We can take you from inception to insights.
Data Analytics involves many phases, from gathering data to streamlining data collection to scaling platforms.
Then there are the aspects of cleaning data and analyzing data to gather insights. There is no one size fits all - supervised and unsupervised machine learning methods to study the data, build models and then use these to provide insights.
Dashboards and visualizations provide descriptive insights or expanded to be actionable and provide predictive insights.
ML models have to be deployed and integrated into production to scale and that's what the data engineering aspects of machine learning deals with -productionalizing of models and seamless integration with the platform.
This is further helped by devops and continuous integration.