Continuous intelligence from all your data is not another phrase to describe real-time, speed or throughput. It's about frictionless cycle time to derive continuous business value from all data. It's a modern machine-driven approach to analytics that allows you to quickly get to all of your data and accelerate the analysis you need, no matter how off the beaten track it is, no matter how many data sources there are or how vast the volumes. It's about not doing this once but letting the machine automate it so it's continuous and frictionless.
There have been ways to do analytics fast by employing various tricks and tools. But what good is a speedy 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 it and tuning your dashboards every time the business has new questions? Analytics that are disconnected by separate modules, separate tasks and separate teams with specialized skills steal time away from what matters most today which is timely nonstop information from all your data.
In today's enterprise organizations, the digital revolution necessitates speed regardless of data complexity. Business lines care about seeing all the data immediately and continuously. They do not expect to get stuck with an IT-established dashboard with rigid drill paths that limit their ability to answer critical questions on the spot. Businesses focused on revenue growth and capitalizing on the digital revolution understand that today's analytics cannot tolerate a punctuated analytic pipeline.
How Continuous Intelligence Differs From Business Intelligence
Continuous intelligence (CI) exists in a frictionless state and enables the business to feed off continuous, high-frequency, intuitive insights from all data. CI solutions are new. CI is an AI-based, machine-driven way to continuously interpret data, discover patterns and learn what's of value in the data. This allows business users to mash up and blend disparate data intelligently with the objective of discovering new insights constantly and revealing it as a data story with complete context. It does away with human biases in each step of the data pipeline and replaces them with a smart machine and AI that discovers everything in your the data, no matter how complex.
Business intelligence (BI) tools, on the other hand, do not employ machine learning or AI. Instead, BI relies on people to orchestrate each step using a BI tool, all the way from data access to the construction of insights in BI dashboards. BI was not designed for complexity, nor was it created for the digital revolution where information is expected to be accessed at an accelerated rate.
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 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.
Now, AI-driven analytics has arrived on the scene by applying the immense power of today's data processing platforms (e.g., Spark) to automatically interpret and harmonize data from disparate sources. Anyone can now point an AI-driven analytics system to complex sources of data and 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.
Getting Best Out Of Your Data:
If you're a business leader looking to gain continuous intelligence from a variety of disparate data sources and see relevant insights for actionable decisions, here are three best practices to follow:
Orient the evaluation around a business use case, and present the use case in an interactive data story so business stakeholders can see the value of continuous intelligence from their data.
Focus your stakeholders on the value of the information in the insights. Ensure they know what they are looking for. Let them explore the insights firsthand and collaborate with each other in real time via their own point-and-click experience. Leverage a solution that uses AI to speed the collection of data from disparate sources, and let the AI analytics solution automate the generation of the data stories with the end goal of putting CI insights into the hands of the business stakeholders fast.
In the modern enterprise, all data, across all sources, needs to be leveraged into insights. Getting to a frictionless state and letting AI-driven solutions power high-frequency insights all the time is how continuous intelligence can help.