Data + CDOs  |  Sharmila Mulligan  |  October 18, 2018

What is Continuous Intelligence?

It’s About Frictionless Analytical Cycle Time for High-Frequency Insights from All Data

Continuous Intelligence (CI) from all your data is not another phrase to describe real-time or speed or throughput. It’s about frictionless cycle time to derive continuous business value from all data. It’s how quickly can you get to all data, accelerate the analysis you need, no matter how off the beaten track it is, no matter how may data sources there are or how vast the volumes. It’s about not doing this once, but automating it so it’s continuous and frictionless.

What good is a speedy in-memory analytics solution if it leads you to a new chain of thought that requires going all the way back to loading more data, modeling it, integrating and tuning your dashboard. Analytics that is disconnected by separated modules, separate tasks and specialized skills, steals time away from what matters most today – which is timely non-stop information. In today’s Enterprise organizations, the digital revolution demands speed regardless of data complexity. Business lines care about seeing all the data, immediately and continuously. They do not expect to get stuck on an IT-established dashboard with rigid drill paths that limit their ability to answer critical questions on the spot. Businesses focused on revenue growth acknowledge that analytics cannot tolerate a “punctuated” analytic pipeline.

Enter Continuous Intelligence (CI) Solutions. Continuous Intelligence exists in a frictionless state and enables the business to feed off continuous high-frequency, intuitive insights from all data. CI solutions are new. And it’s not what Business Intelligence (BI) tools can do. BI was not designed for complexity nor was it designed for the digital revolution where more information, faster, is the only way for businesses to prosper.

The original Big Data vision was to move data from all sorts of internal and external sources into Big Data platforms to coalesce it in one place. But the motley collection of data tables and files being scooped from sources into “data lakes” is incompatible with the existing BI tools with architectures that were not designed for the task and could not provide for fast exploration for insights at scale. This introduced yet a new, painful step. A step IT thought needed to be added into the already punctuated analytic pipeline. Thus a separate and new module was born: Data Wrangling. The net result however is that piling all the data in one place to wrangle it requires more work to make it usable. In reality, this drained the value of the whole effort because this skills-dependent and separate “wrangling” module is not sustainable and inserts yet another disconnected step with more IT-dependency. What good is IT wrangling if it slows down reaching smart daily decisions from the latest data? Whether wrangling or modeling data from their original sources or from an aggregated data lake it’s time to stop adding yet another tool or module in your already-slow BI workflow. There’s no reason to have an even more punctuated analytic pipeline.

AI-driven analytics solves this problem by applying the immense power of today’s data processing platforms, like Spark™ and machine learning, to “automatically interpret and harmonize” data from disparate sources. No manual modeling, no workflows, no reliance on experts, no analytic bottlenecks. It reads every data value via machine algorithms, from every dimension in every source, to infer and harmonize what’s in the data and then automatically blends and harmonizes sources to deliver continuous insights. It employs point-and-click power on massive complexity, terabytes, and sources with 100’s of gnarly dimensions. No human, no army of IT people can do this right and do this in seconds or minutes. Only a machine can. Anyone can now point to an AI-driven analytics system to complex sources of data, click ‘infer and harmonize’ and the system does the work and immediately sends continuous visual insights to the business. Data for business decision-making becomes continuous.

Continuous Intelligence gives organizations the ability to “cycle” though analysis from start to finish without hand-off’s, wait-state’s, errors and constant requests to IT people. Instead with AI and machine-learning, Continuous insights from your most complex data feed right into visual insights with business context. The machine discovers every nugget of insight across vast amounts of data and dimensions. The business can click on any information and discover new insights unconstrained and collaborate in context to end Team debates on whether or not the data is driving the right business decision.

The most valuable thing you can do for your organization today is cut the time and myriad of tools needed to get information from vast amounts of data. Getting to a frictionless state and letting AI-driven solutions power high-frequency insights, all the time, will pay real dividends. It aligns with what organizations already realize, which is that prospering, requires a maniacal focus on driving continuous business value from data.

That’s Continuous Intelligence solutions. Fluid, coherent and frictionless. And that’s not BI.

To learn more about Continuous Intelligence, read Forbes’ original article: What is Continuous Intelligence?

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