How NLP is turbocharging business intelligence

Nearly all search engines tokenize text, but there are further steps an engine can take to normalize the tokens. Natural language processing (NLP) and natural language understanding (NLU) are two often-confused technologies that make search more intelligent and ensure people can search and find what they want. Many of the traditional features of an NLP engine are better suited to simpler techniques that are faster, cheaper and map easily to business needs. Many of the features of an NLP engine, such as named entities, sentence grammar and speaker intention, are all provided by machine learning models, which are foundational to what we call “AI” today.

Collaboration in BI processes is important, according to Mesmerize’s Bernardo. It is essential to have the support of a specialist in a domain to refine workflow architectures and work together with the data team. This convenience plays a significant role in promoting an organization’s analytics culture. By applying NLP to BI tools, even non-technical personnel can independently analyze data rather than rely on IT specialists to generate complex reports. Textio is all about “augmented writing.” The company’s technology is centered around helping organizations write better job postings by using data to score writing on a 100-point scale.

• For AI to truly change how businesses operate, it has to deliver consistent results over time so that consistent business decisions can be made.
• When NLP enhancement originally came to BI systems, “it was kind of clunky,” Henschen said.
• NLP and NLU tasks like tokenization, normalization, tagging, typo tolerance, and others can help make sure that searchers don’t need to be search experts.
• NLU, on the other hand, aims to “understand” what a block of natural language is communicating.

Other NLP And NLU tasks

The technology is maturing quickly, but core business-driven decisions should rely on tried-and-true BI approaches until confidence is established with new approaches,” added Behzadi. Also this week, SalesForce announced OpenAI integrations that bring “enterprise ChatGPT” to SalesForce proprietary AI models for a range of tooling, including auto-summarizations that could impact BI workflows. Signs of a ChatGPT boost to NLP efforts appeared last month as Microsoft said Power BI development capabilities based on this model will be available through Azure OpenAI Service. The company followed up this week with generative AI capabilities for Power Virtual Agents. Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation.

Intent Detection

“NLP-driven analytical experiences have democratized how people analyze data and glean insights — without using a sophisticated analytics tool or crafting complex data queries,” added Setlur. Rasa has its sights set on revolutionizing the field of conversational AI. Not only does the young company provide open source conversational AI that anyone can use and contribute to, it also fosters a community to advance the field and organizes conferences that bring everyone together.

Mozilla Common Voice

Increasingly, “typos” can also result from poor speech-to-text understanding. This is because stemming attempts to compare related words and break down words into their smallest possible parts, even if that part is not a word itself. Stemming breaks a word down to its “stem,” or other variants of the word it is based on. Of course, we know that sometimes capitalization does change the meaning of a word or phrase.

The AI insights you need to lead

As we go through different normalization steps, we’ll see that there is no approach that everyone follows. Each normalization step generally increases recall and decreases precision. It takes messy data (and natural language can be very messy) and processes it into something that computers can work with.

Named entity recognition is valuable in search because it can be used in conjunction with facet values to provide better search results. While NLP is all about processing text and natural language, NLU is about understanding that text. For example, to require a user to type a query in exactly the same format as the matching words in a record is unfair and unproductive. These kinds of processing can include tasks like normalization, spelling correction, or stemming, each of which we’ll look at in more detail. Operating a business is about understanding your market, customers and employees and giving clients what they need without bankrupting yourself while meeting those needs.

“Employing NLP enables people who may not have the advanced skillset for sophisticated analysis to ask questions about their data in simple language. As people can get answers to questions from complex databases and large datasets quickly, organizations can make critical data-driven decisions more efficiently,” Setlur explained. When NLP enhancement originally came to BI systems, “it was kind of clunky,” Henschen said. Enterprise developers had to work to curate the language that was common within the domain where the users of the data lived. That included identifying synonyms people might use to describe the same thing.

• The simplest way to handle these typos, misspellings, and variations, is to avoid trying to correct them at all.
• Another is that while NLP systems require vast amounts of data to function, collecting and using this data can raise serious privacy concerns.
• “Stakeholders and executives can query the data through questions, and their BI platform could respond by providing relevant graphs.
• Separating on spaces alone means that the phrase “Let’s break up this phrase!

We use text normalization to do away with this requirement so that the text will be in a standard format no matter where it’s coming from.

In reality, NLP and AI are not two different technologies; NLP is actually a platform to deploy a series of AI capabilities. “Computer systems would need to be able to parse and interpret the many ways people ask questions about data, including domain-specific terms (e.g., the medical industry). Developing robust and reliable tools that can support BI organizations to analyze and glean insights while maintaining security continue to be issues that the field needs to improve upon further,” added Tableau’s Setlur. Organizations can automate many workflow tasks through natural language processing to get the relevant data.