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Natural language processing and the power of contextual data

by George Dearing

​Describing a company as 'lean' describes how they handle their processes. Employing a lean methodology puts all those Six Sigma and process gurus to work by refining and breaking down bottlenecks into more manageable parts, eliminating waste and driving efficiency.

Leaner organizations with bigger data appetites

Describing a company as “lean” describes how they handle their processes. Employing a “lean” methodology puts all those Six Sigma and process gurus to work by refining and breaking down bottlenecks into more manageable parts, eliminating waste and driving efficiency. NLP is helping organizations rely on fewer people to come up with such answers.

“Every time people come to me and ask for new bodies, it turns out so much of that can be answered by asking the right questions of our data and getting that in front of the decision-makers,” says James Reinhart, CEO of thredUP, speaking to the Wall Street Journal². “I think frankly it’s eliminated four to five people who would [otherwise] pull data and crunch it.”

The knee-jerk reaction to that is: “See I told you, the machines win again!” But not so fast.

All those companies the Journal spoke to “had middle and even senior managers who operated as player-coaches, tasked with both doing things and directing others.” That’s the distinction. There’s still a need for delegation and talented people to decipher meaning from machines.

At least for the time being.

Six questions for NLP projects

A good way to start thinking about NLP use cases is to frame what you’re trying to accomplish. Data marketplace and aggregator Kaggle³ challenges data experts to answer six questions.

1. What problem are you addressing? Who will benefit?

There’s a good chance this is much broader than you think. Spend time mapping your stakeholders. Once you get a good sense of what motivates a particular group, you can start to think about data in a more tailored way.

2. What’s the current solution?

This is a chance to dig deeper and see the fruits of your research. Look for data sources or dormant processes that might be re-invigorated with a fresh set of eyes and improved access to key data. Think about how the use cases might change as data are manipulated and compiled in different ways with newer data sets. That’s the sort of process that spawns new applications and new business models.

Is NLP changing the way you operate?

6. Does it cut costs or increase revenue?

Referback to your business case. There are plenty of business improvementprojects, but the ones that get prioritized have numbers attached to them.

Content’s seat on the throne has been safe for a while. What might be their heir apparent is context. Alissa Lorentz described the scenario in WIRED⁴:

“Take the example of a company that has invested heavily in business intelligence (BI) software that organizes internal data. In an increasingly connected world, this company has not leveraged its data to its potential. Why not? Because the company’s internal data is isolated from the rest of the data universe including news, social media, blogs, and other relevant sources. What if you could join the dots between all these data sources and surface hidden connections?”

That’s exactly where we are in terms of NLP. Many challenges with just as many opportunities.

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  1. 1Tufekci, Zeypep. "The Machines Are Coming." nytimes.com. 18 April 2015.
  2. 2Mims, Christopher. "Data is the New Middle Manager." wsj.com. 19 April 2015.
  3. 3kaggle.com
  4. 4Lorentz, Alissa. "With Big Data, Context is a Big Issue." wired.com.