How are you leveraging automation in your organization today? Which parts of the company are optimizing artificial intelligence to enable a better customer experience?
Hi, I’m Peter Nichol, Data Science CIO.
Today, we’ll talk about natural language processing (NLP) and how it can help accelerate technology adoption in your organization.
Neuro-linguistic programming (influencing value, beliefs, and behavior change) and natural language processing (helping humans better interact with machines or robots) are separate concepts, both of which are identified by the NLP acronym. Make sure you’re clear about which one you’re talking about.
Again, today we’ll be discussing natural language processing.
What is NLP?
NLP is the combination of linguistics and computer science.
Essentially, NLP helps computers better understand what we humans are trying to say. The application of NLP makes it feasible for us to digest, test, and interpret language; measure sentiment; and, ultimately, understand what exactly we mean behind what we’re saying. NLP incorporates a dynamic set of rules and structures that enable computers to accurately interpret what’s said or written (speech and text).
How is NLP being leveraged?
There are many ways in which we see NLP being adopted by organizations. First, we have chatbots and other customer-based tools that allow consumers to interface and interact with technology. In more simplistic terms, the application of NLP helps computers recognize patterns and interpret phraseology to understand what we, as humans, are attempting to do. There are many great examples of NLP in use today; here are a few of my favorites:
- Siri and Google assistance
- Spam filtering
- Grammar and error checking
Siri translates what you say into what you’re looking for by utilizing speech recognition (translation speed) and natural language processing (interpretation of a text). So, for example, NLP can understand your unique voice timbre and accent and then translate this into what you’re trying to say. Google Assistant is another excellent example of speech recognition and natural language process working in tandem.
Spam filtering uses NLP to interpret the type of outcome expressed and recognizing patterns of expression and through processes in the text. In this way, spam filtering uses NLP to determine if the message you received was sent from a friend or from a marketing company.
Grammar-error checking is an excellent use of NLP and super helpful. NLP references a massive database of words and phrases compiled from these use cases and compares what’s entered with that database to determine if a pattern has been used previously.
Text classification looks at your email and makes judgments based on text interpretation. If you’ve ever paid attention to your Gmail, you might have noticed your email is categorized in several ways. For example, you’ll have your primary email in one folder, and you’ll have spam and promotional emails in another. Unfortunately, you didn’t make the delineations; an NLP agent did.
NLP is behind all that type of stuff. It’s artificial intelligence. Essentially, it’s looking at what you say, what text is being written, and interpreting what you mean. As NLP adoption grows and is brought into mainstream business software, we realize there’s much potential to leverage NLP in our environments. The potential of NLP is powerful, especially when we begin to focus on automation and, ultimately, data or technology adoption.
NLP’s role in data democratization
Focusing on data democratization is an excellent example of how NLP can make its mark. Many CIOs and leaders are building data-driven cultures and striving to help raise the data awareness of employees about how to leverage and optimize data, provide insights, and determine and interpret analytics from more extensive data sources. But that’s not always possible.
A lot of the data we use—whether social media or other types of generic input, even voice—is unstructured. It doesn’t fit in well with traditional databases comprising columns and rows. This data is unstructured and, as a result, we need different ways to interpret it.
Historically, we needed particular individuals that could interpret and understand how that unstructured data could be aggregated, cleansed, and, finally, provided to consumers. Today, with data democratization, we’re trying to access tools and technologies that make interpretation fast and seamless. In addition, data democratization attempts to get everyone in the organization comfortable with working with data to make data-informed decisions.
Why does data democratization matter when building a data-driven culture? First, a significant part of building a data-driven culture is offering greater access to the data. This means individuals who otherwise might not have access to that data set can now execute queries, gather correlations, and generate new insights.
The first step toward building that data-driven culture is ensuring that you’ve captured intelligent and valuable data. Next, consider adding additional automation into your ecosystem and explore new ways to work with your business applications.
NLP has the potential to accelerate your most critical initiatives. Take time today to discover which of your approved initiatives could benefit from NLP.
If you found this article helpful, that’s great! Also, check out my books, Think Lead Disrupt and Leading with Value. They were published in early 2021 and are available on Amazon and at http://www.datsciencecio.com/shop for author-signed copies!
Hi, I’m Peter Nichol, Data Science CIO. Have a great day!