Data Is the Foundation
Every AI system depends on data: training data for models, input data for predictions, and feedback data for improvement. Most organizations have more data than they realize, but it is scattered across systems, inconsistently formatted, and poorly documented. Our data assetsevaluation gives you a clear inventory of what you have, what condition it is in, and what gaps need to be addressed before AI can deliver reliable results.
Schema Analysis
We examine your database schemas, file structures, and API response formats. Normalization levels, data types, constraint enforcement, and indexing patterns all affect what AI can do with your data.
Completeness Scoring
We measure null rates, missing fields, and sparse records across your critical datasets. A CRM with 40% missing email addresses tells a different story than one with 95% coverage.
Freshness Tracking
Stale data produces stale predictions. We audit update frequencies, sync lag between systems, and identify datasets where time-sensitive decisions rely on outdated information.
Governance Gap Assessment
We evaluate access controls, audit trails, retention policies, and compliance posture. GDPR, CCPA, HIPAA, and industry-specific regulations constrain how data can be used for AI training and inference.
Evaluation Process
Catalog
Inventory all data sources
Profile
Measure quality metrics
Map Lineage
Trace data origins and flows
Score
Rate readiness per use case
Catalog
Inventory all data sources
Profile
Measure quality metrics
Map Lineage
Trace data origins and flows
Score
Rate readiness per use case
Data Assets Evaluation
Data Lineage Mapping
Understanding where data comes from is as important as understanding what it contains. Data lineage mapping traces every dataset from its point of origin through every transformation, copy, and aggregation to its final resting place. This reveals duplication, inconsistency, and single points of failure that would undermine AI deployments.
Source identification. We trace each dataset back to its original source: user input, sensor reading, API call, manual entry, or third-party feed. Understanding provenance lets us assess reliability and establish data contracts for AI pipelines.
Transformation tracking. Data rarely moves between systems unchanged. ETL jobs, stored procedures, application logic, and manual spreadsheet manipulation all transform data in ways that affect downstream AI accuracy. We document every transformation in the chain.
Quality propagation. A data quality problem at the source propagates through every downstream system. We identify where quality degrades in the pipeline and recommend validation checkpoints that catch problems before they reach AI models.
Who Needs This
Organizations considering AI for customer analytics, demand forecasting, anomaly detection, or any data-intensive use case need a clear picture of their data assets first. This evaluation is especially valuable for companies with data spread across multiple SaaS platforms, on-premises databases, and legacy systems.
Contact us at ben@oakenai.tech to schedule your data assets evaluation.