This multinational manufacturing company implemented Collibra’s data quality management tool to establish management practices and create a roadmap, process and organizational skillset for ongoing data quality remediation across the organization.
About
Industry: Manufacturing
Background: Our manufacturing client had more than 60,000 employees with company-operated stores and facilities located in the United States, Canada, the Caribbean and Latin America, with distributors and construction, industrial, packaging and transportation clients located worldwide.
Engagement Type
This is one part of a multi-year and multi-project engagement. Our work consisted of data governance (DG) implementation, including master data management (MDM), metadata management and data quality (DQ). Over time, the scope grew to include the installation of Collibra’s Data Governance Center and data quality tool and operationalization by turning abstract concepts into measurable observations.
This case study is focused on the data quality management portion of the engagement. View this case study for another engagement with this client.
Opportunity Areas
- DQ errors led to a lack of trust in data across the client’s organization.
- The client was in the process of establishing MDM and metadata across domains such as customer, product and supplier. While working through this digital transformation, our client wanted to implement DQ checks across all applicable fields starting with defined critical data elements.
- The client performed cloud migration, reducing the need for many ERPs by moving from multiple down to one single source within Amazon Web Services/Snowflake. This migration provided the opportunity to assess all data and identify both differences and errors in data throughout this process. Our client leveraged a Collibra DQ tool to identify, fix and monitor quality within the migration process.
- This engagement involved creating a fully functional data quality capability to meet the client’s goals.
Engagement Snapshot
- Establishment of Data Quality Office and framework
- Established roles and responsibilities across the client’s organization, such as governing bodies, individual contributors and the formal structure.
- Executed DQ tool assessment.
- Created processes for identification, assessment, remediation and monitoring of DQ across the organization.
- Developed a roadmap for ongoing DQ success.
- Tool installation
- Partnered with Collibra to install the Collibra DQ tool on premise and make proper data source connections.
- Integrated the Collibra DQ tool with Collibra Data Governance Center.
- DQ training and coaching
- Provided in-depth working sessions and training covering tool functionality.
- Creation and expansion of DQ use cases
- Worked within the organization to understand and define DQ challenges, e.g., what is the issue, what process does it impact, who does it impact, what are the expected values and what systems are involved.
- Prioritized these issues based on criteria established by the DG and DQ offices.
- Use case implementation
- Implemented prioritized use cases into the tool.
- Mapped functionality of the tool to carry out proper DQ checks and monitoring, e.g., source to target, profiling, deduplication, etc.
- Created and applied business and data quality rules to each use case.
- Tested and validated the DQ jobs were performed as intended.
- Established a baseline for reporting DQ scores and errors.
- Integrated DQ data sets, rules, scores cards and reporting within a metadata repository.
Impact
- Established DQ management practice.
- Installed a fully functional DQ tool with training and initial use cases.
- Implemented roadmap, process and skillset for ongoing DQ remediation across the organization.
