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We used to have plain old cars, models and data analytics. Today we’ve moved beyond those basic functional things and entered the age of super-extremes and turbo-charges. This means that the best cars (Lamborghini, Ferrari etc.) are now supercars, the best models (Cindy Crawford, Tyson Beckford etc.) are now supermodels and the best data analytics (there are many contenders out there) are superanalytics functions.
But highly-advanced data analytics systems (perhaps unsurprisingly) aren’t quite racy enough to call themselves superanalytics, the technology industry prefers to talk about hyperconverged analytics (which is the supercar/model version, effectively) instead.
From super to hyper (infinity & beyond)
Obviously coming from the Greek ‘above’ or ‘over’, hyper is usually the tech world’s preferred prefix to denote any part of an IT system that can provide higher-level abstracted control of the powerful technology beneath. So for example, a hypervisor is software that runs multiple ‘guest’ virtual machines (VMs) on one ‘host’ machine so that each virtual computer can share resources such as memory and processing. Equally, hyper-convergence is a term denoting an IT framework that provides a higher-level view and control system which brings together data storage, compute processing, and network connectivity into one single entity to reduce complexity. It is perhaps no coincidence that the word hypervisor is used as a stronger variant of the supervisor, a term for the core kernel of an operating system.
The transition from plain old software to super software (a term that was never actually used) to hyper-level software has given us the chance to apply hyper functions to data analytics. Enterprise data company Tibco is aiming to bring that higher-level functionality and usability aspect to data through the launch of its TIBCO Hyperconverged Analytics product. So if hyper-anything is all about upper-tier power, how would this work with a data analytics product?
Tibco wants us to think of this software as more immersive and smart for data-driven business use. What that ‘immersive’ marketing speak means in real terms is data analytics functionality that can be used by data scientists, but also as a function that is ‘immersed’ into (or at least used alongside) the functions of business apps that regular business people might use. This product sits with the firm’s other hyper-converged analytics tools, which in this case include TIBCO Spotfire 11 and TIBCO Cloud Data Streams.
The playbook here centralizes around being able to work with ‘any’ kind of data (there are perhaps 13 basic kinds) to get predictive insights. Aiming to bring a higher degree of any-user democratization to the use of data analytics, Tibco thinks its latest data tools will help converge workplace roles currently separated out between market analytics specialists with business backgrounds and thoroughbred data science specialists with software engineering backgrounds. This is supposed to be a zone somewhere ‘beyond business intelligence’ where all users have the ability to connect to real-time data, without using code, for real-time insights through the Tibco Spotfire visualizations product.
The company calls it a connected and holistic data experience that embraces the true democratization of analytics to support an entire business’s needs around data analytics. Good luck fitting that on a t-shirt.
“Expert data sources are scarce and costly to secure, yet the power that data can yield remains the number-one success factor of an effective organization. By removing the need for specialist skills for every data request, we are giving all customers the power to re-imagine what analytics can do for them,” said Michael O’Connell, chief analytics officer, Tibco. “With TIBCO Hyperconverged Analytics, we are shortening the time it takes to deliver impactful, predictive insights from all kinds of data, putting the power of insights into the hands of the business.”
Deeper into the product itself, Tibco says that by bringing a ‘power to the people’ element to data analytics, we will all be able to access data analytics intelligence if we want to.
Technology is becoming increasingly component-based
Data scientists would normally need to provide us with ‘analytics components’ to bring together what is usually a diverse and complex set of different data analytics functions together in order to perform complex statistical calculations. Tibco’s strategy here is to enable faster analytics component creation and an all-in-Spotfire simplified experience. The company’s Spotfire Mods framework pushes democratization further by helping to build purpose-built analytic applications that are immersive, smart, and real-time. This, for Tibco, is the validation of and definition of hyper-converged analytics.
The only proviso we should think about here (or at least the first proviso) is just how much control we are giving businesspeople with these new powerful data tools. If business decisions are being made by so-called ‘citizen data scientists’ without extensive experience in the field, then we will need to know the provenance of any technologies being applied at the coalface of business i.e. who made this stuff, asked for it, applied it and who set it free in the business?
Tibco will have provisioned for that provenance factor in its platform as comprehensively as any other major player (Tableau’s work with analytics through data dashboards is a perfect example), but enterprise organizations applying these kinds of technologies should bear that factor in mind.
If hyper is the new super, then the software industry is probably already plotting to supercharge hyper into ultra, colossal, or mammoth. Hyperpro-super plus-megamix data is on the way, probably.