Data Governance



• Assign Stewards: Designate data stewards for specific datasets, covering all systems and data flows.
• Define Goals and Strategies: Establish data quality goals, milestones, and actionable steps. Communicate requirements to responsible stewards.
• Implement Strategies: Evolve standards and shape infrastructures and quality zones where these standards are enforced.
• Monitor KPIs: Track key performance indicators related to data quality and progress toward milestones.
  

Data Stewardship



• Develop Rules: Create and refine BDQ and TDQ rules addressing specific requirements, standards, or goals.
• Validate Rules: Cross-validate BDQ and TDQ rules against each other and previous versions.
• Analyze Data: Validate datasets according to established rules.
• Conflict Resolution: Support manual resolution of issues and post-validation actions.
• Manage DQFs: Define and manage Data Quality Firewalls to enforce rules on data flows.
• Shape DQZs: Collaborate to establish Data Quality Zones by validating datasets and placing DQFs on all data inflows.
• Evolve DQZs: Synchronize updates in rules with dataset validations and affected DQFs.
• Monitor Infrastructure: Keep an eye on KPIs for datasets and data quality components.

Data Quality Analytics



• Analysis Tools: Provide tools for analyzing and visualizing data quality metrics.
• Infrastructure Visualization: Map out the data quality infrastructure.
• Identify Discrepancies: Spot gaps between current standards and future goals.
• KPI Tracking: Log KPIs over time for infrastructure components or datasets.

Data Validation



• Ensure Compliance: Verify that data meets business and technical quality standards.
• Run Checks: Execute BDQ and TDQ validations.
• Issue Certifications: Provide certificates and initiate necessary post-validation actions.

Data Cleansing Services



• Auto-Correct Data: Improve validity, accuracy, completeness, and consistency through automatic corrections and enrichment.
• Address Issues: Fix incorrect data types, missing values, and inconsistencies.
• Leverage Knowledge: Use data models and master data for
  

Data Audit Services



• Anomaly Detection: Identify irregularities using statistical methods.
• Profiling Requirements: Establish needs for data profiling.
  

Data Capturing



• Enabling real time error detection already during capturing, ensuring high-quality data right from the start.
• Automating data capture forms - no frontdend & backend Development need if change in forms required.
  

Data Review



• You don't have enough data and you have to make assumptions for your forecasting? Predict with confidence by using our bi-temporal databases.
• Connect all your data: internal,external, historical.