Evolving technology and ongoing innovation are changing the data analytics needs of modern businesses. Companies need data infrastructures that promote discovery and speed the generation of business intelligence (BI).
As we discussed in our last post, many businesses today struggle with sluggish BI generation because they’re operating with outdated and/or fractured analytics infrastructures. Over the past two decades, analytics solutions were purchased, customized and rolled out to fit niche needs within corporate structures; most solutions weren’t designed or implemented with enterprise-wide BI needs in mind.
Incomplete or non-existent interoperability within an organization’s data structure promotes the development of silos, slows or blocks discovery and the flow of critical insights, and blinds companies to opportunities and dangers alike.
Companies with fractured analytics environments are akin to woolly mammoths when the first hunter-gatherers arrived: they are an evolutionary step beyond analog systems and paper-dependent dinosaurs, but completely vulnerable to inventive, agile newcomers in the business environment.
Brain size doesn’t determine business intelligence. Interconnectedness does.
Humans have large brains relative to other animals our size. But is that the defining characteristic of our intelligence? Are we dominant by sheer virtue of cranial volume?
Ask a neuroscientist and he or she will tell you no. Brain density and volume are, in fact, only two factors determining our smarts. Interconnectedness in the brain is another. The more synapses that exist between discrete areas of the human brain, the faster and better the brain can analyze and respond to the external environment.
This concept can be applied to BI infrastructure: the most intelligent businesses will be those that not only have the capacity to collect and store a lot of data, but the interconnectedness to analyze and act on it in unforeseen, innovative ways.
We have a term for this: data democratization.
Enterprise-level AI analytics solutions foster specialization without segregation.
Imagine the data silos within your company as areas of specialization within a brain.
Just as the cerebellum controls voluntary muscle movements, so too does the sales department drive a company’s outreach to consumers. The visual cortex processes information from the eyes and feeds it to the brain’s decision-making center; a company’s marketing division senses consumer preferences and feeds that information to the C-suite, which can then strategize and adjust responses. The information management or IT department is like the temporal lobe: it collects, encodes and stores information for later recall.
Each of those areas, working on its own, probably excels at what it does. But how much is it working with the other areas of your company’s overall “brain?”
Understand this, and you’ll understand how businesses can no longer afford to approach their data infrastructures piecemeal. You can’t augment one area of the company with a customized solution and ignore the rest. Sure, that one area may become more efficient, but your company as a whole won’t become more proficient.
Proficiency proceeds from every area of the company benefitting equally from an analytics infrastructure designed with enterprise-wide needs in mind.
To develop proficiency, a company must address its needs from a “whole-brain” perspective. It must democratize data. It must allow discrete teams to continue to specialize, but simultaneously break down synaptic bottlenecks between them and grow new connections within the organization.
From learning to tool-fashioning to culture-building.
Businesses that are successful going forward will be those that develop the data equivalent of big, interconnected brains and opposable thumbs — they’ll be companies that can store much, recall quickly, and analyze thoroughly in order to innovate and implement on the fly.
As PwC’s US and Global New Business Leader, Vicki Huff Eckert, recently told BizTech, company size won’t correlate to stability in this brave new world. AI-enabled agility will.
“You will have employees starting to collaborate and think differently about what they are capable of,” she said. “I view it personally as a productivity lift. Employees will be able to ask broader questions and do more thorough analysis.”
She noted that automation will free up companies’ human intelligence. Humans can do several things that machines cannot (yet) effectively do: they can intuit, they can understand intent, and they can empathize.
By freeing employees from the some of the drudge work that a robust analysis requires, AI platforms allow companies to think more like humans. They’ll allow companies to concentrate more human resources on discovering, interpreting and meeting human consumers’ needs.
And that’s an exciting thought.
The pace of globalized business already moves faster than humans do. Automation means survival.
As more data solutions employing machine learning and true artificial intelligence are deployed in the marketplace, competitive advantages will be established — and lost — quite literally at the speed of light.
Organizations that don’t modernize now to account for “the coming of the robots” won’t be able to survive long because the business environment will favor only those companies that have developed the ability to instantaneously analyze and adapt.
AI-powered, data analytics platforms that promote democratization, foster enterprise-wide transparency, and feature built-in instrumentation for tracking usage and improving internal accountability represent the critical next step in business intelligence evolution.
Is your company ready to make the leap?