Data sources may number in the hundreds and it takes a good deal of time for enterprise software development companies
to merge multiple sources and bring petabytes of siloed data together into a single view to leverage them for insights.
Beyond that, there is always a risk of human bias when it comes to the analytics process. The point is that traditional BI systems respond exactly to the queries business users make. Such systems surface only those insights that decision-makers are querying, leaving no room for hidden insights and unexpected results. The result: bad business decisions and missed market opportunities.
To top it all, traditional BI and analytics solutions lack self-service capabilities compelling business users to depend on IT specialists for data ingestion, analysis and insight generation. This heavily undermines effective and timely decision making.
Such tools aren’t capable of scaling to organize and analyze volumes of unstructured data
growing at breakneck speed. And it seems that businesses are working for analytics rather than the other way around.
So why waste precious time and resources if everything can be done automatically, flawlessly and at a much faster rate?
AI-assisted augmented analytics is here to change the game. Once and for all.
Here is how.