- Focus on empowering users with data.
Even after substantial investments, enterprises struggle with data sharing and serving users with the correct data. The primary reason for adopting Data Fabric is data sharing and self-service usage. The focus is on the simplicity of data consumption rather than storage and retention.
2. Expand the computing infra without creating additional silos
Users leverage insight based on trust in the data source. Data Lineage plays a vital role in building that trust. However, it is challenging to trace the lineage with data sources distributed across multiple systems, geography, data format, and processing tools. The IBM data fabric platform provides integration adapters and devices to ensure we support data traceability across traditional sources, new generation cloud services, and open-source tools.
3. Cloud Agnostic data fabric platform avoids lock-in.
IBM data fabric solution is based on IBM “Cloud pak for Data” Platform, which itself can be deployed on-premises and any other cloud vendor, providing a unified data plane and a common user experience. In addition, the data sources or services can be provisioned across AWS and on-premise without the need for additional training.
4. Avoid Cloud Bill Shock for Data-Intensive Services.
AWS provides Sagemaker services to kick start your data science and ML initiative at a very minimal investment. Charges are based on actual compute consumption, an excellent way for experimentation. As ML becomes mainstream, the cost is best managed by combining PaaS and SaaS to ensure your data scientist has the freedom to experiment without worrying about bill shock. The same is true for intensive data processing, where an on-premise deployment can provide the lowest TCO. The Data Fabric platform binds the data pipeline irrespective of the location of the service.
5. Adopt hybrid and multi-cloud risk-free by adopting policy-based security.
Regulated organisations and the public sector has been accumulating petabytes of data with limited access for analytics. Security and privacy concerns limit data sharing. The traditional approach has been regulating access control at the source. While this is a prudent approach, its inhibits collaboration and data sharing and is tedious to manage. Centralised policy-based enforcement and data usage monitoring provided by the IBM Data fabric platform can ensure data protection across distributed sources.
Enterprises must have a data fabric layer before they go mainstream on cloud adoption to avoid further fragmented and silos ecosystems. Without a Data Fabric Platform at the early stage of the Hybrid Cloud Journey, the enterprises may end up with very costly initiatives later trying to bridge and manage the silos.
Anup Kumar Blog 