Natural Language Analytics- Things to know
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language.
NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.
In the current scenario, organizations that empower their people to make data-driven decisions have a competitive advantage. One of the keys to making better decisions with data is to make the data accessible and the analytical tools easy to use for all.
The next generation of analytics is already upon us. We’re moving beyond visual analytics to natural language analytics driven by natural language processing (NLP).
It’s a powerful way to query data, generate visuals, and answer the questions that lead to strategic insights for your company.
According to Gartner, “By 2020, 50% of analytical queries either will be generated via search, natural language processing or voice, or will be automatically generated.”1 Furthermore, “By 2021, natural language processing and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.”
Let’s look at some the major thing that comes under Natural Language Analytics.
- NLP will help create a data-driven culture in your organization —
The path to a data-driven culture typically starts with a small team that has a vision for a better way. By making both the data and the analytics tools widely available, by helping other parts of the organization get answers to important questions, and by showing the organization that you do not have to be a data scientist or software engineer to interact with the data, the seeds of change are planted.
NLP is another step forward that lowers the bar for business users to interact with their data and raises the performance and results of the entire organization. It’s exciting to see this field developing and making an impact by empowering your organization.
2. Natural Language Processing (NLP) enables computers to understand human language — Recent advances in Machine Learning enable computers to do more with natural language. A recent Forbes article defines natural language processing as “a form of artificial intelligence that analyzes the human language.
It takes many forms, but at its core, the technology helps machines understand, and even communicate with, human speech.” Tableau released their new NLP feature called Ask Data in version 2019.1 and has gotten even better with Explain Data in subsequent releases this year.
3. NLP lets you explore your data at the speed of thought — As fast as you can type it (or say it), the visualization software will create queries and deliver the answer to your question.
It drives many of the forms of AI (Artificial Intelligence) that we are used to seeing like Google’s Alexa or Apple’s Siri. NLP is more than just understanding the words we speak. Human communication involves context, not just words. Some examples of this include the way you phrase a question, how frequently you use words (just think about what gets suggested as you type on your iPhone), or even your body language as you speak.
As it applies to analytics and data visualization, and NLP system is taking a simple question and builds up the implications of that question into queries. It then returns the results as a visualization, rather than you having to build it by constructing a series of queries or even dragging and dropping dimensions and measures onto a worksheet in tools like Tableau or PowerBI.
4. NLP empowers more of your people to interact with data- Is your sales leader curious about the sales trend over time? With NLP tools like Tableau’s Ask Data, she can ask the software “what are sales over time?” and will see a chart of the sales over the life of the data set.
The goal here is to continue to enable more and more people in an organization who are not data scientists or analysts to interact with their data in a natural, conversational way that leads to insights.
Better decisions are driven by better data and most importantly, business results are driven by those people who can make decisions in a timely manner on the front line.
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