Data Quality & Profiling
Trust in data starts with knowing its actual state. Is your data worthy of trust? And even if it is, can you demonstrate that to the skeptics who hesitate to base important decisions on it? Data profiling answers both questions, and structural quality management keeps the answer positive.
In practice
Profiling is the diagnosis. It gives you insight and statistics on your data: completeness, validity, uniqueness, consistency, freshness. It uncovers and quantifies quality issues you suspected, finds ones you didn't, or confirms that your perceived quality is real, which is just as valuable when you need others to trust your platform.
Quality management is the cure. We help you move from one-off cleanups to structural quality: rules that codify what 'good' means for your critical data, monitoring that measures it continuously, remediation workflows that route issues to the people who can fix them at the source, and ownership so quality has a name attached to it.
The business case is unusually concrete. Poor quality shows up as failed deliveries, duplicate customer contacts, rework in finance, unreliable reports, and, increasingly, AI models and Copilot answers built on flawed inputs. Most of the measurement and much of the remediation can be automated, which is why even modest quality programs tend to pay for themselves quickly.
Data profiling quantifies the actual state of your data; data quality management turns that measurement into rules, monitoring, remediation and ownership, so data becomes accurate, complete and trustworthy, and stays that way.
The symptoms we see most often
- Reports get manually 'corrected' before every management meeting.
- Mandatory fields aren't enforced on entry, records are duplicated, and downstream teams have learned to work around it.
- Data issues are found by the business, weeks after they happened, instead of by monitoring.
- Cleanup projects succeed, and six months later the same problems are back.
- Nobody can say whether quality is getting better or worse, because nobody measures it.
Recurring quality problems are a process problem wearing a data costume. The fix is structural, not another cleanup.
Our services
Data profiling & assessment
A quantified baseline of your critical data: where the issues are, how big they are, and what they cost, turning gut feeling into a prioritized backlog.
DQ rules & monitoring
Quality rules defined with the data owners who know what 'good' means, implemented as automated, continuous monitoring integrated in your data platform rather than a separate silo.
Remediation workflows
Issues routed to the people who can fix them at the source, with clear priorities and follow-up, so problems get solved once instead of patched forever downstream.
Quality dashboards & KPIs
Quality made visible to owners and management: trends per domain, rule and system, turning data quality from an IT concern into a managed business metric.
How an engagement runs
Fair warning: this is business-heavy work, not a purely technological project. The tooling is the easy part; the value comes from workshops with your data owners, decisions about definitions, ownership and priorities, and the change management that makes new habits stick. We facilitate exactly that, alongside the implementation.
Profile & assess
Baseline the critical data, quantify the issues, prioritize by business impact.
Define rules with owners
Codify what 'good' means, together with the people accountable for it.
Monitor & remediate
Continuous automated measurement, with workflows that fix issues at the source.
Report & improve
Dashboards and KPIs that keep quality visible and the improvement loop turning.
Typical deliverables
What we work with
We work with leading data quality platforms, integrated with data platforms such as Microsoft Fabric and Databricks, so monitoring lives where your data lives. Technology choices always follow your architecture and ambitions.
- Soda
- Ataccama
- Informatica
- Microsoft Purview
- Microsoft Fabric
- Databricks
Frequently asked questions
Should we clean the data first or set up monitoring first?
Together. Cleanup without monitoring decays within months; monitoring without remediation just documents decline. We baseline, fix the highest-impact issues, and switch on monitoring for exactly those rules so the gains lock in.
Who should own data quality: IT or the business?
The business owns what 'good' means and is accountable for it; IT and data teams own the measurement and tooling. Our rule definitions are always co-created with data owners for precisely this reason.
Can quality monitoring live inside our data platform?
Yes, and it should. Tools like Soda integrate natively with Fabric and Databricks pipelines, so checks run where the data flows and failures surface immediately rather than in a separate tool nobody opens.
Know where you stand in a few weeks
The fastest first step is a Data Governance Maturity Scan: an objective view of your current maturity, a benchmark against peers, and a prioritized roadmap.