Data Profiling
Know what your data actually contains before it costs you a decision.
Bad data is invisible until it causes a problem. We conduct systematic data profiling engagements — assessing data quality, documenting schema and lineage, detecting anomalies, and establishing data governance frameworks — giving you confident visibility into the state of your most critical data assets before they underpinn AI models, BI reports, or operational decisions.
What you get
What's included in our
Data Profiling engagement
Comprehensive Data Quality Assessment
A quantified assessment of your data quality across five dimensions: completeness, uniqueness, validity, consistency, and timeliness — with specific field-level findings, root cause analysis for quality failures, and a prioritised remediation plan ordered by the business impact of each quality issue.
Schema Documentation and Data Catalogue
A complete data catalogue documenting every table, field, data type, business definition, source system, and data owner — creating the institutional knowledge that currently lives only in the heads of two engineers who were hired three years ago. Searchable, maintainable, and version-controlled.
Anomaly Detection and Ongoing Monitoring
Automated anomaly detection rules that flag data quality violations in real time — unexpected null rates, value distribution shifts, referential integrity failures, and freshness violations — so data quality issues are caught at ingestion, not discovered weeks later when a business decision has already been made.
Our process
How we deliver Data Profiling
Data Asset Discovery and Cataloguing
We identify all data sources, databases, and data stores in scope — including the shadow IT spreadsheets that are actually running important business processes. We document each source's ownership, refresh cadence, downstream dependencies, and estimated business criticality.
Statistical Profiling and Quality Analysis
We run automated profiling across all in-scope data to generate completeness rates, uniqueness profiles, value frequency distributions, and pattern analysis. Findings are reviewed by our data quality analysts who interpret statistical findings in business context rather than just reporting raw numbers.
Catalogue Creation and Lineage Mapping
We build the data catalogue with business-language definitions for every data entity, field-level descriptions, source-to-consumption lineage diagrams, and data owner assignment. The catalogue is set up in your chosen tool — Atlan, Collibra, DataHub, or Notion — and populated with profiling findings.
Governance Framework and Quality Rules Implementation
We define data governance policies for ownership, classification, retention, and access control. Automated data quality rules are implemented in your pipeline infrastructure, with a quality scorecard dashboard that gives your data team ongoing visibility into the health of your data estate.
Stack
Technologies we use
Why Palsoro for Data Profiling
We Surface the Issues Data Teams Have Learned to Ignore
Every data team has normalised some level of data quality problems. Our external profiling process uses objective statistical analysis to surface issues that internal teams have stopped noticing — including the ones with the highest downstream business impact.
Business Context, Not Just Technical Counts
A 3% null rate in a low-priority field is irrelevant. A 3% null rate in the customer ID field that feeds your revenue attribution is a crisis. We interpret every data quality finding in its business context, so your team knows which problems to fix this week and which can wait.
Deliverables That Don't Expire on Delivery
A data catalogue that's already outdated on delivery day is worse than no catalogue. We build governance frameworks that include process, ownership, and tooling — so your catalogue stays current as your data estate evolves, not just as a snapshot from the week we finished the project.
We profile relational databases (PostgreSQL, MySQL, SQL Server, Oracle), cloud data warehouses (BigQuery, Snowflake, Redshift), flat files (CSV, Parquet, JSON), and SaaS application data accessed via API or direct database connection. If the data is accessible, we can profile it.
Data profiling is about understanding what data you have and its quality. BI is about building reporting systems on top of that data. Profiling is often the right prerequisite to BI — you don't want to build dashboards on data you don't understand or trust. Many clients start with profiling and proceed to BI once the data foundation is established.
For small-to-mid-size data teams, we typically recommend DataHub (open source) or a well-structured Notion workspace. For enterprise data teams with formal governance requirements, Atlan or Collibra provide the workflow, policy enforcement, and integration capabilities that justify their cost. We help you choose and configure the right tool for your maturity level.
Absolutely — and we strongly recommend it before any AI/ML project. Model quality is fundamentally constrained by training data quality. Profiling identifies biases, missing values, encoding inconsistencies, and distribution issues in your training data before they become mysterious model performance problems.