Improving Data Quality Automations
Organisations managing large datasets struggle with data quality across ingestion, processing, and use. Growing volumes and sources make manual validation impractical, causing inconsistent checks and delays. This use case shows how analytics automation can profile datasets, validate consistency, and flag anomalies in pipelines. Embedding structured profiling and validation into workflows helps teams detect issues early and trust their analytical outputs.
Where It Breaks Down
Data and operations teams commonly encounter the following challenges when profiling and validation rely on manual or ad-hoc approaches:
Inconsistent validation rules
Different datasets or teams apply varying checks, reducing comparability and trust
Reactive issue resolution
Data quality problems are addressed only after downstream processes are affected.
Lack of auditability
Validation outcomes are
inconsistently recorded and
tracked across processing cycles.
Limited visibility into data quality
Issues such as missing values, abnormal distributions, or unexpected changes are often detected late.
High manual effort
Profiling large datasets requires repetitive querying and inspection, consuming significant analyst time.
How We Elevate It
How ORTECH Powers This Use Case
ORTECH uses Alteryx to bring structure and consistency to how organisations monitor data quality. Instead of relying on manual checks or one-off reviews, data profiling and validation are embedded into an automated workflow. This ensures issues are detected early before they affect reports, dashboards, or operational decisions.
What this means in practice:
- Incoming datasets are automatically prepared and standardised.
- Data quality checks run consistently across every processing cycle.
- Completeness, consistency, and anomalies are clearly identified.
- Structured outputs make issues easier to review and track.
- Quality trends can be monitored over time, not just at a single point
Key Capabilities Delivered
Automated data profiling
Generates descriptive statistics, completeness metrics, and distribution summaries for incoming datasets.
Rule-based data validation
Applies consistent validation checks to identify anomalies, outliers, and unexpected changes.
Recurring quality monitoring
Executes profiling and validation workflows on a scheduled basis for ongoing oversight.
Structured quality outputs
Produces analysis-ready summaries to support review, tracking, and remediation.
From Raw Data to Operational Insight
Your data inputs
- Periodic datasets from multiple sources.
- Reference and metadata information.
What ORTECH does
- Automates ingestion and standardisation of datasets.
- Profiles data attributes and distributions.
- Applies validation rules and anomaly detection.
- Generates structured quality summaries and indicators.
How does it work for you
- Earlier detection of data quality issues.
- Reduced manual profiling and validation effort.
- Improved confidence in downstream analytics.
- Consistent and auditable data quality checks.
What It Delivers
Business Value & ROI
• Reduce time spent on manual data quality checks.
• Improve reliability and consistency of analytical outputs.
• Enable proactive identification of data issues.
• Support scalable data operations as data volumes grow.
