Most people agree that data is the lifeblood of most companies in the current environment. More and more data is available, but this leads to a need for a great deal of work to make good use of it. Raw data needs to be collected, cleaned, analyzed and insights generated that are useful in conducting business.
To do much of this work requires data scientists, but those professionals are notably scarce. Also, their business instincts may be low. Others can try to fill in, but with varying degrees of efficiency.
This is where augmented analytics comes in.
Augmented analytics makes use of machine learning and artificial intelligence to automate the cleaning, analysis and insight generation aspects of the process. Augmented analytics engines build a database of business-based algorithms to enable the generation of insights from the analysis that will be based on specific business elements and relationships. This is more than a data scientist would normally be able to do without significant additional training.
The analysis from augmented analytics is likely to be more relevant than any conventional analytics and, in addition, requires little or no human intervention.
It's a technological answer to a business need in a data-based world. Most large Business Intelligence providers are getting into augmented analytics.