Data Quality Analysis and Metadata Collection – Small

Approach:

Using Ataccama ONE, Adastra will analyze the given data set against data quality metrics such as completeness, validity, conformity, consistency, integrity, and duplicity. We will use AWS Glue for data wrangling and consolidation, and data can be persisted in Amazon S3.

Adastra will collect, create and enrich the data dictionary based on profiling results and business validation. We will also evaluate the business impact of the data quality issues found.

Resources:

• 1 principal DQ consultant
• 1 DQ analyst
• 1 business analyst

Scope: Up to 100 data elements/fields and 60 data quality business rules

Duration: 6-8 weeks

Assumptions/Dependencies:

• Ataccama ONE will be used for the engagement
• Business SME’s are available and engaged @20-40%
• Production quality data must be available
• Systems and fields to be determined by client, prior to start of engagement
• The environment must be provided for data access
• Baseline metadata or specification documentation is available

Activities:

• Detailed data profiling: field level, cross field level, table level, cross table level, duplicate analysis, foreign key analysis, business rules validation
• Metadata collection and enrichment
• Identification of DQ issues and business impact validation
• Development of recommendations and next steps

Deliverables:

• Data profiles
• Metadata data dictionary matrix
• Data quality issues and impact presentation
• Next steps and recommendations

Outcomes:

The output of this analysis will provide organizations with a clearer understanding of their data quality issues, as well as a focus for future state projects. It will serve as a pilot into data quality profiling and metadata collection activities to support overall data governance initiatives

Next Steps:

• Data quality pilot
• Data quality implementation