How do you balance intuition and evidence when forecasting?

Balancing intuition and evidence in forecasting requires a structured process that ensures consistency, transparency, and rigor. Begin by clearly defining the forecasting objectives, scope, and timeframe. Establish a systematic approach for gathering, analyzing, and interpreting data, incorporating both qualitative and quantitative inputs. This structured process should outline specific steps for integrating intuition and evidence, such as data collection, hypothesis formulation, model selection, and validation. By following a structured process, you can mitigate biases, ensure accountability, and improve the overall reliability of your forecasts.

Here are some key points to consider when using a structured process: Define the problems3 Gather data Analyse trends Generate scenarios Estimate probabilities Communicate results Test and learn. By following a structured process, you can minimize the influence of biases, ensure a comprehensive analysis of evidence, and enhance the transparency and reliability of your forecasts. It also helps you create a systematic approach that can be replicated and refined over time, leading to more accurate and confident predictions.

One approach to balancing intuition and evidence when forecasting involves deploying a structured process. Start with defining the problem at hand. Next, gather pertinent data, analyze prevailing trends, generate potential scenarios, estimate related probabilities, and finally, articulate your findings. 👩💼📊 This method not only navigates you clear of cognitive biases such as overconfidence, confirmation, and anchoring that could potentially skew your judgment and intuition, but it also enables a thorough documentation of your assumptions, techniques, and data sources, ensuring transparency and allowing room for testing and reevaluation. 💡📑

AI can analyze a huge amount of historical datasets, both operational and financial, to identify patterns and trends. This is likely to help in informing the budget or forecasts. Combining the power of AI with judgement of seasoned business leaders is a win-win.

Integrate qualitative insights and quantitative analysis to leverage the strengths of both approaches. Qualitative methods, such as expert judgment, focus groups, and scenario planning, provide valuable context, tacit knowledge, and insights into complex or uncertain environments. Quantitative methods, such as statistical models, time series analysis, and machine learning algorithms, offer empirical rigor, objectivity, and precision. Combining these methods allows you to capture a comprehensive view of the factors influencing future outcomes, enhancing the robustness and relevance of your forecasts.

Here are some key considerations when integrating qualitative and quantitative methods: Qualitative methods: Expert opinions Surveys and interviews Quantitative methods Statistical models Simulations and scenario analysis. Integration and validation: Triangulation. Sensitivity analysis Iterative refinement: Feedback loops Learn from discrepancies By combining qualitative and quantitative methods, you can leverage the strengths of each approach and address their respective limitations. This integration allows you to capture a more comprehensive view of the forecasted domain, validate and enrich your intuition, and enhance the reliability and robustness of your predictions.

Actively seek feedback and incorporate diverse perspectives from a broad range of stakeholders, including domain experts, data analysts, and decision-makers. Diversity in viewpoints helps identify potential biases, challenge assumptions, and uncover blind spots in your forecasting process. Encourage open dialogue and collaboration to integrate different forms of expertise and intuition, ensuring that forecasts are informed by a wide range of experiences and knowledge. This collaborative approach fosters a more balanced and nuanced understanding of the factors driving future trends.

View other perspectives with different set of circumstances could be beneficial in terms of the final solution which could be a smarter way when an issue needs to be resolved.

At the end of the day, the data is interpreted and executed upon by humans with intution. So it's more important to focus on visualization of the data in an comprehensible way, than to worry about whether the data wins or human intuition wins. If intuition is not agreeing with the data, then improve visualization of the data to help human comprehension. Better visualization will influence and possibly override intuition if it's wrong.

Continuously update and revise your forecasts based on new data, emerging trends, and feedback from stakeholders. Regularly reassess the assumptions, methods, and inputs used in your forecasts to ensure they remain relevant and accurate. Incorporate real-time data and feedback loops to refine your forecasts dynamically, allowing for adjustments in response to changing conditions or unexpected events. This iterative process enhances the adaptability and responsiveness of your forecasts, aligning them more closely with actual outcomes.

As you update your forecast, identify what incorrect assumptions or data you used earlier. If it is the first time you have seen this situation/unlikely to be seen again, no need for change. If there are multiple data points starting to line up, you can adjust to increase accuracy. This also helps you improve your "gut feel" more quickly.

While intuition can provide valuable insights, it is essential to validate intuitive judgments with empirical evidence. Use intuition to generate hypotheses, identify patterns, or anticipate emerging trends, but always test these intuitions against objective data and rigorous analysis. Evaluate the consistency, reliability, and validity of both intuitive and evidence-based inputs, ensuring that decisions are informed by a balanced mix of instinct and empirical support. This approach helps mitigate overreliance on intuition while leveraging its strengths in conjunction with solid evidence.

Employ scenario planning and range forecasting to capture the uncertainty and variability inherent in future outcomes. Develop multiple scenarios based on different assumptions, conditions, and drivers, exploring a range of possible futures. This approach allows you to assess the potential impact of various factors on your forecasts and understand the boundaries of uncertainty. Using ranges instead of single-point estimates provides a more realistic and flexible representation of future possibilities, helping decision-makers navigate uncertainty and make more informed choices.

En mi experiencia, equilibrar la intuición y la evidencia al pronosticar implica utilizar la intuición como un punto de partida, buscar evidencia objetiva, validar y actualizar los pronósticos, establecer un proceso de toma de decisiones estructurado, ser consciente de los sesgos cognitivos, fomentar la cultura del datos y la toma de decisiones basada en datos, y buscar retroalimentación y aprendizaje.

Consider fostering a culture of continuous learning and improvement within your organization. Encourage ongoing training and development in both qualitative and quantitative forecasting techniques, and promote the use of advanced analytics tools and technologies. Ensure that your forecasting process is transparent, well-documented, and aligned with organizational goals and decision-making needs. By embedding these principles into your forecasting practice, you can achieve a more balanced, reliable, and effective approach to predicting future outcomes, combining the best of intuition and evidence.

Your forecast should take the risk profile into account. If it’s super risky, your forecast should be conservative. If it’s lower risk, reach for the stars. But never ever let your forecast be the reason you blow up.