Technology landscapes like these are “eye charts”.
While meant to classify vendors and technologies, when viewed from a distance they convey the message that selecting technologies is a daunting and overwhelming exercise. Matt Turck from Firstmark Capital recently updated his Big Data landscape for 2017. His IoT landscape probably could use a refresh soon, as IoT is a rapidly evolving ecosystem.
The domain we work in unfortunately is surrounded by a lot of hype. If you take a look at a recent Gartner Hype Cycle, you will see that concepts such as data science, prescriptive analytics, and big data are near the “peak of inflated expectations”, with Internet of Things squarely at the top!
In a prior blog post called “Why Your Analytics of Things Aspirations will Fail” we discussed approaches to take to avoid the hitting bottom in the “trough of disillusionment” with respect to your data.
But what about technology? How does one go about selecting a set of technologies to match an initiative or an idea? First Analytics is a strong believer in the hands-on approach. Nothing makes a case better than working with real data and real tools. Many proprietary software vendors support proof-of-concepts or pilots. And, of course, most of the open source tools cost nothing, other than time and effort, to evaluate.
Through our Collaborative Analytics Lab service, we can help you pre-select technologies based on the envisioned use case and its requirements. We then set up a laboratory environment, either on your systems or one that we host, where we work side by side with your team to do a full vetting of the candidate technologies.
In making investment decisions that involve time, resources, and dollars, it is best to support those decisions with facts derived from data and real examples — without taking a lot of time get those facts. That’s what labs like these are designed for.