Why traditional machine learning fails?

Improving data quality and use In traditional ML, data is frequently fragmented and of inconsistent quality. Connecting divergent data sets can also be problematic. By assigning common indicators across data harvesting activities usually generates the best outcomes from linked data sets. Designing common indicators to be used in all data-collection efforts in a country would help get the best from data sets once they're linked. Delivering more thorough insights Being armed with a full understanding of all the variables that can be driving behaviors (policies, laws, influencers, personal beliefs, inherent bias, and unique individual motivators) can result in more accurate and relevant outcomes.