Published on December 16th, 2019 | by Emergent Enterprise0
The Growth of Cognitive Search in the Enterprise, and Why it Matters
There are countless scenarios of employees not being able to find company information when they most need it. As Kyle Wiggers reports at VentureBeat, artificial intelligence is coming to the rescue in these situations by understanding user behavior and the large volumes of data that exist within the silos of the enterprise. By pushing high potential content before the user at the time of need and in the workflow, problems get solved quicker and questions get answered faster (or that’s the the hope). Companies that implement effective cognitive search will be a step ahead of the competition.
Image Credit: bettervector / Shutterstock
Enterprises typically have countless data buckets to wrangle (upwards of 93% say they’re storing data in more than one place), and some of those buckets invariably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends, while a separate Veritas report found that 52% of all information stored by organizations is of unknown value. The opportunity cost of this unused data is substantial — the Veritas report pegs it as a cumulative $3.3 trillion by the year 2020, if the current trend holds.
That’s perhaps why this year saw renewed interest from the corporate sector in AI-powered software-as-a-service (SaaS) products that ingest, understand, organize, and query digital content from multiple sources. “Keyword-based enterprise search engines of the past are obsolete. Cognitive search is the new generation of enterprise search that uses [AI] to return results that are more relevant to the user or embedded in an application issuing the search query,” wrote Forrester analysts Mike Gualtieri, Srividya Sridharan, and Emily Miller in a comprehensive survey of the industry published in 2017.
Microsoft kicked the segment into overdrive in early November by launching Project Cortex, a service that taps AI to automatically classify and analyze an organization’s documents, conversations, meetings, and videos. It’s in some ways a direct response to Google Cloud Search, which launched July 2018. Like Project Cortex, Cloud Search pulls in data from a range of third-party products and services running both on-premises and in the cloud, relying on machine learning to deliver query suggestions and surface the most relevant results. Not to be outdone, Amazon last week unveiled AWS Kendra, which taps a library of connectors to unify data sources, including file systems, websites, Box, DropBox, Salesforce, SharePoint, relational databases, and more.
Of course, Google, Amazon, and Microsoft aren’t the only cognitive search vendors on the block. There’s IBM, which offers a data indexing and query processing service dubbed Watson Explorer, and Coveo, which uses AI to learn users’ behaviors and return results that are most relevant to them. Hewlett-Packard Enterprise’s IDOL platform supports analytics for speech, images, and video, in addition to unstructured text. And both Lucidworks and Squirro leverage open source projects like Apache Solr and Elasticsearch to make sense of disparate data sets.
The cognitive search market is exploding — it’s anticipated to be worth $15.28 billion by 2023, up from $2.59 billion in 2018, according to Markets and Markets — and it coincides with an upswing in the adoption of AI and machine learning in the enterprise. But it’s perhaps more directly attributable to the wealth of telemetry afforded by modern corporate digital environments.