When it comes to data and analytics, would you rather lean more toward risk aversion or tolerance?

Agree with all the other comments here - it’s dependent on the use case and sensitivity / criticality of the decision and how reversible actions are based on it (2 way door in Amazon parlance), the sensitivity of the data and the overall maturity of the organisation in making data driven decisions.

I am more risk tolerant when it comes to application of data and analytics in driving business growth, Data does have intrinsic value, like an ingredient that goes into making any dish. The overall value could exponentially increase based on how the ingredient is used, what recipes are used, and how skillful and creative the chef is. However, if the ingredients are not used in a timely manner, they can get stalled and even lose intrinsic value over time. I believe that businesses need to take calculated risks and embrace new data-driven strategies to remain competitive in today's fast-paced environment. 

Both Suzi and Jason's points resonate with me, and I'd add that the data maturity of an organization also plays a crucial role. It's important to consider the phase an organization is in regarding its data and analytics capabilities. For a company at the inception stage, assessing organizational readiness—including the understanding and support from stakeholders like boards and executives—is vital. Additionally, external factors, such as macroeconomic conditions, can influence an organization's risk appetite and tolerance. In essence, not all data is treated equally, and understanding the specific use case and external influences is key to determining the appropriate level of risk tolerance.

The decision between risk aversion and tolerance hinges on the specific use case. For internal environments aimed at experimentation, especially with anonymized data, we can afford a higher risk tolerance. However, with emerging technologies like generative AI, where legal frameworks are still developing, a risk-averse approach is prudent. This cautious approach helps ensure compliance and safety in our contracts and partnerships, avoiding potential legal pitfalls and negative publicity.

The approach largely depends on the classification of the data involved. For non-confidential, unsecured, or non-regulated personally identifiable information (PII), there is more freedom to explore, which allows for a higher risk tolerance. In scenarios where data is merely directionally correct rather than precise, taking greater risks is permissible because the need for absolute accuracy is less critical. However, when dealing with data that requires precision or falls under strict classifications, a more controlled, risk-averse approach is necessary. In essence, if the data is less sensitive or the situation less critical, one can afford to be more adventurous and exploratory.