Always, the data is wrong

1. 1Identify potential biases in data collection and analysis
2. 2Consider qualitative insights in addition to quantitative data
3. 3Evaluate the quality and quantity of available data before making decisions
4. 4Use techniques like A/B testing to collect data and inform decision-making
5. 5Recognize the potential dangers of overreliance on historical data

Data scientists, analysts, and product managers can benefit from understanding the limitations of data-driven decision-making to make more informed decisions

💡 Data-driven decision-making is limited by the quality and quantity of available data, and can be susceptible to biases and overreliance on historical data

💡 Data-driven decisions are only as good as the data itself. Be aware of limitations and potential biases to make informed decisions