Data-driven Decision-making vs. Intuition-based Decision-making

November 08, 2022

Data-driven Decision-making vs. Intuition-based Decision-making

As a data analytics team, we understand the importance of making informed decisions. Decision-making is often the difference between the success and failure of a business. Therefore, it is crucial to weigh the pros and cons of data-driven decision-making and intuition-based decision-making to choose the best approach for your business.

What is Data-driven Decision-making?

Data-driven decision-making is an approach that involves relying on data and analysis to make decisions. It requires data collection, cleaning, and analysis to identify trends and patterns. The goal is to make informed decisions driven by data rather than gut feelings or intuition.

What is Intuition-based Decision-making?

Intuition-based decision-making is an approach that involves relying on gut feelings and experience to make decisions. It requires personal beliefs and experience to make informed decisions rather than statistics and data.

Pros and Cons of Data-driven Decision-making


  • Objectives: Data-driven decision-making aligns with business objectives by quantifying results in measurable, meaningful, and specific terms.

  • Accurate: Data-driven decision-making provides accurate information to make informed decisions.

  • Consistent: A structured analysis can be repeated over time, ensuring a consistent decision-making process.

  • Unbiased: Data analysis is unbiased since it relies on factual data to make informed decisions.


  • Time-consuming: Collecting, cleaning, and analyzing data can be time-consuming.

  • Costly: Companies may require an investment in technology and training resources to efficiently analyze data.

  • Overwhelming: Data analysis can often provide too much data that can be challenging to sort through and analyze.

Pros and Cons of Intuition-based Decision-making


  • Quick: Intuition-based decision-making is often quicker since they do not require in-depth analysis.

  • Less expensive: Companies' voluntary experience does not require technology or training resources or investment.

  • Simple: Personal experience, beliefs, and perspective make decision-making easy.


  • Biased: Intuition-based decision-making reflects personal bias and experience, potentially resulting in erroneous decisions.

  • Inaccurate: Intuition can be wrong and lead to poorly informed decisions.

  • Non-consistent: With no structure, decision-making can vary depending on the maker or source.


Both data-driven decision-making and intuition-based decision-making involve advantages and disadvantages. Companies need to decide which approach suits their organization to gain a competitive advantage. Data collection and analysis is needed to guide informed decisions that minimize risks and maximize profits. However, intuition-based decisions may sometimes be better suited, especially in situations where historical data is unavailable.

Ultimately, the decision-making approach taken should be a blend of both data-driven and intuition-based decision-making, taking into account the business's objective, limitations, and available data.


  • Whetten, D. A., & Cameron, K. S. (2011). Developing management skills. Pearson Education.
  • Belsky, G. (2010). Don't follow your gut. Harvard Business Review, 88(6), 114-118.
  • Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.

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