What happens when advanced analytics and business-critical data come together? You get more relevant insights! Sounds simple, but the task of gleaning insights from enterprise data is only getting tougher. That’s because data is both qualitative and quantitative; it’s also structured and unstructured. It’s true that data scientists and analysts are heaving a sigh of relief, thanks to the arrival of a new paradigm in data and analytics  – augmented analytics.

Insight generation. Automated and accelerated.

What’s the use of presenting an awe-inspiring bar chart to a user who struggles to extract the required informationfrom it?

The traditional static reports and dashboards may no longer be able to provide the insights needed to take informed decisions. Augmented analytics uses machine learning and natural language processing to automate and simplify the process of insight generation. In other words, it’s like data interpretation on steroids! Truly a disruption, it’s expected to change the way data analysts create, interpret and share data with a diverse group of users. But the good news is that it’s the new way to add artificial intelligence to existing business intelligence tools. The result: work gets done faster and the results become more meaningful.

Deeper analysis of data

Augmented analytics detects latent signals, patterns and outliers traditional analytics tools may not be able to view clearly.
It also analyses different formats and combinations of data to arrive at more accurate results. The solution streamlines data collation, governance and assimilation, while offering a single view of the customer (SVC). The analysis will also be aimed at extracting information that makes business sense to stakeholders. Nobody wants metrics and graphics that camouflage the reality! 

Faster and smarter sharing

In this era of Siri and Alexa, the voice command is becoming commonplace. Augmented analytics leverages this trend, so even business decision makers can draw insights easily  – just by asking simple questions! A case in point – A sales manager wants to compare the server sales figures of two previous quarters. He has two options: Count on the data analyst who is busy running repetitive reports, or chat with a bot to elicit the desired answers in jiffy. The latter is what augmented analytics can do.

Simplified analysis

Stakeholders across departments stand to gain immensely by this disruptive technology. Augmented analytics tools help eliminate time-consuming and error-prone manual processes. The users get to focus more on strategic tasks rather than discovery and interpretation.  Business decision makers (read non-technical folks) can also draw insights easily without much assistance from data analysts. Instead of spending time on reports, they can focus on applying the insights to explore profitable business opportunities.

According to Gartner, the company that coined the term augmented analytics, more than 40% of data science tasks will be automated by 2020, thereby resulting in increased productivity and broader usage of data and analytics.  Stated simply, augmented analytics is not a fad. It is here to stay and transform the world of data and analytics.

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