The primary goal of the Williams Master Data Management (MDM) project was to create the flexibility required to allow the delivery of real, measurable value to their business, supporting key objectives such as:
- Improving business operations and decision-making
- Streamlining business operations and processes
- Establishing a single version of the truth across the Williams enterprise
- Establishing a strong data foundation layer
- Reducing information costs and improving productivity
- Facilitating cooperation and collaboration across organizational boundaries
- Improving regulatory compliance, control, and risk management
Williams recognized that to improve business operations and decision-making ability, the underlying data must be consistent, accurate, reliable, accessible, and integrated with key business systems and applications that need to use it. This enterprise approach to providing an enterprise system focused on providing policies and procedures, a new data architecture, a focus on quality data and metadata management, master data management and security of the data. Realizing that access to a single version of the truth data was not going to be an easy task; Williams carefully planned a communication strategy to address the needs of Williams GIS users.
In order to meet these challenges, GISinc teamed with Williams to ensure that the new data architecture was carefully vetted through the various sectors of Williams paying careful attention to existing data management practices for their Pipeline Open Data Standards (PODS) data architecture. By providing a centralized repository for many disparate data elements and disparate data locations, GISinc is currently stepping through a defined migration process to ensure that current and relevant data are migrated and duplicative data elements are eliminated. Specific components we are working on for them include: making their data easily discoverable by leveraging VoyagerSearch, a data search and index solution, implementing an enterprise data strategy, analyzing their data, integrating and migrating their data, data quality management, and putting into place data governance practices.