Does software as a service work for analytics?


PC Magazine defines the broad industry term Software as a Service (SaaS) as, “Software that is rented rather than purchased. Instead of buying applications and paying for periodic upgrades, SaaS is subscription based, and upgrades are automatic during the subscription period.”

SaaS, according to the same source, is ideally suited for cloud computing. In the SaaS model, applications are maintained in the provider’s data center or in a public cloud such as that provided by Amazon Web Services.

Redefining software as a service for analytics

PC Magazine’s definition of SaaS was largely created by vendors of operational systems, such as Salesforce. Analytics is different in many ways, and SAS, as a market leader in analytics, has put a lot of thought into what SaaS means to customers. Below is a round-up of the ways analytical systems are different from operational systems and what that means for SaaS and cloud computing:

Operational Systems Analytical Systems Implications for SaaS
A large number of transactions capturing or modifying small volumes of data. Small numbers of processes extracting insight from large volumes of pre-existing data. Data volumes associated with any process are large and therefore are slow to move around in real time.
Data is current and localized in predefined formats. Many applications can be self-contained. Older data is touched less often than new data. Needs to access data that is diverse and dispersed. May need to process historical data many times in different ways. Complex interconnections with many other systems in different locations. Batch processes need to give repeatable results.
Systems are likely to be mission critical. Systems are likely to be value-adding to optimize the business. Requirements for high-availability are lower: typically, 99% instead of 99.9%.
Little opportunity to differentiate from competitors, and generally more cost-effective to conform and commoditize. A potential source of competitive advantage, with greater value in being unique. The ability to adapt the solution to changing business needs is more important.


Despite the differences just highlighted, one thing remains the same: organizations are looking to SaaS for rapid time-to-value and low cost of entry. The trade-off is that buyers accept that with this low cost that they're buying a standardized offering, and in some cases the environment that they're using may be shared with other organizations and updates may be applied by the vendor to all users simultaneously. This approach, known as multitenancy, works well for many types of application, but it makes it more difficult for an organization to customize or evolve its platform as it needs to.

So, although low entry cost and rapid time to value are important, SAS understands that customers will need to evolve their SAS solution over time, adapting to new data sources and business problems. Or customers may simply become more sophisticated in their ongoing drive for competitive advantage, and then this multitenant approach to SaaS might not be suitable.

Evolving from SaaS to customized analytics

Therefore, SAS has adapted the delivery model of SaaS to meet customer requirements to provide a standardized environment to begin with, that can then be modified over time. SaaS packages that are designed this way include SAS® Cloud Enterprise Miner™, SAS® Cloud Office Analytics, SAS® Cloud Visual Analytics, SAS® Cloud Visual Statistics, and SAS® Cloud Visual Data Mining and Machine Learning. Learn more about all of these SaaS offerings from SAS. 


About Author

David Annis

Director, Sales Support and Enablement (Cloud)

Dave Annis has been specializing in the field of data and business analytics for over 30 years. In that time, he has seen trends come and go, but one thing remains the same – organizations have the potential to get enormous value from analytics. Back in the 80s when Dave was starting out, cool job titles like “data wrangler” didn’t exist, and although he wishes they had (“assistant statistician” didn't have quite the same ring), he’s excited to see how Data Science has come into the limelight, and continues to grow.

Comments are closed.

Back to Top