Ever looked at a spreadsheet or a report blankly lost in numbers? How about a chart or analysis that was visually pleasing but had little to no business context or simply just didn't make any sense? I have and am guilty of creating such monstrosities - and I'm allegedly a data person...why?

As an avid fan of making intangibles tangible through data and numerical analysis, sometimes one can overcomplicate a simple problem statement in the pursuit of an answer when the fact of the matter is that all problems created by humans are generally solvable by the same humans - it's simply a question of identifying different perspectives and reaching common ground.

The other day I was watching the movie "Return of the River" at a local film festival in New York which was balancing the decision of maintaining the economic interests of a small town in western Washington with the impact on salmon populations to the indigenous Indians of the Elwha River, then 2-3% of historical levels, by deciding whether or not to remove a dam several miles upstream.

The beauty of data is making insights within the scope of the aggregate versus making a decision on an abstraction of a single data point is that democratizing that insight keeps us honest and connected to the overarching scope of the problem statement. In this case with the Elwha River, it was balancing the two diametrically opposed perspectives with the facts that energy contribution  by the dam was negligible and a reservoir was interchangeable with  a river by nature enthusiasts - neither perspective held by either party prior to this revelation.

Analytics is a team sport and different perspectives matter. Whether an individual be visual communicator or a numbers person, it's the balance of the art and science that makes analytics meaningful. Where data facilitates enterprise innovation is by grounding all these perspectives in a common aggregate data set, eliminating biases and enlightening us to other perspectives. No matter what the perspective is, it needs to be justified within the scope of the whole - never to be decoupled from the overarching problem statement.

Imagine other challenges that could be solved by democratizing data such as weary eyed residents benefiting from a second set of eyes on the patient history, analytics professionals looking at scientific or environmental data sets to find root cause unobservable to others with potential biases, or thousands of people looking at public sentiment on social media to identify market trends & missing innovation.

No matter what, shared insights on common data sets provide us with the opportunity to transcend our own biases and collectively work towards a democratized innovation. The next question is, what problem do you want to solve first?