Across the business world, software as a service (SaaS) has redefined the way that people think about licencing software. In the past, licencing enterprise software was a major undertaking, but now the time-to-value has dropped from months to days. The trade-off is that organizations that buy SaaS will get an off-the-peg offering, pre-defined by the vendor. In the analytics world, that’s great for getting started, but if your requirements change over time, where do you go from there?

In this post we'll explain the different types of cloud offerings for analytics and use an example from the manufacturing industry to explain how each works – and how our example company, an appliance manufacturer, can grow from using one type of cloud analytics service to more advanced analytics solutions.

Defining software as a service

SaaS can solve business problems through products that include software, hardware, and hosting services for a contracted period. SaaS provides uninterrupted access to the specific software you need to make fast, accurate business decisions.

SaaS solutions are generally multitenant, meaning that all organizations have the same version and must upgrade at the same time. With traditional SaaS, if you want to customize anything, you must move off that platform and onto something new. But with managed SaaS, you don’t have these restrictions. You don’t have to upgrade the software as soon as an upgrade is available. Instead, you have the flexibility to delay an upgrade to a mutually convenient time. Also, as your analytics needs change, you can move to a more customized hosted managed service without having to migrate or rebuild the environment.

Best of both worlds

Emerging businesses who are discovering analytics for the first time may want to try a results as a service (RaaS) approach. With RaaS, value is delivered from analytics without you having to touch the software. Just provide the business problem and data, and you get the answers you need. For example, an appliance manufacture might provide data about warranties and customer service to be analyzed do detect and prevent product quality issues.

Once you’ve begun to experience the power of analytics with RaaS, you may want to start developing your own capabilities. A great next step would be to consider SaaS. SaaS allows you to solve business problems in a ready-to-use cloud environment, meaning that there is no requirement to deliver an IT project as a prerequisite to getting value from the software. Continuing the example above, moving from RaaS to SaaS would mean our appliance manufacturer might transition from getting regular product alerts and a weekly report to having more of a portal where product managers could log in and query the data for potential safety or recall information.

The standard SaaS offerings are highly powerful and functional, but we know from experience that once an organization embarks upon an analytics project, its requirements become more sophisticated. Many of our customers view analytics as a valuable component in their quest to achieve competitive advantage, so it is important that they be able to grow and adapt their environments to their own requirements.

So the next logical step after utilizing a standardized SaaS approach is a managed service solution, which permits higher levels of customization to meet your exact requirements. If a SaaS approach has already been used, any additions and customizations would simply be added to your existing environment - there is no need to migrate.

Again, if we continue with the appliance manufacturing example, moving that SaaS solution to a managed service solution would mean that you could define custom interfaces and develop some of your own models for warranty analysis, but the solution would still be maintained and managed off site.

Although there are undeniably more benefits to having your own custom solution, as with any investment, it may not always be appropriate to dive straight in with a fully managed service solution. Managed SaaS offerings are designed to bridge the gap between the low involvement of a RaaS offering and the more highly involved Managed Services.

Choosing a cloud solution – like choosing a car

Deciding which solution is right for you is not unlike choosing a new car.

Buying a car with very specific requirements could be likened to selecting managed services. The car would need to be custom built to your exact specifications. Buying a custom-designed car would require more time till delivery and would cost more, but you would get exactly what you wanted.

In contrast, standard SaaS offerings are comparable to renting a car. You choose the size of the car, but the make, model, and options are determined by the rental company. Like SaaS, the rental car is immediately available, but once you drive it off the lot you can no longer make changes to it. Like SaaS, a rental car is pay as you go and can be returned when you want.

Managed SaaS bridges the gap between standard SaaS and Managed Services. The comparison here would be selecting your car from a dealership’s available inventory. You have choices in color and accessories, but your car won’t be custom designed for you at the outset. Later, however, you could make modifications to it, but you couldn’t change its fundamentals.

Managed software as a service – bridging the gap

One of the benefits of managed SaaS is that it helps you step through the maturity curve of analytics. It is ideal for organizations that have test driven analytics using a RaaS-type solution and want to start building out their own analytical capabilities with a low entry cost.

Learn more about SaaS solutions 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.

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