“Ok Google, show me the nearest supermarket.”

“Okay, here are some options”. Google proceeded to list several options within a two-kilometres radius.

The ability of machines such as Google Home, Amazon’s Alexa and Apple’s Siri to process human language is now possible thanks to Natural Language Processing (NLP). Harnessing NLP has allowed businesses to simplify tasks and do things faster, while providing a better user experience.

At a recent DBS Asia X (DAX) U event attended by more than 120 people, Andrew Hill, Senior Sales Consultant of Tableau elaborated how NLP truly works. Like human interactions where our brains receive social cues that encourage certain behaviour and restrict others, computers also require cues and feedback to decipher what is right and wrong.

The technology has been through cycles of transformation since its early days. Fun fact: Did you know that the first dictation machine was constructed in 1879 by Thomas Edison but the first voice recognition technology only came about in the 1950s? It was 1952 when the machine created by Bell Labs could understand the digits 0 to 9 with a 90% accuracy when spoken by its inventor.

We caught up with Andrew and the rest of the Tableau team after the event to understand more about NLP and the relevant trends we should look out for.

There is a lot of buzz around NLP. What is NLP really about?

Simply put, NLP is a field of computer science and computational linguistics that have to do with the interactions between human (natural) language and the computer. It’s about helping computers to leverage semantic structures of information — context within the data — to derive meaning.

Natural language has already permeated our daily lives. The most common examples of natural language are technologies like Alexa, Siri, and Google Assistant that recognise patterns in speech to infer meaning and serve an appropriate response. NLP is also used in Google’s Gmail to automatically detect and understand contents of your email messages and detect tasks such as meeting invitations, package shipment notifications and reminders.

Another example of NLP in action is web search engines. When you key a phrase into a search engine, it shows suggestions based on other similar search behaviours. This function works the same way on social media sites where searching “people I know who live in Singapore” will show a list of friends in the region.

What are the benefits to using NLP?

Today, business intelligence (BI) vendors offer a natural language interface to visualisations so users can interact with their data naturally. In the context of the BI market, natural language is often categorised under the term “smart analytics”. This refers to the application of machine learning and artificial intelligence.

NLP opens data analysis to every level of users because using the technology doesn’t require deep knowledge of a BI tool to find insights.

Everybody has a thirst for getting insights of their data. And natural language is one important modality for bridging that gap. It’s being able to ask a question about your data without having to think about the mechanics of doing so.

People don’t typically start from a blank state when asking questions about data. We often rely on context to spark our curiosity. Similarly, the NLP system leverages context within the conversation to understand the user’s intent behind a query and further the dialogue, creating a more natural conversational experience. For example, you could request for a BI tool to “Find large earthquakes near Indonesia” and then ask a follow-up question like, “How about near Malaysia?” without mentioning earthquakes for a second time.

As natural language matures across the BI industry, this breaks down barriers to analytics adoption across organisations and transforms how people interact with data.

What are some trends in NLP? Are there specific businesses or industries that have taken a keen interest in it?

The integration of NLP into unchartered territories will grow. The likes of Amazon Alexa, Google Home, and Microsoft Cortana have set people’s expectations that their software can understand and act on what they say. For example, by a simple command like “Alexa, play Yellow Submarine”, the Beatles’ hit starts to play. This same concept is also being applied to data, making it easier for everyone to ask questions and analyse their data.

Gartner predicts by 2020 that 50% of analytical queries will be generated via search, NLP or voice[1]. This means it will suddenly be much easier for the CEO on the go to quickly ask his mobile device to tell him: “Total sales by customers who purchased staples in New York,” then filter to “orders in the last 30 days,” and then group by “project owner’s department.” NLP will empower people to ask more nuanced questions of data and receive relevant answers that will result in better everyday insights and decisions.

In the same way, developers and engineers will make great strides in learning and understanding how people use NLP. They will examine how people ask questions, ranging from instant gratification (“which product had the most sales?”) to exploration (“I don’t know what my data can tell me — how’s my department doing?”). Consequently, the opportunity will arise not from placing NLP in every situation but making it available in the right workflow, so it becomes second nature to the person using it.

Chatbots and robots currently use NLP and machine learning — do you think machines will ever fully understand what we say? Will computers ever get smarter than human?

Machine learning’s potential to aid an analyst is undeniable but it’s critical to recognise that it should be embraced when there are clearly defined outcomes. Machine learning is not great when your data is subjective. For example, when conducting a survey to customers about product satisfaction, machine learning cannot always pick up on qualitative words.

Additionally, the analyst needs to understand success metrics for the data to make sense of it in a way that is actionable. In other words, inputs into a machine don’t make the outputs meaningful. Only a human can understand if the right amount of context has been applied. This means that machine learning cannot be done in isolation.

While there are concerns being replaced, machine learning will supercharge analysts and make them more efficient, more precise, and more impactful to the business. Instead of fearing new technology, we should embrace the opportunities it presents.

Do you see NLP getting more prevalent in everything we do and use in future? How so?

Developments in related disciplines of machine learning and AI are propelling the use of NLP forward. Industry leaders like Gartner have claimed conversational analytics as an emerging paradigm. This shift enables business professionals to explore their data, generate queries, receive and act on insights using natural language — whether it be voice or text, through mobile devices, or personal assistants.

Let’s act on some instincts for a change. If you had a create a TV show about NLP, what would you name it and why?

“Talk Data to Me” — because data is becoming the modern language of every person and organisation.

DAX U is a series consisting of curated curriculum about innovation in different fields, offering DBS staff and the innovation community additional learning resources. Topics ranging from technology in data virtualisation to entrepreneurship tips and tricks are shared via brown bag sessions and mini-workshops conducted at DBS Asia X.

[1] https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo