To compete in today’s dynamic environment, organizations need to empower decision-makers at all levels with relevant, timely, trusted, and complete data. Using analytics and AI/ML to become data-informed and data-driven is critical to business strategies and to smarter operational processes and data applications.
Reaching goals requires a modern, flexible, and scalable data architecture. Legacy data platforms and practices are often a barrier. Cloud migration is the path many organizations take to overcome legacy barriers. The cloud is home to modern data platforms that enable organizations to stand up scalable new systems quickly, including digitally transformed data applications and services that generate terabytes, if not petabytes, of data.
However, data migration to the cloud alone is not enough. First, there are decisions about what to migrate and how—and dependencies and change management to deal with. Second, organizations need to consider questions regarding:
- Distributed versus centralized data, including emerging approaches such as data fabrics and data mesh
- Data integration and ETL/ELT
- Improving self-service data exploration and data relationship analysis
- Data observability, management, and governance
- Fitting with modern application development, including Kubernetes
Addressing these questions demands a holistic, future-focused view of the entire data architecture. This presentation will discuss trends and opportunities seen in TDWI research to help organizations put together a holistic view and develop the right modernization strategy.