- Operational metrics and KPIs
External Data
- Market data and economic indicators
- Weather and environmental data (critical for Park City)
- Social media and review data
- Third-party APIs and data feeds
Unstructured Data
- Documents and contracts (PDF, Word, scanned documents)
- Images and media files
- Email and communication data
- Website and user behavior data
Technical Implementation
Data Ingestion Layer
- Source system connectors and APIs
- Change data capture for incremental updates
- Data validation and cleansing at ingestion
- Schema evolution handling
Processing & Transformation
- Data cleansing and standardization
- Business rule application and validation
- Aggregation and summarization for analytics
- Machine learning feature engineering
Storage & Access Layer
- Data lake for raw data storage
- Data warehouse for structured analytics
- Real-time databases for immediate access
- Caching layers for performance
Consumption Layer
- API development for data access
- Self-service analytics tools
- AI/ML model training pipelines
- Dashboard and reporting interfaces
Quality Assurance Framework
Data Quality Metrics
- Completeness - are all required fields present?
- Accuracy - is the data correct and up-to-date?
- Consistency - is data consistent across systems?
- Timeliness - is data available when needed?
Validation Rules
- Format validation (email, phone, address formats)
- Range and logic checks (age ranges, date logic)
- Cross-reference validation (matching between systems)
- Business rule compliance (custom validation rules)
Monitoring & Alerting
- Automated quality checks with alerts
- Trend analysis for quality degradation
- Root cause analysis for data issues
- Corrective action workflows
Security & Compliance
Data Privacy
- PII identification and masking
- Consent management and tracking
- Data retention policies and automation
- Right to erasure (GDPR/CCPA compliance)
Access Control
- Role-based access control (RBAC)
- Data classification and labeling
- Audit logging for all data access
- Encryption at rest and in transit
Compliance Frameworks
- GDPR, CCPA, HIPAA compliance as needed
- Industry-specific regulations (finance, healthcare)
- Data residency requirements
- Cross-border data transfer rules
Pricing & Packages
Data Pipeline Setup
$25k–$50k • 4–6 weeks
- Basic ETL pipelines for 2–3 data sources
- Data quality monitoring
- Documentation and training
- 3 months support
Data Foundation Platform
$60k–$100k • 8–10 weeks
- Comprehensive data platform
- Multiple data sources integration
- Advanced quality and governance
- API development
- 6 months support
Enterprise Data Lake
$120k–$150k+ • 12 weeks
- Large-scale data lake implementation
- Advanced analytics capabilities
- AI/ML pipeline integration
- Full governance and compliance
- 12 months managed support
Park City Business Applications
Tourism Data Platforms
"Our data foundation handles booking data, guest preferences, seasonal patterns, and market intelligence to power personalized recommendations."
Hospitality Analytics
"Integrated data from reservations, operations, finance, and guest feedback enables predictive analytics for revenue optimization."
Construction Data Management
"Project data, supplier information, regulatory compliance, and financial metrics are unified for better project management and forecasting."
Local Business Intelligence
"Customer data, inventory, sales, and operational metrics provide comprehensive business intelligence for decision making."
Implementation Process
Assessment & Planning (Weeks 1-2)
- Current state analysis - what data do you have?
- Requirements gathering - what do you need for AI?
- Architecture design - how should it be structured?
- ROI projection - what's the expected value?
Foundation Development (Weeks 3-8)
- Infrastructure setup - cloud platforms and tools
- Pipeline development - data ingestion and processing
- Quality frameworks - validation and monitoring
- Security implementation - access controls and encryption
Testing & Deployment (Weeks 9-10)
- Integration testing - end-to-end validation
- Performance testing - scalability and reliability
- User acceptance - business user validation
- Production deployment - go-live and monitoring
Optimization & Support (Weeks 11-12+)
- Performance tuning - optimization and cost management
- User training - adoption and best practices
- Ongoing monitoring - health checks and improvements
- Evolution planning - future enhancements
Common Data Foundation Mistakes
Starting with Technology
"Don't buy tools first - understand your data requirements and use cases, then select appropriate technology."
Ignoring Data Quality
"Data quality issues compound over time. Start with quality frameworks from day one."
Underestimating Governance
"Data governance isn't optional for AI success. Poor governance leads to unreliable models and compliance issues."
Lack of Monitoring
"Data pipelines require ongoing monitoring. Set up proper observability from the beginning."
Ready to Build Your Data Foundation?
Free data assessment to evaluate your current data landscape and identify foundation requirements.
Or contact us: info@parkcityaisolutions.com