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Conversations in the analytics community increasingly turn toward the growing volume of data in the digital universe and our ability to analyze it. Chatter surrounding the Internet of Things (IoT) and big data suggests that by 2020 there could be 50 billion internet-connected objects generating as much as 44 zettabytes (44 trillion gigabytes) of data. That's an enormous amount of potentially analyzable information.

As the volume of data continues to rise steadily, questions are inevitably raised about how to grapple with and how to analyze it. In a recent post on his Perceptual Edge blog, business intelligence guru Stephen Few addresses one of analytics' core issues, namely, how scalable does an analytics solution need to be?

The answer is intrinsically tied to one of analytics core components: people.

In his post, Stephen Few acknowledges the lack of skilled data analysts who are equipped to handle the analysis of increasing volumes of raw data -- much of which, he adds, is mostly "noise." Still, some would argue that technology can replace the prowess of skilled data scientists. Stephen Few disagrees with this approach, writing, "Even the best technologies cannot do the work of skilled data analysts … the problem can only be effectively addressed by helping people develop analytical skills." 

With data increasing at "exponential rates," technology proponents believe that an analytics solution must scale up to handle a growing volume of data. Humans, they would argue, are not scalable, but technology is. 

Stephen Few counters this neatly. "The amount of useful information is not increasing exponentially, therefore the need for analytical horsepower is also not increasing exponentially," he writes. "Data sensemaking is a human activity that can at best be augmented and assisted by analytical tools. The only viable solution to the analytical challenges that we face is to develop the human resources that we need."

Why People Matter in Analytics

Even the most powerful analytics technology will be of little use if it's not being constantly tweaked and manipulated by experienced data scientists. Moreover, analytics is not a one-size-fits-all endeavor. It's impossible to devise an innovative, customized presentation layer without the work of skilled analysts. 

As Stephen Few acknowledges, the increasing volume of data in the digital universe is not all useful. It needs to be sifted, wrangled, and cleaned before it is run through complicated software to yield results. 

Experienced data scientists not only understand software, they also understand business. They are the key to a comprehensive analytics solution. 

Analysts are trained to have a foundation in statistics, math, modeling, analytics, computers science, and applications. But, they also have the sense to look at data from multiple sources and identify patterns -- a task that often yields the most useful insights for businesses. 

Finally, data scientists have the ability to engage with clients and understand their unique business goals and user needs. This knowledge allows them to target their analysis and deliver insights in an actionable manner rather than through a generic, computer-generated system.

How Software as a Service Addresses the Need for People

Technology can never fully replace people and still deliver high-quality analytics. Stephen Few sums it up nicely, "Don’t trust a technology vendor who claims that skilled data analysts can be replaced with their product. That analytical product does not exist."

Instead, look for a vendor that understands the value people bring to analytics. 

Solution as a Service (SolaaS) is a unique approach to data analytics that includes people as one of its core components. Through a single vendor, SolaaS combines a powerful analytics backed with the industry's top data scientists, delivering a seamless and robust analytics solution to customers.

The data scientists working for a SolaaS vendor like PolyVista have intimate knowledge of the vendor's software, plus years of business and analytics experience. They are capable of understanding business challenges faced by a variety of industries, pinpoint relationships among data sets and recognize insights. Plus, these analysts encourage client-vendor collaboration to create a user-focused, customizable presentation of results. 

Summary

As the volume of data in the digital universe continues to grow, some would argue that we can cut people from analytics, and turn solely toward technology to handle the task. Their argument is that analysis of growing data sets should be handled by technology, because it is scalable and people aren't. But as Stephen Few writes, "The amount of useful information is not increasing exponentially; therefore the need for analytical horsepower is also not increasing exponentially."  People -- namely, experienced data analysts -- will always be a critical part of the analytics equation. A Solution as a Service approach to analytics addresses this need for people by combining a powerful analytics backend with the services of highly skilled data analysts.