Information Architecture and Data Governance

The combination of a practitioner’s perspective and a rigorous analytical approach enables us to help our clients define how data needs to be organized and integrated consistently across the business lines and enterprise programs to achieve both operational and analytical objectives. We help our clients rely on their data with confidence through the implementation of data governance structures, processes and tools that are appropriate to their current maturity and needs.

Information Architecture and Data Modeling

Understanding how data needs to be organized and integrated

• Creation of detailed information models that enable the business and its technology partners to understand the information the business needs, how the information needs to be organized and the valid ways it can be combined to answer analytical questions and obtain meaningful answers.
• Assessment of vendor and solution logical and physical data models to ensure they support business requirements for the organization and integration of data.
• Implementation of business requirements for organizing and integrating data within relational and dimensional data models.

Understanding how data is used

• Creation of business metadata taxonomies to enable identification of data sources using business-meaningful language as well as documentation and understanding of business lineage.

Data Governance

Data governance target operating model

• Identification of required business and technical capabilities appropriate to the current maturity and needs of the organization.
• Organizationally appropriate governance structures, policy and supporting procedures.

Process design and tool selection / implementation

• Implementation, design and selection of organizationally appropriate processes and toolsets to support data governance including data quality (DQ) assessment, metadata management, data lineage / mapping and issue management.

Data management operations

Design and implementation of:

• Operational scorecards to track data quality against service level agreements.
• Workflow solutions for operational control over data governance processes.
• Processes, procedures and artifacts to enable diagnosis and resolution of  data issues.