Intuition is like a machine learning “box.” The more data you feed it, the better and more accurate it becomes. Think about the moments when your intuition leads you to grab a bag of potato chips. It feels right, doesn’t it? That’s because, whether consciously or subconsciously, you’ve repeatedly trained your “box” to believe it’s a good choice.

I think blending intuition with data requires a structured yet flexible approach. I would start by using data to frame the context—identify trends, outliers, and patterns. Then, leverage intuition to interpret nuances that numbers can’t fully capture, such as market sentiment or cultural dynamics. For example, when entering a new market, data might highlight demand trends, while intuition can guide decisions on local preferences. So, I would validate intuitive insights with A/B testing or scenario planning to minimize risk. Finally, I would maintain a feedback loop—track outcomes and refine your decision-making process by learning what worked and why. This synergy ensures decisions are both informed and adaptive.

I often start by analyzing relevant data to establish a factual foundation, which helps clarify the context. From there, I reflect on my instincts and past experiences, weighing them against the insights derived from analytics to identify potential pitfalls. Additionally, I seek input from colleagues with diverse backgrounds to enrich my perspective. This collaborative dialogue not only challenges my assumptions but also strengthens my gut feelings. By marrying instinct with empirical evidence, I can make more robust, confident decisions that balance creativity with analytical rigor.

What we need to have is the wisdom to acknowledge what are the pros and cons of both, intuition and pure data insights. In a way, intuition is ultimately a neurological low volume data driven method. Our brains excel in finding certain patterns between seemingly unrelated information, so when we face new scenarios, our hunches pick up pieces of factual information and compare them to similarly shaped aspects of past experiences, even if the overall scenarios are completely unrelated. With data, we can compare large amounts of information, which gives us more statistical accuracy. So what do we do? We blend both: we use our neurological prowess to correlate seemingly unrelated data to come up with specific indicators for probability.

1. Start with the data: Analyze trends, benchmarks, and KPIs. For instance, in a market expansion, use sales forecasts and competitor analysis to map the landscape. 2. Engage intuition: Use industry experience to interpret what the data doesn’t say—like anticipating a competitor's unexpected move. 3. Test assumptions: Combine both by running small experiments or pilot programs before committing fully. Blending both ensures decisions are not just informed but also forward-thinking.

When making big business decisions, I blend data and intuition. Data gives me the solid facts, trends, risks, and patterns that help guide the way. But intuition? That’s the secret sauce. It’s shaped by experience and helps me see things the numbers might not show, like spotting trends before they show up in the data. The trick is using data to stay grounded, but letting gut instincts drive creative thinking and innovation. It’s all about balancing both: data for clarity, intuition for those out-of-the-box decisions that can make all the difference.