How The Covid-19 Pandemic Accelerated Enterprise Investment in NLP

2020 has been a year like no other. The COVID-19 pandemic has affected life as we know it both personally and professionally in more ways than we can count. As such, an overall downturn in IT spending is no surprise. While technology is a vital part of day-to-day business, most enterprise organizations are focused on mission-critical tools and processes, rather than big-picture innovation. Despite this, there is one seemingly pandemic-resistant technology that’s trending upward – and for good reason.

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Natural language processing (NLP) has been on the forefront of transformative technology for the last several years, but perhaps even more so in the wake of the coronavirus. With the power to improve virtual patient care, predict health outcomes, and track how disease spreads, NLP is having a shining moment in the healthcare industry. But beyond that, the technology can even help with onboarding remote workers or better responding to customer service queries, making the transition to working from home or in a field that’s already operating digitally that much easier.

While it’s no surprise that NLP has proven to be quite valuable during these times, it is surprising to see the level of investment in the technology, given declining overall IT spending this year. According to new research commissioned by Gradient Flow, spending on NLP is increasing consistently, and in many cases, significantly. In fact, 53% of respondents indicated their NLP budget was at least 10% higher compared to 2019, with 31% stating their budget was at least 30% higher than the previous year. The same trend applies to large companies (those with more than 5,000 employees), in which 61% of respondents cited NLP budget increases in 2020.

To fully grasp why NLP budgets are increasing, it’s important to understand how practitioners are using the technology and what for. The same global research report, which surveyed 600 respondents from more than 50 countries, found that data from files and databases top the list of data sources used to provide for NLP projects. 61% of technical leaders surveyed stated that they used files – pdf, txt, docx, etc. – for their NLP systems. More than a third (36%) of this group also indicated that their organization used a text annotation tool for labeling training data for NLP.

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As for the ‘what for,’ the most popular applications for NLP are Document Classification, Named Entity Recognition (NER), Sentiment Analysis, and Knowledge Graphs. Document Classification and NER are by far the most popular use cases among respondents who worked in organizations further along the NLP adoption curve. Respondents from healthcare cited de-identification (38%) as another common NLP use case – which prior to being automated by NLP, had been a manual and labor-intensive process. This is especially important due to privacy regulations that require healthcare users to strip medical records of any protected health information (PHI).

Another factor to keep in mind when considering NLP investments are the tools organizations are using to deploy their NLP projects. When choosing the right NLP solution for your organization, it makes sense to explore a range of offerings from both open source libraries and cloud solutions. While popular cloud-based solutions are readily available – and used by 77% of survey respondents – they are generally perceived as a low-accuracy, high-cost option. While it’s easy to get started, models aren’t trainable, are difficult to scale, and it’s likely that users will pay thousands of dollars before even going live. This could be another reason budgets are on the rise.

Open source libraries, such as Spark NLP, are both trainable and come in at a lower price point. While it may not be as easy to get started as its cloud counterparts, it’s imperative to also consider the industry you’re in when choosing the best option for your business needs. Take highly-regulated industries, such as healthcare and financial services – both rely heavily on their own jargon and terminologies, and may not be able to legally share documents with a third-party service. That said, libraries come with challenges of their own – language support, scalability, and integration issues are all areas for improvement – but tools are evolving and refining constantly.

The stage of adoption is another interesting factor when it comes to NLP investment. While 44% of survey respondents indicated they are using NLP​, meaning they have deployed NLP to production, 56% reported that they are in the exploratory phase, and have not yet deployed NLP to production. Because a majority of IT leaders are in the early phases of NLP, it’s safe to assume that budgets will continue to increase as they move closer to deployment. Knowing where businesses are on the adoption curve also helps us understand the tools they’re using, which absolutely factors into the overall investment. For example, companies that have more experience deploying NLP are not as readily using expensive cloud-based solutions, compared to companies earlier on in their NLP journey.

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While it’s impossible to say whether the global COVID-19 pandemic is the cause for such a significant uptick in NLP investments, it’s easy to see how the use cases in healthcare, HR, customer service, and beyond are making the case for how valuable this technology can be. It’s encouraging to see NLP thrive, as we adjust to the ‘new normal,’ and try to optimize business operations during these uncertain times. To learn more about NLP, hear from experts using the technology, and experience cutting-edge use cases, register for The NLP Summit, a free online event taking place from October 6-16.