Every enterprise wants to use data as a competitive advantage. But most aren’t. Why? Data everywhere and siloed analytics.

The most valuable and transformative business use cases—IoT-enabled predictive maintenance, genomics research, real-time compliance monitoring—require multiple analytics workloads and data science tools and machine learning algorithms run against the same diverse data sets. It’s how the most innovative enterprises are unlocking value from their data and competing in the data age. 

However, many enterprises are struggling because:  

1. Their analytic workloads run independently—in silos—because even newer cloud data warehouses and data science tools weren't designed to work together.
2. Their data is everywhere—in data centers, public clouds, and at the edge—and they have no practical way to run analytics or apply machine learning algorithms to their data.
3. Siloed analytics and data everywhere make a coherent approach to data privacy or IP protection nearly impossible, or at best force onerous controls that limit business productivity and increase costs.

Most enterprises need a new approach. Simple analytics that improve data visibility are not enough. Being data driven requires the ability to apply multiple analytics disciplines against data anywhere. Take autonomous and connected vehicles—you need to process and stream real-time data from multiple endpoints at the Edge while predicting key outcomes and applying machine learning on that same data set.