Despite huge progress in data, analytics, and AI platforms and tools, our data challenges keep stacking up! The fundamental business needs of storing, processing, and accessing data are complicated by ongoing cloud migration, growing data variety and volumes, demand for real-time everything, and ever-expanding regulatory and compliance mandates.
Data management — which includes data ingestion, integration, transformation, governance, orchestration, security, quality, and preparation — is core to delivering consistent and real-time data across the enterprise to support business operations. Trusted and integrated data is even more critical as enterprises implement more AI around the organization and in customer experiences. Poor training data leads to flawed AI models, which lead to suboptimal (or even catastrophic) outcomes, particularly for use cases that are automated, highly public, risky, or under regulatory scrutiny.
- Generative AI integrates with data management. Data management solutions already offer many approaches to support AI use cases. Vendors are innovating to deliver even more integrated capabilities, such as providing a natural language interface to enable access to all enterprise data without knowledge of SQL or other programming languages.
- Global data fabrics. We are seeing the emergence of global data fabrics expanding to support multiple domains across geodistributed locations and supporting more built-in data intelligence. While global data fabrics are in their early stages, the potential is huge for users and vendors to support global applications and deliver real-time analytics across regions.
Originally posted on Forrester
