Clinton Scott, Managing Director, TechSoft International

By Clinton Scott, Managing Director at TechSoft International

We are not short of analytics tools; many businesses have at least three at hand that they use to fuel data-driven decisions. But it is the bringing these tools together to get to a point where data is ready for analysis where the problem lies. It is this challenge that has birthed the notion of hyperconverged analytics.

Hyperconverged analytics is the combination of the capabilities, tools and data we have on hand that create an all-encompassing analytics experience. If it were a system, it could be considered as a single-engine that marries visual analytics, data science and streaming analytics in one location. The results? The acceleration of insights and actions for businesses from their data.

The nitty-gritty

Businesses rely on data to ultimately predict actions and behaviour, which then allows for deliberate efforts to follow. With hyperconverged analytics, you are creating an environment that now combines a smart, immersive experience with real-time or streaming (analytics) side-by-side. When coupled with data science, you start to benefit from your data.

While many vendors try and lock you down to a single analytics platform, the concept of hyperconverged analytics extends past the tools. It allows you to mix and match or plug and play different components and systems to build the analytics architecture that best suits your business. Ultimately analytics only works if it is open.

With hyperconverged analytics, you get to build analytics applications that are immersive and smart. They pool the best results from the visual analytics, streaming analytics, data science, artificial intelligence, machine learning, model ops, and auto ML tools you have in place and extend this to a visual representation of your business—all in real-time. So, you get to build applications that use as much or as little of these components as you dictate. 

The reasoning

The primary reason that hyperconverged analytics has arisen is that customers were tired of just using analytics for a historical view of their business. As we digitally transform and the edge becomes more intelligent, we are getting more real-time data from devices all over the organisation, which can give real-time insights if combined with data science. But the legacy model is flawed because your experts must be alerted of a need, build a model, and then send through the detail they have uncovered.

It is this concept of prolonged time to analytics that has been the catalyst for speeding up the process and bringing analytics closer to the application. When you blend data science into a centralised platform that oversees your data policies and your data behaviour, then you don't need to rely on the data science team for every query. It is putting analytics into the hands of more people in the business without the need for expensive skills.

The role of streaming analytics

Streaming data is a real game-changer for business and data science teams. Open source streaming solutions like Kafka as an example is sending a deluge of data from the edge and IoT devices that are hitting your data lake every second. But if it is just directed to a data store, it is only data. What a hyperconverged analytics engine does is connect to streaming data sources. It collects your real-time data and then transforms or aggregates these real-time data streams and forwards them to the tool you want to use to crunch this data, and transform it into information.

It is unchartered territory for some as it is very different building a platform that supports streaming data sources than the traditional rows of static data.

No longer potluck

With hyperconverged analytics, you take the guesswork out of analytics, and you create an environment where you can access multi-layered data sources with a centralised view that you create. You have control of and that you take down to the application layer. Making the process smarter and allowing you to democratise and operationalise artificial intelligence and machine learning across your business.

The net result is that data becomes the first-class citizen it needs to be, and your business can view yesterday and today while making real decisions for tomorrow.

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