Augmented analytics automates data insight by utilizing machine learning and natural language processing (NLP) to automate data preparation and enable data sharing. This manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Tech Republic brought this interesting information to our attention in their article, “How to implement augmented analytics: 3 important caveats.”
Users can go beyond opinion and bias to get real insight and act on data quickly and accurately. However, when it comes to augmented analytics, users might not be data literate. Dialogue about big data and analytics have emphasized the importance of leveraging data for the past decade. That dialogue has lacked addressing the need to leverage the ability of people to understand data and apply this understanding to the business.
Augmented analytics promises shorter lead times to insight for the end business. This is possible because end users can now query data in a natural language and a system can then go to work with machine learning and self-developed algorithms to provide new insights.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
