The rise of NLP and conversational analytics
Updated: Nov 5, 2019
Voice technology is a three-way intersection of IoT (devices), AI (services) and UX (interactions). The result is a hand-free, frictionless way to use technology that feels more like science fiction than reality. In the mobile first era today, our voices are playing a greater role in how we access information and complete everyday tasks.
While conversational systems have changed how mobile consumers interact with the voice assistants, these systems are also well positioned to transform enterprise IT and the way people within organizations access and interact with data. According to Gartner, by 2021, “conversation first” will be adopted by the majority of enterprise IT organizations as the most important new platform paradigm, superseding “cloud first, mobile first.”
What is NLP and how is it transforming analytics:
Natural Language Processing (NLP) is a branch of AI that enables computers and humans to interact using natural language to find greater insights. Conversational analytics solutions typically comprise of a transcription engine that converts speech to data, an indexing layer that makes data searchable and a query and search user interface to allow the user to define requirements and carry out searches. It is often paired with a reporting application to present the output in a user-friendly manner.
Most enterprises today have an overabundance of data but lack the resources and technology to harness it. AI based conversational analytics can radically transform enterprises by:
Democratizing data: Data in an organisation is typically accessible to people who hone the skills required to analyse the data and generate insights from it. Conversational analytics can break this barrier by enabling everyday business users to interact with the data in an accessible system using natural language. This is increasingly essential as most firms have a shortage of skilled human resources required for old paradigm.
Standardising data reporting: The AI conversational platform helps ensure consistency and accuracy of data reporting and insights at an enterprise level. This takes care of the misrepresentation that can otherwise occur as various people with different technical capabilities align data to fit their agendas. Standardised data in a single system can easily enable C-suite executives to generate reports without being constrained by analyst capacity.
Gartner sees a significant future for NLP in analytics. It predicts that 50% of analytical queries will be generated via search, voice or NLP (or automatically generated) by 2020. A further foresee is that NLP and conversational analytics will drive analytics and business intelligence adoption from 35% of employees up to over 50% by 2021. According to the research, NLP will enable whole new classes of users, such as front office workers, to take advantages of analytics tools.
The use cases are so vast that the NLP market is anticipated to be worth $13.4bn by 2020.