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Whether you've sent an email using Google Gmail, or accessed CRM data with SalesForce.com – you have benefitted from software as a service (SaaS) products and you may appreciate their convenience of being available from anywhere. Cloud-based software continues to be the tech industry's darling because it requires no installation or maintenance; it also has the reputation of being quick and easy to use.

SaaS is well-liked for good reason: it provides a smart and successful way to deliver software. However, where data analytics is concerned, there are limits to its benefits. The good news is a new concept has emerged, Solution as a Service (SolaaS), which builds on the advantages of SaaS and raises the bar for data analysis projects.


Data Analytics and the Limitations of SaaS

In businesses around the globe, IT departments welcome SaaS products with open arms. Cloud-based software is typically easy-to-use and does not require in-house implementation; if something goes wrong on the backend, the IT department is not responsible to troubleshoot and fix the problem. 

In general, software usage can be broken into two essential parts:  
Software installation and maintenance; this includes handling any patches, bug fixes, enhancements, or new features that may be added over time
Utilization of the software by an end user. Whether a business chooses software that is installed onsite, or a SaaS product, this second component is always present
For example, if a business uses Google Gmail for its email service, Google maintains the backend, relieving the company's IT team of responsibility for installation and maintenance.

Still, the Gmail users are responsible for writing their emails, at times composing complicated and difficult notes. In this case, Gmail – SaaS based email – might make life easier for an IT team, but Gmail does not make the task of writing emails easier for the user. 

Similarly, analytics provided through SaaS does not make analysis work easier for the end user.  This reality of SaaS is seldom discussed. 

Today, many analytics vendors rely on SaaS to power their backend. However, when a business invests in data analytics, they want more than access to powerful software; they want results -- and that requires people. 

Hiring and retaining an in-house analytics team to extract insights from analytics software is costly and time consuming. Time and money are lost while the new team acclimates to the backend. Relying on your business's existing IT team is not possible; analytics software is complicated and needs to be implemented and tweaked by highly trained data scientists.

The goal of any analytics project is to obtain data-driven insights. The above issues with SaaS can thwart the delivery of actionable results, and contribute to a project's failure. 

Bottom-line 

SaaS might be convenient to utilize, but it does not make data analytics easier. 


Raising the Analytics Bar: Solution as a Service

Through a single vendor, Solution as a Service (SolaaS) combines powerful analytics software with skilled data scientists. In essence, SolaaS offers businesses SaaS plus people, in effect, upping the analytics ante. 

With SolaaS, a vendor like PolyVista employs data analysts with intimate knowledge of the vendor-owned software. These analysts can identify and present insights customized to individual clients' needs using PolyVista's patented user-interfaces. 

Because SolaaS provides clients with services in addition to software, it frees businesses from the burden of managing their analytics software and unearthing insights. 

SolaaS raises the bar by adding a new twist to existing elements: marrying the industry's best software and data scientists, while embracing a philosophy of vendor-client collaboration. 

Moreover, SolaaS vendors such as PolyVista eliminate cost concerns by charging a fixed, monthly fee -- businesses never face pricey consultants, unpleasant surprises or hidden costs.

Summary

SaaS quickly delivers a software product and relieves IT teams of certain burdens; however, its benefits are severely limited with data analytics. Although SaaS seems like a convenient analytics solution, it creates difficulties for businesses who lack talent to utilize the analytics software. Businesses can achieve greater success with Solution as a Service, which seamlessly joins software and data scientists' services through a single vendor.