Big data

Semantic Technology Is Not Only For Data Geeks

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You can’t bring up semantics without someone inserting an apology for the geekiness of the discussion. If you’re a data person like me, geek away! But for everyone else, it’s a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.

It begins with a seemingly innocent set of questions:

“Is there a better way to master my data?”

“Is there a better way to understand the data I have?”

“Is there a better way to bring data and content together?”

“Is there a better way to personalize data and insight to be relevant?”

Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They’re born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester’s Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.

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Let’s Break All The Data Rules!

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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?

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Is Zombie Data Taking Over?

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It is easy to get ahead of ourselves with all the innovation happening with data and analytics. I wouldn’t call it hype, as that would imply no value or competency has been achieved. But I would say that what is bright, shiny, and new is always more interesting than the ordinary.

And, to be frank, there is still a lot of ordinary in our data management world.

In fact, over the past couple of weeks, discussions with companies have uncommonly focused on the ordinary. This in some ways appeared to be unusual because questions focused on the basic foundational aspects of data management and governance — and for companies that I have seen talk publicly about their data management successes.

“Where do I clean the data?”

“How do I get the business to invest in data?”

“How do I get a single customer view of my customer for marketing?”

What this tells me is that companies are under siege by zombie data.

Data is living in our business under outdated data policies and rules. Data processes and systems are persisting single-purpose data. As data pros turn over application rocks and navigate through the database bogs to centralize data for analytics and virtualize views for new data capabilities, zombie data is lurching out to consume more of the environment, blocking other potential insight to keep the status quo.

The questions you and your data professional cohorts are asking, as illustrated above, are anything but basic. The fact that these foundational building blocks have to be assessed once again demonstrates that organizations are on a path to crush the zombie data siege, democratize data and insight, and advance the business.

Keep asking basic questions — if you aren’t, zombie data will eventually take over, and you and your organization will become part of the walking dead.

To defend your business from zombie data, read:

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Top 10 Tips for Data Warehouse Developers

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I’ve been reading some excellent material lately about the perilous task of building data warehouse capability. It cause me to reflect on my own learning from running large enterprise data […]