Most Innovative Countries - Perception vs. Reality

Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting, collecting, structuring and analyzing data present serious risks.
Code examples that show to integrate Apache Kafka 0.8+ with Apache Storm 0.9+, while using Apache Avro as the data serialization format.
The easiest person in the world to fool is yourself. Data scientists sometimes fool themselves - in matters trivial and important. Thus, I strongly suggest that we acknowledge real or subconscious biases in ourselves, the data, the analysis and group think. It is prudent for data science teams to have both internal and external checks and balances to expose potential biases and better understand objective reality.
The Data Science Stack Exchange is a question and answer site for data science professionals.
The goal of Data Analytics (big and small) is to get actionable insights resulting in smarter decisions and better business outcomes. How you architect business technologies and design data analytics processes to get valuable, actionable insights varies.
According to recent survey by Burtch Works.
"Life imitates art far more than art imitates life." - Oscar Wilde