Is Augmented Analytics there yet?

We recently wrote an article about what is augmented analytics and the potential it holds in changing the analytics game. Augmented Analytics allows for deep insights from data using AI/ML with little to no supervision. They are more conversational and interactive in nature allowing for democratisation of analytics and access/usability for all.

But the question remains is that whether augmented analytics has developed enough to deliver on all these promises. Is it there yet?

The short answer: Not Yet!

As we move towards a world where increasingly AI/ML technologies are driving business and digital transformation, feasibility remains a factor. We can broadly separate augmented analytics into three stages/areas:

• Data preparation & Discovery: Most of the existing augmented analytics technologies are currently at this stage. A few key players include IBM Watson analytics and native NLP/Augmentation technologies of BI tools such as Tableau Insights & Qlik Sense. Currently, these technologies serve as a complement to existing data analysts & scientists, aiding them in various areas. However, Augmented analytics is still not mature enough to substitute them.
• Signal Detection: At the next stage of augmented analytics, the algorithms have the capability to detect true & relevant signals in a company’s data with high reliability. However, they would be unable to connect or link these signals with business situations or actionable insights without supervision.
• Actionable Insight Generation: This is the last stage of Augmented Analytics at which point, the analytics engine would be powerful enough to generate actionable insights and directly interface with company executives requiring little to no supervision. It would also seek to replace data analysts considering executives would no longer require inputs from them to obtain business insights.

Significant improvements to your analytical capabilities

As we approach the third stage of Augmented Analytics, some of the significant improvements that augmented analytics can contribute to your analytical capabilities are:

• Automatically clean, compile and prepare different data points and variables for rapid analytics and reporting.
• The ability to identify key trends and changes across critical metrics. Further, it can also be drilled down to identify potential improvement opportunities for your data.
• Dynamically track actions that have been taken to improve metrics and identify their effectivity.