Drive cloud-ready flexibility – Shift workloads within a public cloud, private cloud and on-premises environment, based on your application requirements.

Better-enable your data scientist – Collaboratively analyze data using the built-in IBM Data Science Experience solution, or use existing Jupyter notebooks and bring data scientists to your organization’s data.

Gain real-time value with machine learning (ML) – Stream, analyze and learn from data sets with machine learning without explicit programming, so data scientists can develop and improve ML models on the platform where the data resides.

Access, query and analyse data across your data warehouse and Hadoop – Use IBM’s Common SQL Engine for workload portability and skill sharing across public and private clouds.

Reduce disruptions by scaling out incrementally – Scale out compute power and storage in-place and reduce disruptions to your analytics systems.