- Data science (Python , R ) is incomplete without Big Data (Hadoop , Spark) software
- SAS continues to be the most profitable stack in data science because of much better customer support ( than say the customer support available for configuring Big Data Analytics for other stacks)
- Cloud Computing is increasingly an option for companies that are scaling up but it is not an option for sensitive data (Telecom, Banking) and there is enough juice in Moore’s law and server systems
- Data scientists (stats + coding in R/Py/SAS +business) and Data engineers (Linux+ Hadoop +Spark) are increasingly expected to have cross domain skills from each other
- Enterprises are at a massive inflection point for digital transformation (apps, websites to get data), cloud to process data, Hadoop/Spark/Kafka to store data, and Py/ R/ SAS to analyze data in a parallel processing environment
- BI and Data Visualization will continue to be relevant just because of huge data and limited human cognition. So will be traditional statisticians for designing test and control experiments
- Data science will move from tools to insights requiring much
shorter cycle times from data ingestion to data analysis to business action
These are my personal views only
DECISION STATS 
Great article, would you consider posting on https://imadata.ninja/blog/