Always, the data is wrong

📰 Medium · Data Science

Learn to critically evaluate data-driven decisions and their limitations, and how to avoid potential pitfalls such as overreliance on historical data and biased data collection

intermediate Published 8 May 2026
Action Steps
  1. Identify potential biases in data collection and analysis
  2. Consider qualitative insights in addition to quantitative data
  3. Evaluate the quality and quantity of available data before making decisions
  4. Use techniques like A/B testing to collect data and inform decision-making
  5. Recognize the potential dangers of overreliance on historical data
Who Needs to Know This

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

Key Insight

💡 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

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

Key Takeaways

Learn to critically evaluate data-driven decisions and their limitations, and how to avoid potential pitfalls such as overreliance on historical data and biased data collection

Full Article

Title: Always, the data is wrong

URL Source: https://medium.com/@saikumar938104/always-the-data-is-wrong-c0441b741faa?source=rss------data_science-5

Published Time: 2026-05-08T23:01:59Z

Markdown Content:
# Always, the data is wrong. Data-driven decisions | by Saikumar | May, 2026 | Medium

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# Always, the data is wrong

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## Data-driven decisions

As you’ve been learning, data-driven decision-making means using facts to guide business strategy. The phrase “data-driven decisions” means exactly that: Data is used to arrive at a decision. This approach is limited by the quantity and quality of readily-available data. If the quality and quantity of the data is sufficient, this approach can far improve decision-making. But if the data is insufficient or biased, this can create problems for decision-makers. Potential dangers of relying entirely on data-driven decision-making can include overreliance on historical data, a tendency to ignore qualitative insights, and potential biases in data collection and analysis

## Example of a data-driven decision

A/B testing is a simple example of collecting data for data-driven decision-making. For example, a website that sells widgets has an idea for a new website layout they think will result in more people buy
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