“Data rushes in like a ferocious torrent.
Wisdom can only be found in calm.
Seek shelter in the rock of One Good Question.” - The Zen of Data
We conceive of data as a science, and how technology allows us to interact with data very much reflects that. You come up with a premise or a question, formulate a SQL query, which generates the data, and then you chart or measure it in some way. The results will either confirm or refute your original premise.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=iJust like the scientific method: Question. Hypothesis. Test. Measure. Results. Even the language of data reflects this very scientific approach: Data models, data scientists, data analyst.
Why don’t we ever talk about data art, data storytellers, data dreamers, data Buddhists?
Big Data is changing that. By its very nature, Big Data tools allow people to take a much more playful approach to data.
All the consultants in the world will tell you that to start with Big Data, you need a business use case — a question or hypothesis to test. But the Big Data experts will tell you that the true innovation is found by discovery.
“The good news is that Big Data lends itself to long discovery,” said Jill Dyché, vice president of Thought Leadership with business services and analytics vendor SAS. “Now we can look for patterns we never knew to look for. That kind of exploration is much more practical and cost-effective than ever, and that's super exciting when it comes to looking for unanticipated correlations and patterns.”
Not surprisingly, that kind of open-ended inquiry is primarily the domain of research institutes and Web 2.0 companies.
“I can't go into the CIO and say let's acquire Hadoop and get trained on it and get some of these visualization tools installed because we want to look for stuff we don't know about,” Dyché added “There's no discovery budget in most IT shops."
But maybe there should be — it’s not the craziest idea, when you think about it. After all, many enterprises have innovation centers for incubating new ideas, exploring new solutions to old problems. The new insights you can find with Big Data technologies might make it worthwhile to add a data person to this team – a data explorer, who can play with new and old datasets to find new trends, new insights.
At the very least, Big Data means we rethink how we approach data, both within IT and in the business units.
It’s not just about exploration, though. It’s also about humanizing the data, Om Malik of GigaOm advised in a recent post. That moving beyond being “data informed,” to being “data aware and data intelligent” means using data to create narrative.
“What will it take to build emotive-and-empathic data experiences?” Malik wrote. “Less data science and more data art — which, in other words, means that data wranglers have to develop correlations between data much like the human brain finds context. ”
Along a similar theme, innovative data experts Jeff Bladt and Bob Filbin argued in a recent Harvard Business Review blog post that a data scientist’s real job is not to collect and analyze data, but to translate that into a meaningful story.
“Our challenge as data scientists is to translate this haystack of information into guidance for staff so they can make smart decisions,” Bladt and Filbin wrote. “In short, we're tasked with transforming data into directives. … We ‘humanize’ the data by turning raw numbers into a story about our performance.”
“Data gives you the what, but humans know the why,” the article goes on to say.
Bladt and Filbin outline a three-step process organizations for “rehumanizing” data and finding its story.
This sounds pretty touchy-feely, and maybe it is. But it is not about making the data lie or say whatever executives want it to say.
Instead, it’s about finding what matters in the data, even if it’s not what you were looking for. And it’s about making it meaningful — not just for a few reports, but for the organization.