Big data comes of age

It’s too easy these days to be tired of big data, with all the defining and redefining, marketing, and Hadooping. I can’t help but think to myself, “Just shut up and do it!” Of course, some organizations have gone and done it. Now a new report from TDWI Research describes the common stages they go through on the way to making big data a permanent part of their toolset.

Research director Fern Halper and Sixth Sense Advisors CEO Krish Krishnan also offer a tool anyone can use to get a quick rating of any organization’s maturity in big data analytics. (See below.)

Halper and Krishnan’s maturity model can help answer an organization’s particular unanswerables: Do we have the right stuff? What problems should we prepare for? How do we evangelize the program? It can help guide structure and processes, define goals, shape vision, reassure, or warn. They also shoot down some false assumptions one might form from all the industry’s hoopla.

… Big data maturity is not simply about having some technology in place to deal with high volumes of data. Nor is it simply about using social media to analyze buzz about your brand. It is a journey that involves building an ecosystem that includes technologies, data management, analytics, governance, and organizational components.

Their five levels of maturity — six if you count the “Chasm” — start with the “nascent,” when you’re still sniffing out the possibilities. Then comes pre-adoption, early adoption, The Chasm, corporate adoption, and mature/visionary. Organizations being fundamentally social, progress can be uneven and confusing. Perhaps the infrastructure is in place but proficiency in data analysis lags.

Descriptions of each phase cover the whole field, from assembling the right stuff to helping big data find a warm spot the organization’s heart.

“The Chasm” sounds like the tough one. One might imagine the 1944 preparations for the Allied invasion of France. Funding, data “ownership,” skills, and governance have to be ready for full corporate adoption. But, wouldn’t you know it, it’s culture and politics that often stem big data.

The Chasm’s complexity shows in this section’s word count: more than 1000, which is several hundred words longer than the next longest sections, “early adoption” and “corporate adoption.” It’s also more than twice the length of the first and last sections, “nascent” and “mature/visionary.”

By the fourth stage and fifth stages, “corporate adoption” and “mature/visionary,” big data analytics has proven itself. In the “corporate adoption,” big data supports the organization. In “mature/visionary,” the organization finally supports big data. Big data finally gets tenure.

One more thing: The “TDWI Big Data Maturity Model Guide: Interpreting Your Assessment Score” is one of the industry’s better written pieces. Halper and Krishnan have managed to write clear and interesting explanations and to avoid the gray, fatigued phrases that substitute for actual thought in so many other pieces. In their 8500 words, they actually make the path to mature big data analytics bright and clear.

Assess your progress here.

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