Tricia Wang, Sudden Compass | IBM Data Science For All
Channel: SiliconANGLE theCUBE
Date: 2017-11-01

Tricia Wang, Sudden Compass, talks with Dave Vellante and John Walls at IBM Data Science For All

#DSforALL #theCUBE 

https://siliconangle.com/2017/11/06/thick-data-sees-the-market-future-when-big-data-cant-dsforall-guestoftheweek/

‘Thick data’ sees the market future when big data can’t

Data analytics is a numbers game, right? Adding more data points solves inaccurate algorithm outputs. Actually, no. Outputs will not predict human behavior until inputs are as complex, unexpected and sometimes as contradictory as humans themselves.

“Customers are really the most unpredictable, the most unknown, and the most difficult-to-quantify thing for any business,” said Tricia Wang (pictured), ethnographer and co-founder of Sudden Compass, a New York City-based consultancy that helps companies figure out why their best efforts in quantitative data analytics fall short of expectations.

It’s crucial to marry big data with “thick data,” which is difficult to quantify but may hone in closer to consumers, according to Wang. Ye olde marketing data variables like education and income are so-so predictors of consumer behavior, Wang — a self-described “global tech ethnographer” — said in an interview during the IBM Data Science for All event in NYC She spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio. (* Disclosure below.)

“The new networked customer of today has multiple identities and is better understood when in relationship to other people,” Wang said. Quantitative data science is indispensable for sca; however, thick data takes individual consumers’ temperature more precisely and offers depth. “That’s why you need to combine both to be able to make effective decisions,” she added.

This week, theCUBE spotlights Tricia Wang in our Guest of the Week feature.

Telling a data story
Clearly, some ingredient is missing in the data analytics stew at most companies. Gartner Inc. researchers have predicted that 60 percent of big data projects begun this year will fail. This explains the data software and employee shopping sprees now common in enterprises. Seeking a magic wand in a software tool or outside expertise will only get a company part of the way to success, according to Wang. At the last mile, it’s an inside job.

“You’ve hired the right expert; you have bought the right tools — but we now need to make sure that we’re creating data literacy among decision makers,” Wang said. The data-literate are able to tell stories. These stories may combine quantitative big data with some unquantifiable observation from real life.

Rendering qualitative data into a form that can hold its own can be tricky. Social media content like tweets or venue reviews are qualitative data that may be juiced into some definite or quantitative insight. Supporting data literacy efforts, Yelp Inc. has opened its massive store of reviews to technology students in its Yelp Dataset Challenge. The user-generated reviews site awards researchers who find innovative ways to use the data in natural language processing and sentiment analysis, among other things.

“Some decisions need to be made (which can be subjective) when deciding how to transform the qualitative data into quantitative form,” wrote Dr. Kirk Borne, principal data scientist at Booz Allen Hamilton, in a blog post for MapR Technologies Inc. “That is a challenge, but it is also a rich opportunity — there are far more subtleties and intricacies in language that we can use to extract deeper understanding and finer shades of meaning from our qualitative data sources about our customers, employees and partners.”

Analytics and anthropology
Sometimes qualitative data is best in the raw. Working as an ethnographer for Nokia Corp. in 2009, Wang experienced the predictive power of unquantifiable data first hand. In her fieldwork studying emerging customer trends in China, it soon became clear that flip phones were not long for this world.

“Look, your company’s about to go out of business, because people don’t want to buy your feature phones anymore. They’re going to want to buy smartphones,” Wang stated. But with no quantitative data in hand, she had a tough time capturing the attention of the corporation’s data scientists. “Back then, they were saying, ‘No, we don’t even consider your data set to be worthwhile to even look at,” she said.

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Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of IBM Data Science for All. (* Disclosure: TheCUBE is a paid media partner for the IBM Data Science for All event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)