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  /  Analytics   /  How Can Augmented Analytics Benefit Business And Data Analytics?

How Can Augmented Analytics Benefit Business And Data Analytics?

Augmented Analytics acts as a medium that enables technologies like machine learning, AI and natural language processing (NLP) to enhance data analytics, data sharing to assist with data preparation, insight explanation, and business intelligence. This can help people in exploring and analyzing data in analytics and BI platforms. The term was just coined in July 2017 by analyst firm, Gartner in its 2017 Hype Cycle for Emerging Technologies report and claimed it would be the future of data analytics.

While data itself is useless and valueless unless any meaningful insights are mined from it, it is not easy to analyze it either. Sometimes, even after using data analytics tools, it still fails to explain the reasons and meaning behind those insights. Although hiring data scientists may help, but due to their high demands, it is not a viable option for small scale business organizations, and neither they can always help in business matters. Under augmented analytics, data is automatically taken from raw data sources, checked for patterns, and parsed impartially. Then the output is communicated in a report using natural language processing that humans can understand without the need for a professional to explain it from the machine’s point of view. Furthermore, by combining machine learning with human creativity, can allow augmented analytics to provide faster responses, improved productivity, along with targeted insights to help make better decisions.  And it enables accelerated, and automated, AI modeling.

When an employer wants insights on a given set of data, AI and machine learning simultaneously start the analysis of the structured and unstructured data section against the search keywords to display the most relevant results, including visual representations and data narratives. The user can then explore interpretations of that data that were previously unavailable to help them to make the best business decisions. This can boost data literacy within the organization with just a little but proper training. So now, employees can handle any data related work. However, it does not mean that data scientists will become history. Instead, thanks to the democratization of data, augmented analytics will make it available to a broader workforce with new opportunities. While data scientists spend most of their time gathering, preparing, and cleaning up data, augmented analytics can get it done at a faster rate, automatically and with fewer errors. They are thus freeing up the load on a data scientist to help them focus on strategic matters and special projects.

Therefore, future organizations may adopt augmented analytics for business projects. Introducing this technology will mean better transparency, increased trust, clear discussion on data analytics, and in-depth data analysis of myriad data sources for the higher return value. Besides, it inspires employees of an organization to become data-focused and be empowered by data as it becomes a part of their everyday activity and not just reserved for the data scientists.

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