Data Architecture Modernization
The Data-Centric Manifesto posits that data is the center of the universe, and the trends around Data Science support the value proposition of data. Trends around adopting Data Governance is a reflection of organizations having increasingly complex data architectures, and the critical importance of quality data. Organizations require comprehensive capabilities and processes around data; the DMBOK Wheel summarizes the challenges.
This project:
- Hired dedicated Data Architects to help lead the data architecture modernization initiative
- Rationalized feedback with DMBOK to develop vision, do a gap analysis, and prioritize
- Partnered with the new Enterprise Data team to promote Data Governance
- Explored new solutions, conducted proof of concepts, and purchased new capabilities
- Established a Community of Practice for Data Architecture to communicate new capabilities, best practices, and discuss challenges
Benefits included:
- Standardized on tools and platforms
- Provided uniform and secure access to data through a Data Virtualization platform, connecting SQL, non-SQL, and Data Lake data sources
- Introduced a cloud data warehouse with an astonishingly low TCO and impressive performance
- Connected a Data Science platform for efficient machine learning and artificial intelligence projects
- Initiated the consolidation of several proprietary master data management systems into one industry leading Enterprise Data Management platform
- Shifted consumers to leverage a real-time data distribution platform
- Published data models and metadata for core enterprise data
- Defined SLAs around critical data sets and optimized processes
- Empowered users and promoted self-service, rather than relying on expensive, overloaded IT
- Saved days of unnecessary data pre-processing, helping teams efficiently run machine learning models
