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Augmented Intelligence for Data Insights | SDG Group

Written by SDG Group | Dec 31, 2018 11:15:00 PM

There is more data to analyze in today's business environment, and insights need to be delivered faster, but finding relevant insights among vast amounts of complex data can be difficult even for data experts. We need new means of consuming data. This is where Augmented Intelligence can help.

Augmented Intelligence has disruptive potential. Business leaders need to examine the impact it will have and include it in their business models to achieve the following benefits for their organization:

Accelerated Adoption of Analytics
Augmented Intelligence assets like Virtual Assistants and Chatbots are becoming more common and user-friendly. They are making it easier for low-skill users to interact with data. This empowers all users in the organization, regardless of skill level, to find the analytics they need to make data-driven business decisions.

 

An Augmented Approach to Data Exploration & Discovery
It can be challenging to find what you are looking for without knowing specific search terms or tag hierarchies with a traditional keyword search. Augmented Intelligence takes users from a manual approach to analytics to an augmented approach because capabilities like Natural Language Processing, Natural Language Query, and Conversational Analytics give users the ability to search through data using their work context.

 

Access to Unstructured Data
Data lives outside of structured rows and columns. It sits in documents, emails, internal chat, Sharepoint, and in many more places. When Augmented Intelligence analyzes natural language, context, communication patterns, and phrases to extract the meaning of data, it can successfully make connections and discover meaningful insights across all data sources.



Reduced Cost and Time
There are a small amount of highly skilled data scientists compared to the vast amount of complex data that most organizations have. Augmented Intelligence is helping to make data experts out of low-skill users, reducing the need for expert data scientists and increasing the amount of citizen data scientist roles, allowing more employees to make data-driven decisions.

 

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