Check source tables, relationships and quality.
MyDataStories / What we solve
Make database reporting accurate and repeatable
We improve SQL queries, join logic, transformations and reconciliations so the same question gets the same answer.
Build documented SQL transformations and checks.
Expose reliable reporting tables and extracts.
We agree measures and definitions with each organisation. Any demonstration charts use illustrative data, not client performance claims.
Discuss your data →SQL & Data Platforms
Bring disconnected systems into dependable reporting
We connect SQL-based sources and business systems, build consistent transformations, and establish checks that make datasets usable across analytics teams.
- SQL extraction and performance-aware queries
- ETL/ELT pipelines and repeatable transformations
- Data lineage, reconciliation and exception monitoring
SQL — data validation trend
Successive data-quality check runs
Key / units: Validation success rate (%) across eight runs. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
SQL validation and reconciliation. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
What we solve
Unreliable extracts, slow queries and datasets without clear ownership.
We build SQL transformations, validation queries, documented data models and extract processes that are easier to maintain and audit.