How NLP is turbocharging business intelligence

Setlur believes this has changed how organizations think of growing their businesses and the types of expertise they hire. As with other technology areas, the field stands to change even more dramatically as large language models like OpenAI’s ChatGPT come online. Integrated NLP-enabled chatbots have become part of many BI-oriented systems along with search and query features. Long-established and upstart BI players alike are in a highly competitive environment, as data science and MLOps technologies pursue similar goals.

How CodeRabbit brings AI to code reviews

Before storing any data, organizations need to consider the user benefits, why the data need to be stored, and act according to regulations and best practices to protect user data,” said Bernardo. “With NLP-enabled chatbots and question-answering interfaces, visual analytical workflows are no longer tied to the traditional dashboard experience. People can ask questions in Slack to quickly get data insights,” Setlur told VentureBeat. “Natural language querying and natural language explanation are pretty much routinely found in most every BI analytics product today,” Doug Henschen, analyst at Constellation Research, told VentureBeat. Systems such as Domo, Google Looker, Microsoft Power BI, Qlik Insight Advisor Chat, Tableau, SiSense Fusion and ThoughtSpot Everywhere have seen NLP updates. These have made data consumption considerably more convenient as business users retrieve data through natural language queries.

Mapping the context, specificity, and personalization of NLP to the industry it serves is challenging. We’re unlikely to encounter sarcasm, for example, in a legal contract. “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).

AWS imposes caps on Kiro usage, introduces waitlist for new users

Makover says that we might see BI integrations with generative AI in the near future. 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. On a daily basis, the insurance industry faces a very high percentage of claims that are likely to be fraudulent. In the U.S., insurance fraud costs $309 billion a year; this equates to almost $1,000 for every single U.S. citizen.

If such an evolution is not taken, chatbots will continue to be costlier to develop and maintain than traditional applications. Banks operating in different countries must comply with multiple data privacy regulations (despite being from one bank, the data is subject to the regulatory requirements of the region or country in which it resides). In this situation (which is typical for many banks), detecting fraud by processing documentary data poses a significant challenge. To improve the accuracy of machine translations, computers can be programmed to compare between machine and human translations. “This can be done automatically, so if the machine translation overlaps a lot with the human translation, then it’s good,” he said. Alibaba aren’t the only ones honing their NLP capabilities, with other technology giants such as Baidu, Microsoft, Google and Ping An doing the same.

Search form

“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. At Alibaba’s natural language processing (NLP) research facilities spread across the US, China and Singapore, some of the world’s top researchers are solving the most challenging problems in the field of artificial intelligence (AI). As a final step, the researchers also ran the data set through an algorithm to remove as many “artifacts” as possible—unintentional data patterns or correlations that could help a language model arrive at the right answers for the wrong reasons. This reduced the chance that a model could learn to game the data set.

• Machine learning makes it possible to capture that collective knowledge and build on it.
• In the past 18 months, Alibaba has also started building domain-specific NLP capabilities to handle the lexicon used in different fields.
• “With NLP-enabled chatbots and question-answering interfaces, visual analytical workflows are no longer tied to the traditional dashboard experience.
• An increasing number of global companies are now adopting NLP-driven business intelligence chatbots that can understand natural language and perform complex tasks related to BI.

In short, these are two real examples of NLP’s applications in different sectors that expand the security focus of companies. Undoubtedly, neither of these applications will make headlines despite being an incredible and innovative breakthrough in the fight against fraud. Solutions based on blockchain and multiparty secure computing (MPC) allow different decentralized data sources to work securely within a joint project. By adopting solutions like this, banks can have access to information such as know-your-customer (KYC) and customer due diligence (CDD) processes. “In this case, we can improve the translation quality for hundreds and millions of products by only utilising a small set of human translations,” he added.

Natural language processing (NLP), business intelligence (BI) and analytics have evolved in parallel in recent years. But there is much work ahead to adapt NLP for use in this highly competitive area. The company also taps fresh talent from top universities such as Tsinghua and Peking in China, and Princeton and Duke in the US. In an interview with Computer Weekly, Seattle-based Si said although the benchmark is an important test of Alibaba’s NLP capabilities, just one researcher has been tasked to work on it on a part-time basis. “Our major task is still to use our technical development to support Alibaba’s business,” he said. Years ago, a person’s word or handshake was all that was needed between two parties to do business.