{"id":4687,"date":"2018-06-25T19:56:15","date_gmt":"2018-06-25T19:56:15","guid":{"rendered":"https:\/\/www.rocketsource.com\/?p=4687"},"modified":"2024-01-24T23:27:50","modified_gmt":"2024-01-24T23:27:50","slug":"data-as-a-service","status":"publish","type":"post","link":"https:\/\/www.rocketsource.com\/blog\/data-as-a-service\/","title":{"rendered":"Data as a Service: The What, Why, How, Who and When"},"content":{"rendered":"

The thought of Data as a Service <\/em>(DaaS<\/em>) often conjures up images of complex algorithms and machine learning, but at RocketSource we think of making data sets accurate, easy to digest and actionable. That\u2019s not an easy task. Our years of experience as a digital consulting firm have shown us that executives continually struggle to harness the firehose of information pouring into their businesses. 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.<\/p>\n

You\u2019ll be hard-pressed to find an executive in today\u2019s world who doesn\u2019t 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<\/a> and, most importantly, act on it.<\/p>\n

Data analytics is far more complex than setting up algorithms to feed into databases. To tap into the insights buried in datasets in a meaningful way \u2014 one that yields tangible results \u2014 requires 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.<\/p>\n

Quick reading note: <\/em>We are Buckley Barlow and Jonathan Greene, co-founders of RocketSource and we’re writing this post together in an effort to apply our collective skill sets. Our goal is to give you as much insight into 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 audio soundbite in the post to hear us personally dive deeper into this rich topic.<\/p>\n

Data as a Service on the Gartner Hype Cycle<\/h2>\n

If you\u2019re 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 in 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<\/a> 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 versus future state, we leave it to Gartner and other leading research firms to speculate on how the latest and greatest technologies will evolve. For this Data as a Service analysis, we keep a regular pulse on Gartner\u2019s Hype Cycle for data management.<\/a><\/p>\n

In brief, the Gartner Hype Cycle showcases which technology is worth adopting and the timeline in which you should consider adopting it. The first part of the curve \u2014 the highest peak \u2014 showcases the areas filled with hype from the media. The prospects listed here are new and exciting, but also unfamiliar because there’s been little adoption in the marketplace, so 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 through\u00a0quickly as adoption picks up steam, whereas others will stall out in the Trough of Disillusionment.<\/p>\n

Take a look at where Data as a Service sits in this recent Gartner Hype Cycle.<\/p>\n

\"Data<\/a>

Source: Gartner (September 2017)<\/p><\/div>\n

You can see that Data as a Service is on the rise but Gartner deems that it’s still 5-10 years from the Plateau of Productivity, 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\u00a0humanize big data<\/a> and offer unprecedented glimpses into consumer and employee behavior. But for now, DaaS is sitting comfortably on the upward slope of positive media hype. It’s still early enough 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.<\/p>\n

We hope you’ll find our breakdown of DaaS fascinating, even if you don\u2019t 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.<\/p>\n

What is Data as a Service?<\/h2>\n\r\n\t\t
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