Data Quality & Data Lineage
Understand where data is coming from and when you can trust it.
A metric is only as reliable as its sources and processing steps. Missing values, unnoticed changes and unclear data flows complicate analytics, applications and AI projects. We connect measurable quality rules with traceable data origins and a defined process for exceptions, helping you diagnose errors and assess changes.
Your options
Services that move your project forward
Identify critical data and expectations
We select datasets based on their business value and the consequences of errors. Completeness, uniqueness, freshness and business validity are defined in practical terms.
Quality is measured against verifiable requirements.
Establish a data profile and baseline
Suitable samples and profiling methods reveal distributions, missing values, duplicates and anomalies. We interpret the findings with the responsible business experts.
You can identify relevant quality issues and understand their significance.
Automate quality rules
Agreed rules are checked at appropriate points in the processing. Threshold values, error messages and the handling of exceptions are explicitly defined.
Important deviations are detected repeatably.
Make data flows and dependencies visible
We connect sources, processing steps and consuming systems using existing metadata and suitable lineage interfaces. Gaps in coverage are documented.
The consequences of changes and possible sources of error can be better narrowed down.
Organize error handling and ownership
An alert needs a recipient, an assessment and a route to correction. We define responsibilities across source systems, data processing and business use.
A quality alert becomes a task someone can act on.
Improve quality during operation
Test results, recurring causes and changes are included in regular reviews. Tests and technical monitoring are connected to the existing platform.
Rules and documentation remain aligned with the data flows.
Where to start
Data Quality & Data Lineage Use cases
Three example situations show how we can help.
Reports suddenly show divergent results
We examine a defined data path and compare business expectations with sources and transformations.
An interface change affects unknown recipients
Lineage and documented dependencies help to assess impacts before implementation.
An AI use case needs reliable input data
Quality rules and data origin create a verifiable basis for further development.
From requirements to results
A clear process with agreed milestones
Prioritize data and risks
We select the first dataset and define its business importance.
Check the starting point and data paths
Profiling, sources and processing steps make the need for action visible.
Implement rules and reporting channels
We implement checks, assign responsibilities and add suitable lineage capture.
Review results and hand over IT operations
We test exception handling, discuss coverage limits and agree ongoing maintenance.
Your benefit
What you receive
- A quality profile for the agreed data sets.
- Documented and implemented quality rules with traceable results.
- An overview of recorded data paths and remaining lineage gaps.
- A procedure for evaluating, correcting and regularly reviewing deviations.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Data Quality Check
For clarity: profiling, business expectations and prioritized error causes.
Request a quote: Data Quality CheckImplement quality checks and lineage
For a defined data flow: automated rules, traceable origins and actionable alerts.
Request a quote: Implement quality checks and lineageExpand quality management
For other data products: common rules, better coverage and regular reviews.
Request a quote: Expand quality managementSYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Deployment and platform automation
Kubernetes · Azure Kubernetes Service · Helm · Argo CD · Terraform
Versioned configuration and declarative deployment connect infrastructure and applications. GitOps makes proposed changes reviewable and the desired state explicit. Operational transitions and recovery procedures are still planned for the specific application.
Repeatable changes and clearer responsibility boundaries.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
Questions before you get started
Which dimensions of data quality are relevant?
Completeness, uniqueness, freshness, consistency and business validity are often relevant. Their importance depends on the use case. We define specific, testable rules for the selected data.
Can data quality be checked completely automatically?
Technical checks and clearly defined business rules can be automated. Meaning, exceptions and changing business conditions still require business judgment. The solution connects automated checks with named owners.
What does Data Lineage mean?
Lineage describes where data comes from, what processing steps it goes through, and where it's used. Depending on the tool and integration, this can be captured down to the column level. Actual coverage is checked for your environment.
Does a Lineage tool automatically show all data paths?
Only supported and integrated processing steps can be reliably recorded. Manual exports, external systems or individual scripts can cause gaps. We document these boundaries and add appropriate metadata or procedures.
Does every quality failure stop a data pipeline?
This depends on the importance and impact of the rule. A warning, a quarantine of individual data records or an abort may be appropriate. The behavior is agreed in advance and tested with typical error cases.
Can you also clean up existing datasets?
A targeted clean-up can be agreed. Rules, approvals and the handling of data records that cannot be clearly corrected are clarified in advance. At the same time, the causes in the recording or processing process should be addressed.
How does Lineage support change and error analysis?
Recorded dependencies show which reports or applications rely on a source and its transformations. This supports diagnosis and change planning. Business validation and integration tests are still needed.
How can we keep the initial scope manageable?
A critical dataset with known users and a few relevant quality rules is often a useful start. Sources, coverage, checks and error handling are defined explicitly. More data flows can then be added in stages.
Discuss your next step
Which dataset needs a more reliable foundation?
Describe the usage and typical deviations. We narrow down an initial scope of testing and improvement together.
Data Quality & Data Lineage
Your next step
Tell us what you need. We will route your enquiry to the right team and discuss the next steps with you.
Fields marked * are required. Phone, company and postal address are optional.