Data & Intelligence
Missing, duplicate and implausible data is detected before it distorts workflows and decisions.
Salytiq turns validation rules, exceptions, owners and quality status into a repeatable control process.
Salytiq turns validation rules, exceptions, owners and quality status into a repeatable control process.
From intake to outcome
- 01Prioritise critical fields and failure impact
- 02Define completeness, consistency and plausibility rules
- 03Automate checks and exception reports
- 04Agree ownership and correction path
Business outcomes
- Detect errors earlier
- Make quality status transparent
- More reliable reporting and routing
Typical deliverables
- Prioritised rule catalogue
- Automated quality checks
- Exception report or monitoring view
- Correction process and ownership matrix
When the solution fits
- Duplicates or missing values occur regularly
- Teams repeatedly correct the same errors
- Metrics need visible quality evidence
Scope boundaries and prerequisites
Clear boundaries
- Automated rules do not replace business data ownership
- Unresolved definitions remain visible as open decisions
Required foundations
- Prioritised data objects and known error examples
- Owners for rule acceptance and correction
Interactive proof
Open interactive demoData & Reporting Automation
Data checks, exclusions, metrics and export.
How an engagement can work
- 01
01 Understand
Capture the workflow, effort, systems and visible pain.
- 02
02 Prioritise
Align impact, feasibility and economic value.
- 03
03 Implement
Build, test and clearly hand over one bounded workflow.
- 04
04 Operate
Monitor and improve within an agreed scope.
Frequently asked questions
What does Data Quality include?
Salytiq turns validation rules, exceptions, owners and quality status into a repeatable control process. Typical deliverables include: Prioritised rule catalogue; Automated quality checks; Exception report or monitoring view; Correction process and ownership matrix.
What information is needed to get started?
A robust initial scope requires: Prioritised data objects and known error examples; Owners for rule acceptance and correction.
When is this solution a good fit?
A useful starting point is especially likely when: Duplicates or missing values occur regularly; Teams repeatedly correct the same errors; Metrics need visible quality evidence.
Which boundaries are clarified before implementation?
Transparent scope boundaries are part of the work: Automated rules do not replace business data ownership; Unresolved definitions remain visible as open decisions.
How can an engagement begin?
We first understand the workflow and prioritise one clearly bounded step. Where the starting point remains unclear, an Automation Audit can prepare the right implementation.
Related solutions
Salytiq
Discuss a projectDoes this solution fit your workflow?
Share the starting point, systems involved and frequency. Salytiq will frame a realistic next step.