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Remember in the Wizard of Oz, when Dorothy and her crew discovered that the all-knowing Wizard was actually the so-called "man behind the curtain"? In the world of data analytics software, there also lurks a man or woman behind the curtain, or in this case, software: data scientists. They are the ones hard at work, often behind the scenes, making the real analytics magic happen.

But not everyone would have businesses see data scientists or analytics software in this light.

The fact is, analytics software is a complicated entity whether it's installed on-premise or delivered through software as a service (SaaS). Still, plenty of data analytics SaaS vendors sell their products under the guise that it's "easy-to-use." Though SaaS eliminates maintenance headaches, it does nothing to make the software easier to use. Unfortunately, it's not until after businesses have purchased the software and must go it alone trying to implement and manipulate the product that they realize they've gotten themselves in over their heads. 

Data analytics software is unwieldy. Before it can bring useful results to a business, the data being sought must be understood and properly questioned. First trying to wrangle this data through cleansing and preprocessing – the critical first step in the analysis pipeline, then learning the nuances of the software, and finally deriving useful results can be a time-consuming and costly task for businesses ill prepared for the product's reality.

Unfortunately, most businesses don't have the resources or talent – top-notch data scientists – on hand who intrinsically know how to best navigate the data and manipulate SaaS products. 

Why are Data Scientists a Necessity?

SaaS analytics products fail to acknowledge the very real dilemma surrounding any data analysis: even the most powerful software will be of very little use if it's not being manipulated by first-rate talent. 

Simply put, data analytics software – whether on-premise or SaaS – needs people.

Data analysis is like a pipeline. Beginning with data cleansing and preprocessing, the result of each step in the process is inherited by the next step, and repeated down the pipeline, until the results are ready to be shared with stakeholders. These insights lead companies toward smarter, data-driven decision-making and subsequently yield lucrative results.

A business might purchase a SaaS data analysis product with the hope that an in-house untrained person, rather than a data scientist, can take the helm, only to be disappointed with results. 

Ideally, data scientists not only have intimate knowledge of analytics software, they also possess great business acumen, giving them the ability to derive the greatest value, or results, from the data. Data scientists have a solid foundation in computer science and applications, as well as statistics, math, modeling, and analytics. They have mastered the artistry of not simply looking at data from a single source, but from multiple ones, better identifying trends or patterns.

Still, the secret sauce, as it were, is the data scientist's ability to focus on business problems that are most relevant to a particular organization, rather than give a glancing overview to issues in general. Finally, they can then clearly communicate their work and findings to both the IT segment and business leaders. 

But when businesses opt to work with SaaS analytics products, the people that a business designates to manipulate the software typically don't have an intimate knowledge of it, or perhaps, a wider understanding of relevant corporate concerns. This is an inherent shortfall that can cost organization time and money as they try to get up to speed.

Where to Find the Missing Link: Solution as a Service (SolaaS)

The answer to the software-talent divide lies in the revolutionary model of Solution as a Service (SolaaS). With SolaaS, a single vendor provides both quality data analytics software to sift through data, along with the industry's finest data scientists who can augment your needs and capably exploit the results to the company's benefit. 

The knowledge and understanding of unique business challenges imparted by these data scientists helps deliver better-targeted results to any company.

Just as vendor and customer collaboration is imperative, the single vendor solution presents an equally valuable collaboration between the software's developers and data scientists. As questions or issues arise between the two, they can be easily and quickly addressed.

The time and money that companies lose with SaaS analytics products – ramping up the software, accruing consulting costs – are avoided. Plus, because analytics software needs constant tweaking and review, SolaaS guarantees an ongoing network of support by leveraging its software and team in a symbiotic relationship with repeatable results.

Summary 

In a world where too many SaaS vendors promise an easy-to-use analytics solution that turns out to be anything but, Solution as a Service (SolaaS) gives businesses access to some of the industry's most powerful analytics software, as well as the people who best know the intricacies of the software product. When software and talent are served through a single vendor, the results can be astonishing – and valuable.