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.
The fourth age of analytics is hyperconverged
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. This technology now includes a framework for extending the visual analytics palette provided by Tibco Spotfire Mods (an analytics tailoring and modeling technology), invoking no-code data science functionality alongside streaming analytics and a platform-agnostic approach to what is being called ModelOps.
What is ModelOps?
Coined by analyst house Gartner and defined here, “ModelOps (or AI model operationalization) is focused primarily on the governance and lifecycle management of a wide range of operationalized Artificial Intelligence (AI) and decision models, including Machine Learning (ML), knowledge graphs, rules, optimization, linguistic and agent-based models.”
So to put all that in more straightforward terms, these are technologies that enable data scientists to build analytics models and then use tools (in this case, some low-code accelerators) to connect the results of those analytics to another application library, Application Programming Interface (API) or workflow. Once again, it’s data analytics hyperconvergence i.e. it’s a case of two (or more) data functions being brought together, conjoined, coalesced or connected for the greater good.
“With every release, we improve the productivity of all stakeholders, data scientists, business analysts and line-of-business leaders, increasing the impact derived from analytics for our customers. The automation of tasks and the convergence of analytics, data science and DevOps is central to our approach,” said Michael O’Connell, chief analytics officer, Tibco.
O’Connell points out that now specifically, his organization’s Spotfire 11 software delivers embedded data science workflows, lowering the barrier for non-technical professionals to add Python language and R language data functions to Spotfire analyses. Again, embedded data science spells hyperconverged data analytics. Spotfire 11 also includes new community-sourced visualizations and a growing library of Python and R data functions covering feature engineering, machine learning and geo-analytics.
Open source extends the hyperconverged hyperloop
The company also says that forthcoming functionality in Tibco ModelOps enables users to have access to simplified model deployment and monitoring.
“This empowers organisations to manage thousands of statistical, machine learning and rules-based models from commercial or open source environments, enabling model lifecycle governance and transparency for algorithmic decisions and AI-guided customer interactions,” notes Tibco’s O’Connell and team, in a product descriptor statement.
Where next on the hyperconverged data analytics and data science hyperloop then? Well, anywhere and any part of the business that you can plug in a digital connection at in order to benefit from extended low-code-enabled visualization-simplified model-accelerated data analytics, basically.
When we do all go back to work in an office one day (maybe, possibly, sometimes, occasionally), if your lunch orders have been autocompleted for you and already billed to your workplace food & beverage account, it may be because hyperconverged data analytics has modeled your consumption preferences into a visualization dashboard that has API-connected to a salt-beef sandwich delivery service and helped to dispatch your order and brought it to your desk.
They still forgot the extra pickles though? We’ll get there soon, it’s only a matter of time.
