How Data Analytics Became Hyperconverged

Hyperconvergence is one of the IT watershed tier moments of our age. It is the coming together of technology sources, disciplines, toolsets and functions into one more centralized space. In what might be its 4.0 evolution stage, data and information analysis is now entering a state of hyperconverged analytics. 

But how did we get to hyperconverged analytics and what will the new hyper-stream look like?

First, there was analytics. This was the pre-digital age notion of assessing a life or business outcome based upon a basic balance sheet of pros and cons. Although a possibly apocryphal account, Winston Churchill is said to have made many state-level decisions using this technique, but with just a pencil or pen and paper.

Next, there was data analytics. Combining the use of databases, mathematical models and algorithmic logic, our ability to perform what is now considered to be comparatively rudimentary data analytics was limited by computing processing power, data storage and database transactional capacity for much of the pre-millenial years.

In the third age, we reached big data analytics. This was (and still is) defined to be data analytics that has the breadth and scope to be able to process that level of information that does not normally fit into a traditional relational database. 

Technology firms have been building out increasingly cloud-based big data analytics power to enable our data sources to ‘make analytics calls’ on-demand on a massively big scale and uncover previously hidden patterns and correlations that the vendors love to call insights… and when these so-called insights become actionable insights, then we can start to talk about Artificial Intelligence (AI) and more.

Hyperconverged data analytics is still big data analytics, but it is highly scalable increasingly intelligent data analytics that has been unified with other core data tools and data functions, while it is also dovetailed with other business tools and business functions.

Known for its data integration heritage and track record, Tibco Software Inc is one of a number of enterprise technology organizations attempting to gain recognition in this space. The company’s Predict portfolio of products have over the last few years been engineered towards hyper-status by dint of their expansion and wider integration.

Enhancements to the Tibco Spotfire (for data visualization & analytics), Tibco Streaming (for handling real-time cloud data) and Tibco Data Science (a data science authoring and deployment technology) products are all now available in a single platform. So, hence, a coming together and a hyper-level converging for hyperconverged data analytics.

Aiming to conscientiously incorporate low-code functionality into its data analytics toolkits, Tibco has brought low-code shortcuts to bear as part of its branded TIBCO Hyperconverged Analytics experience.