Augmenting Opinion with Data

For those of us who are more left-brained thinkers, it's a pretty exciting time right now in 2013.

Silos to data have been broken down in many instances... and there are a plethora of data sources available for fee or for free that can be tied together.

Data visualization tools have progressed well beyond the visual and data processing constraints of Microsoft Excel. Business people can make things happen in tools like Tableau or SiSense, and D3 is emerging for situations that benefit from significant customization and drilldown.

And perhaps most importantly, the "data-driven" approach is moving beyond early adopters and becoming something that many consumer brands and enterprises want to both know more about and execute more effectively.

Call it "revenge of the nerds" if you will.

The secular trend in business is to give data a seat at the decision-making table. Savvy executives understand that there are a lot of ways to measure expertise. And the person who asserts him/herself as an expert might not be as infallible as once thought. He/she may bring biases to the table that data can confirm one way or another.

A multidisciplinary approach to data is necessary to take full advantage of the opportunities. Why? Because it's a problem with different dynamics that require different skill sets that often don't lie in a single person.

1. Opportunity identification -- a keen knowledge of what is out there, as well as the classes of data that are generally easy and those that are difficult to obtain.
2. Data "creativity" and translation to business problems -- the ability to listen to client needs/requirements and propose/present data-driven solutions to help improve upon current decision-making.
3. Understanding of statistics -- familiarity with different statistical methods used to build explanatory and predictive models to enhance decision-making that is currently done by feel or more subjective criteria. Key here is to employ statistics *accurately* and let the conclusions come forth from the data.
4. Data visualization -- knowledge of tools/resources available across the aforementioned tools and technologies such as Tableau, SiSense, D3, etc.

This is what it takes to become more data-driven. Fall down in one of these areas, and data efforts can miss the point.

What I find that most enterprises and consumer brands want is the best of all worlds. How do I take advantage of the people and expertise we have, but augment them with the conclusions and accuracy that only data can provide?

Nobody wants data to replace original thought, but rather to augment it and turn people into smart, discerning, and data-driven powerhouses.