As organisations start embracing public infrastructure cloud for their critical data and application needs, Data Science SaaS will become more practical for use. The biggest challenge for Data Science SaaS is to have customers expose / store their data to / on their multi-tenant public platform. Increasingly, public cloud services are becoming a compelling ground for enterprises. This means with applications, data also becomes a good prospect to be moved to these platforms.
This is due to low data access latency requirements for apps in cloud and increasing confidence in shared public cloud services.
Once the data is out of the enterprise data center, inter cloud service integration becomes easier. For eg:- Imagine an AWS EC2 infrastructure running your enterprise application, with data lying on S3 / RDS. To extract Business Intelligence and inference out of this data, it is practical to use an existing public SaaS that can work on this data, rather than using in-house analytics infrastructure. Greenfield apps having their genesis on public cloud are already candidate to be used with Data Science services (aka Analytics as a Service).
The future of Data Science SaaS looks promising. Startups like Datameer, ClearStory are doing pioneering work on this.
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