Blog Topics: Blog, Brainspace Discovery

The Shift to Augmented Intelligence and How it Helps Your Organization

At this year’s Gartner BI and Analytics Summit, Brainspace founder and CEO, Dave Copps, spoke about the present technological shift to augmented intelligence. In the past, the focus for companies was to manage the abundance of their data and knowledge using technology which simply stored and organized it. To use this information, knowledge workers are required to search these data stores using tags and keywords.

While enterprise content management and search have advanced to some degree over the years, there remains one key issue with this methodology: when knowledge workers “don’t know what they don’t know”, it is immensely difficult for them leverage keyword search to find valuable information. This failure can cost the organization significant time and resources. According to Copps:

“We have reached a point where synthesis of machine learning and user experiences (UX) that simplify human curation has the potential to completely reshape analytics and knowledge discovery in the enterprise.”

In other words, modern analytics needs to undergo a shift from management to discovery as a means to create advantage and insight. This shift not only includes technology advancements but also a highly critical human curation component. In organizations, employees curate content through their own work, subscriptions and communication patterns. Augmented intelligence looks at the unique knowledge sharing and curation patterns of the organization to connect patterns and synthesize data. This has several benefits for the organization:

Any knowledge worker can be a data scientist

Augmented intelligence combines the power of machine learning with human curation and intuition, producing an outcome greater than the sum of its parts. Providing powerful, contextual text analysis with a great user experience empowers everyone in the organization to operate like a data scientist.

Contextual discovery vs. traditional search

With traditional keyword search, it can be impossible to find what you’re looking for without knowing specific search terms or tag hierarchies. Augmented intelligence enables contextual discovery so users can find what they’re looking for based on natural language queries and context of their work.

Analysis of unstructured data

Organizational information lives in more than just structured rows and columns — in fact, the majority is unstructured in documents, emails, internal chat, Sharepoint… the list goes on. With machine learning analyzing natural language, communication patterns, context, and phrases to extract the meaning of data, this technology can provide meaningful connections across an organization’s data stores.

Higher quality insights

Analyzing unstructured data with machine learning and predictive analytics means that business users can reach the information faster and with greater breadth in order to make thorough decisions. If this data is hidden behind a search box, it may never be found.

Lower time and allocated resources

Quantitatively, the speed with which users can extract key insights with machine learning can save your organization significant time and allocated costs. Brainspace Discovery has saved one consulting firm up to 98% in allocated labor costs alone.

At its core, augmenting worker intelligence allows your organization to extract valuable data hidden in all corners of the company and connect it in ways that make sense to human users. Being able to extract meaning from unstructured data and present it sensibly enables the organization to derive higher quality insights more quickly. Augmented intelligence is the perfect marriage of machine learning and human intuition, enabling any user to become a data scientist; R&D can use it to research patents, HR can use it for workforce analytics, or corporate investigators can use it to explore cases.

Brainspace Discovery is the industry’s most advanced, large-scale machine learning platform. Brainspace rapidly ingests millions of pages of unstructured text, dynamically learning without taxonomies or ontologies, and connects that information using human patterns like communication analysis, timelines, and clusters.

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