Data Quality

Adastra safeguards your most valuable asset by employing an iterative framework, which is institutionalized and operationalized as part of Data Governance initiatives, developing a solution tailored specifically to your organization. 

Understand Data Assets

Through a series of activities, including data classification, metadata collection, data profiling, etc. Adastra will assert the expectation and objectives of your data, driven by business and technical requirements, to help lay the foundation of your DQM Strategy. 

Measuring Data Quality

Establishing rules to determine the reliability and validity of data assets, along with thresholds to identify classifications of quality levels, allowing for separate lines of business to maintain consistent assessments of their information. 

Monitoring and Reporting on Data Quality 

Creating a formalized process to keep track of the level of conformance over time, of the data against the defined rules.

Improve the Level of Data Quality

Creating automated rules and processes driven by technical and business requirements, either through Data Quality tools, or manual intervention and workflow processes such as:

• Filtering
• Parsing
• Cleansing
• De-duplication
• Standardizing
• Enrichment
• etc. 

Machine Learning

As stewards manually correct data quality issues and exceptions, Machine Learning algorithms will re-evaluate the current automated cleansing rules and workload of Data Quality issues. They will make determinations based on thresholds on whether or not to automatically cleanse Data Quality issues, adjust Data Quality rules, or suggest recommendations for integration efficiencies.