Data as a Service: The What, Why, How, Who, and When
When people think of Data as a Service or DaaS, they often conjure up images of complex algorithms and machine learning, but what we at RocketSource think of first is making data sets accurate, easy-to-digest, and actionable. That’s not an easy task. There’s a firehose of information pouring into businesses, and yet, our years of experience as a digital consulting firm have shown us that executives continue to struggle to harness it all. There’s tremendous confusion about how to use data to drive buy-in, fuel business transformation, and drive top and bottom line ROI-generating initiatives.
You’ll be hard-pressed to find an executive in today’s world that doesn’t appreciate the importance of data-centric insights. And yet, many executives are left scratching their heads wondering how to collect it, mine it, visualize it, humanize it, and most importantly, act on it.
Data analytics are far more complex than setting up algorithms to feed into databases. To tap into the insights buried in datasets in a meaningful way—one that yields tangible results—requires both human touch paired with a scientific approach. In this post, we’re going to dig deep into the ambiguous field of Data as a Service.
Quick reading note: We are Buckley Barlow and Jonathan Greene, co-founders of RocketSource and we’re writing this post shoulder-to-shoulder in an effort to apply our collective skill sets. Our goal is to give you as much insight on Data as a Service as possible. Although it’s co-authored, we want this to feel personal, so be sure to hit play on each of the audio soundbites in the post where you’ll hear us personally dive in deeper to this rich topic.
Data as a Service on the Gartner Hype Cycle
If you’re anything like us, you aim to avoid risks associated with shiny object syndrome, such as paying too much for new services that won’t stick around for the long run. We’re firm believers that investments should pay off in dividends, which is why we regularly look to maturity indexes when making decisions for our business and for our clients. For example, we lean heavily on the S-curve of Business when analyzing the maturity and evolution of a company. Though the S-curve is a great way to plot and visualize a business cycle, as well as current vs. future state, we leave it to Gartner and other leading research firms to speculate on the latest and greatest technologies and how they will evolve. For this Data as a Service analysis, we keep a regular pulse on Gartner’s hype cycle for data management.
In brief, the Gartner hype cycle showcases which technology is worthwhile to adopt and the timeline in which you should consider adopting it. The first part of the curve—the highest peak—showcases the areas filled with hype from the media. The prospects listed here are new and exciting, but also unfamiliar because there’s such little adoption in the marketplace. That lack of adoption means that the risks are relatively unknown. As a technology moves through the hype cycle, the costs and benefits become clearer and more defined, which in turn makes these solutions less risky to adopt. Some technologies will move quickly through as adoption picks up steam, whereas others will stall out in the Trough of Disillusionment.
Take a look at where Data as a Service sits in this recent Gartner hype cycle.
You can see that Data as a Service is on the rise but Gartner deems it still 5-10 years from the Plateau of Productivity, or where it’s estimated that high-growth adoption will kick in. This tells us that DaaS has some serious staying-power, which is no surprise due to its ability to tap into journey analytics and humanize big data and offer unprecedented glimpses into consumer and employee behavior. But for now, DaaS is sitting comfortably on the slope upward of positive media hype. It’s still early enough on that many corporations are unsure about the costs and benefits. It’s that ambiguity that we hope to clear up in this post, so let’s get to it.
We hope you’ll find our break down of DaaS fascinating even if you don’t read about R-Squared or Python for fun (but especially if you do). Fascinating or not, our post will take you through how to mitigate disasters, gain buy-in, and refine business strategies using data-driven insights.
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