There was a time when words like analytics, business intelligence, and big data could be politely left up to the "specialists."

That time has come and gone. Unfortunately, while huge strides have been made regarding data collection, within many organizations a painful gulf now exists between their numbers and their decisions.

According to the MIT Technology Review, only 0.5 percent of all data "is ever analyzed." Even worse, that bleak figure was calculated back in 2013. Today, experts like the Executive Director of the University College of London's Big Data Institute, Patrick Wolfe, say "that percentage is shrinking."

Naturally, providing company-wide access to business intelligence tools and just telling everyone to dive in creates its own set of problems. As an April 2016 McKinsey & Company Study found, the number one challenge companies face with data and analytics is "designing an appropriate organizational structure to support analytics activities."

The keywords there are "organizational structure": ensuring that your approach walks the line between data sharing without data drowning.

Why shared data improves business

Data on the use of data paints a clear portrait: Analytics-driven companies outperform their non-analytical counterparts on all fronts as long as one crucial requirement is met.

Consumer-facing products like FitBit and Nike+, for example, give individuals the ability to track everything from steps, to calories, to sleep, to heart rate. However, the biggest benefit of all this data is happening at the macro level in places like the healthcare sector where it can be shared and integrated.

Delta airlines recently took a page out of the same playbook to alleviate one of their customer's major pain points: baggage. By sharing their in-house data externally, travelers can now track their bags via an app.

"The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results." Andrew McAfee & Erik Brynjolfsson, Harvard Business Review

And, of course, e-commerce companies like Amazon and Spotify have mastered the art of predictive analytics by using data from both individual customers as well as overall trends to recommend products with near clairvoyant accuracy.

For Spotify in particular, this shared approach has been a cornerstone of their 27 percent conversion rate from free-trials to paying customers, topping Dropbox's once-lauded four percent conversion rate and destroying Google Drive's 0.5 percent.

Each of these applications has one thing in common: they've all broken down traditional data silos that hold organizations back from sharing the information they already have and putting it to use.

How to stay afloat in the data flood

Once the data faucet is turned on, the biggest risk is drowning. And that's why the one life preserver you need is simplicity.

Instead of using tools that disseminate data indiscriminately, balance comes from two ingredients: aggregated data in a central location coupled with departmental and personalized delivery.

For instance, numerous big data solutions require the formation of in-house teams to collect and analyze input first and then distribute that output to the people who need it. Think of these as data middlemen.

Simplicity means using those same "data specialists" within your organization not to stand between your teams and the information they need, but instead to serve as "data facilitators."

If "data facilitators" are outside the scope of your budget, newer platforms like Sisense--who works with diverse organizations like eBay, ESPN, and even NASA -- make visualizations (a hallmark of data simplicity) part and parcel of distribution itself.

The ability to create custom dashboards tied to a single repository gives teams data breadth while providing manageable data depth. Just be sure to limit the number of visualizations on dashboards so they're valuable "at a glance."

"We're all suffering from information overload or data glut. The good news is there might be an easy solution to that, and that's using our eyes more." David McCandless, Data Journalist & TED Talk Presenter

Other single-stack tools--also referred to as "warehouses"--include platforms like Dundas BI, which works similar to Sisense by majoring on custom dashboards and visualizations, and Segment, whose focus extends beyond connecting your data sources by building custom integrations so you can implements changes directly to those systems themselves.

Your life preserver

Regardless of the meta-tool you select--and there are plenty to choose from--simplicity demands two steps:

This one-two punch of simplicity is the only life preserver that will keep your company afloat while at the same time delivering the kind of data-driven results that come from connecting numbers and decisions.