Review data regularly, not when you only need to make a decision. Turn it into a repeatable process that includes validating the data and cross checking along the way.

Trust but verify has saved me from costly mistakes. Before any major decision, I run a 'data triangle': cross-reference multiple sources, challenge assumptions with reverse analysis, and get boots-on-ground validation from team members closest to the data. Also, clear documentation of assumptions and methodology reduces biases and improves confidence in the decision-making process.

Before taking a pivotal business decision, it is important to reflect on a high level which data most critically affects the decision. What guided you in the direction that you want to go? Which scenarios could make you regret this decision? Once the critical factors are apparent, one should dive in to validate the data by checking the source of the data, as well as its consistency with other sources and general top-down or bottom-up estimates. However, depending on the complexity of the data it is advisable to also consult with other subject matter experts, who can spot inconsistencies in the data.

When the correct interpretation of data is the backbone of informed decision making in many vital spaces (e.g. healthcare, government, education), the data behind any policy needs to be reliable, accurate and high quality. Frequent data quality checks and practices should be applied to the output of any application that creates records that are then integrated into data processes and analytics reporting. Additionally, data should be represented honestly and appropriately in visualizations, to allow stakeholders to draw conclusions based on a realistic depiction of a dataset. When data is misrepresented in visualizations to show potentially inaccurate and unclear depictions, this could be described as unethical and dishonest.

Before making a critical business decision, ensuring data trustworthiness is essential. Start by verifying the accuracy, completeness, and consistency of your data while ensuring it’s sourced from reliable and up-to-date systems. Check for relevance to the decision at hand, audit for potential biases, and use statistical methods to validate insights. Collaborate with experts to interpret the data and document your processes for transparency. Finally, leverage tools that flag anomalies and perform sensitivity analyses to ensure your decision holds up across various scenarios. Trustworthy data is the foundation of confident and impactful decisions.