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Data Quality & Validation
A measurable, evidence-based view of how healthy your data really is. We profile it, test it against rules, compare it across systems and report quality in scorecards leadership can act on.
Overview
Turn arguments about bad data into numbers you can act on
Data Quality and Validation gives you a measurable, evidence-based view of how healthy your data really is. We profile it, test it against rules, compare it across systems and report quality in scorecards leadership can act on. It is built for regulated and high-growth organisations where poor data quietly drives bad decisions, failed reports and compliance exposure, and where “the data is a mess” is a widely shared complaint that no one has ever managed to quantify.
Most data quality problems persist because they are argued about rather than measured. Without numbers, every team has its own anecdote and no one can agree what to fix first. Baknet keeps accountability in one place across security, data assurance, software QA and people, so data quality is owned and tracked rather than passed around. Our method is evidence-led: repeatable findings, fixes confirmed closed, one included retest and a daily view of progress. As an organisation certified to ISO/IEC 27001 and ISO 9001, we bring measured rigour to work that too often runs on opinion.
What it includes
Four capabilities, one accountable service.
Data Profiling and Baseline Assessment
We analyse patterns, distributions and anomalies to establish measurable quality benchmarks, giving you a factual starting point instead of a general sense of unease. Profiling surfaces the surprises early: the field that is half empty, the format that changes mid-table, the values that should never occur but do. That baseline becomes the reference every later improvement is measured against.
Rule-Based Quality Checks
We validate for nulls, duplicates, referential integrity and domain constraint violations, translating what the business expects into executable checks. Each rule is repeatable, so the same test can be run again after any fix or change to confirm the issue has genuinely gone. As defects reveal blind spots, we extend the rule set to cover them.
Cross-System Consistency Checks
When the same entity lives in several systems, they drift apart quietly until two reports disagree. We compare data across systems to surface mismatches, drift and reconciliation breaks, showing exactly where and by how much systems fall out of step. This is what turns “our numbers never match” into a specific, fixable list.
Data Quality Reporting
We bring the findings together in quality scorecards, with issue tracking and structured root-cause analysis support. Rather than a static report that ages on a shelf, you get dimension-level scores tied to open issues and named owners, so quality becomes something the organisation can manage over time.
How we deliver
Delivering Data Quality & Validation
Daily progress updates run throughout and one retest round is included, so fixes are verified rather than assumed.
01
Profile and Baseline
Analyse patterns, distributions and anomalies to establish the measurable starting point.
02
Codify the Rules
Translate business expectations into executable checks for nulls, duplicates, integrity and domains, so quality is defined in terms the business recognises.
03
Test Within and Across Systems
Run rule-based and cross-system consistency checks to surface defects, drift and breaks wherever they hide.
04
Scorecard and Root-Cause
Publish quality scorecards, track issues and support structured root-cause analysis through to closure.
It is the same disciplined path we use across the practice. See how we work on How We Engage.
Why it pays off
Why Data Quality & Validation pays off
Measurable Quality
Scorecards turn vague complaints about bad data into specific, trackable, improvable metrics. With quality expressed as concrete numbers, teams can set targets, monitor progress and demonstrate genuine improvement rather than just asserting it.
Root-Cause Resolution
Structured root-cause analysis stops recurring data issues instead of endlessly patching symptoms. Fixing the underlying cause once means the same defect stops reappearing across every downstream report, dashboard and process.
One Consistent Truth
Cross-system checks eliminate the silent drift that makes systems disagree. When every system reconciles to the same values, teams stop arguing over which source is correct and act on one agreed picture.
Lower Data Costs
Early detection avoids the downstream rework, bad decisions and compliance exposure that poor data causes. Catching issues at the source is far cheaper than correcting them once they have spread through reports and operations.
Data quality you can measure, manage and defend.
What you receive on every engagement
Daily Progress Updates
Quality Scorecards
Results of Executed Checks
One Included Retest
Questions, answered
Frequently asked
How do you decide which rules to test against?
Business expectations drive the rule set. During profiling we translate what your teams believe the data should look like into executable checks, then extend them as defects reveal blind spots, so coverage grows with what we learn.
What appears on a quality scorecard?
Dimension-level scores such as completeness, uniqueness, integrity and consistency for each critical data element, trended over time and tied to open issues and owners, so the scorecard drives action rather than just describing a problem.
Is this a one-off assessment or an ongoing service?
Either. Many organisations start with a baseline assessment and then move to periodic scorecarding, or automate the rule suites through our Data Pipelines & Continuous QA service.
Do you fix the data as well as measure it?
Our focus is independent measurement, root-cause analysis and verified retesting. We give your teams the evidence and the specific findings they need to fix the underlying cause, and confirm the fix has worked in the included retest.
What do you need from us to get started?
Typically read-only access to the systems or extracts in scope, a short briefing on your critical data elements and the business rules that matter most, and a point of contact for questions. We agree scope and access up front, then handle the profiling and testing ourselves so the load on your team stays light.
How do you keep our data confidential?
We work under a mutual non-disclosure agreement and handle only the data needed for the engagement, on the access terms you set. As an organisation certified to ISO/IEC 27001 and ISO 9001, we apply defined controls to information security and quality throughout the work.
What dimensions do you measure data quality against?
We assess against well-established data quality dimensions, commonly completeness, uniqueness, validity, consistency, integrity and timeliness, mapped to the elements that matter to your business. The exact set is agreed with you so the scorecard reflects the risks and decisions your data actually supports.
How does this work alongside our in-house data team?
The engagement is independent and evidence-led, so it complements rather than replaces your team. We provide the measurement, findings and retest verification, while your engineers own the fixes, with the specific, reproducible evidence they need to act quickly.
Is a retest included, and can we move to ongoing monitoring?
One retest round is included so agreed fixes are verified rather than assumed. Where you want continuous assurance, the rule suites can be automated and run on a schedule through our Data Pipelines and Continuous QA service, or repeated as periodic scorecarding.
How is the work priced?
Engagements are scoped to your systems and critical data elements, so pricing reflects the size and complexity of the work rather than a fixed package. Share your context and we will return a clear, obligation-free proposal before anything begins.
Also in Data Confidence: Data Warehouse & BI Testing · Data Migration Testing · Data Advisory & Strategy · Data Pipelines & Continuous QA
Ready to measure the data you rely on?
Share your context and constraints and we will return a clear, evidence-driven proposal, scoped to your systems and your critical data elements.