When I think about data, I can't help but think about hockey. As a passionate hockey mom, it's hard to separate my conversations about data all week with clients from the practices and games I sit through, screaming encouragement to my son and his team (sometimes to the embarrassment of my husband!). So when I recently saw a documentary on the building of the Russian hockey team that our miracle US hockey team beat at the 1980 Olympics, the story of Anatoli Tarsov stuck with me. 

Before the 1960s, Russia didn't have a hockey team. Then the Communist party determined that it was critical that Russia build one — and compete on the world stage. They selected Anatoli Tarsov to build the team and coach. He couldn't see films on hockey. He couldn't watch teams play. There was no reference on how to play the game. And yet, he built a world-class hockey club that not only beat the great Nordic teams but went on to crush the Canadian teams that were the standard for hockey excellence.

This is a lesson for us all when it comes to data. Do we stick with our standards and recipes from Inmon and Kimball? Do we follow check-box assessments from CMMI, DM-BOK, or TOGAF's information architecture framework? Do we rely on governance compliance to police our data?

Or do we break the rules and create our own that are based on outcomes and results? This might be the scarier path. This might be the riskier path. But do you want data to be where your business needs it, or do you want to predefine, constrain, and bias the insight?

Companies that challenge preexisting rules are winning. Some quick back-of-the-napkin stats show that nontechnology companies that break data rules and think insight first get shareholder results that look more like NASDAQ technology companies and trail Internet Giants by about 16% over the past three years. In contrast, these rule-breaking companies outperform their S&P cohorts by almost 20% and twice as much as the DJIA.

What does rule-breaking look like?

1) Semantic models are productionalized and not dusty conceptual architectural artifacts.

2) Semantic models learn and evolve to account for all points of view for personalized fit-for-purpose systems.

3) Data governance is a training and policy management practice rather than a policing effort for compliance.

4) These companies surrender to the intelligence of the system and own what matters — subject matter expertise over platform.

It's okay to challenge preconceived notions. You have permission to think creatively and critically if it means it advances the business. You can throw away the rule book.

So listen to NHL great Wayne Gretsky: "A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be." And, "You miss 100% of the shots you don't take."

Get data to where the business is going — take a shot! Just watch out for my goalie son; he has a mean glove save!

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