Augmented analytics is perfect in terms of performing the assistive role to humans while enhancing humans’ interpretation capabilities and providing the deeper and more insightful look into data for searching its ‘business sense’ and defining its significance regardless of structural complexity.
Data analytics software with data analytics characteristics makes use of machine learning so that it would be possible to implement and provide on a large scale the ‘human-like’ interaction with the data.
In general, the analysis process starts with collecting data from public and authorized private sources like web or private databases. After data is available it needs to be prepared and organized for extracting the relevant insights. It is followed by transferring them to end-user alongside with sharing thoughts on action plan pertaining to results of analysis.
The potential of augmented analytics has not been fully discovered yet, though, at this stage, it is clear that its implementation in business can considerably increase revenue if users know how to incorporate the results of data analysis into the business context of the specific industry.
The main challenges for applying augmented analytics properly pertain to:
• making insights actionable
• connecting them to vocalized business issues
• encompassing the augmented data analytics in business priorities.
Apparently, this technology is not yet mature enough to be labeled as commercially viable and fully accessible, which means that demand for augmented analytics platforms overlaps the existing supply.
Apart from tech giants (Microsoft, IBM, DataRobot, SAP) that actively implement augmented data analytics, a lot of start-ups emerge on the market indicating the immense industry interest to developing cutting-edge augmented analytics platforms that would provide the unbiased and accurate actionable insights into data.