What do you do if stakeholders doubt your data mining findings?

One should be transparent to resolve any possible doubts. Going through the CRISP-DM cycle and elucidating every step would help the audience realize how the results were obtained. Stakeholders usually don't have prior knowledge of data mining, so one should explain the concepts as simply as possible and avoid using jargon, which makes them more confused.

If stakeholders doubt your data mining findings: 1. Communicate clearly. 2. Provide context for the findings. 3. Demonstrate data quality. 4. Showcase validation processes. 5. Align with business goals. 6. Invite collaboration. 7. Address concerns proactively. 8. Provide additional evidence. 9. Seek third-party validation if needed. 10. Iterate and improve over time.

I think doubt always arises from lack of transparency on how results were achieved. This can be minimised by documenting the steps taken to arrive at the results. The methodology should always be clear before communicating the results

I thing by clarifying doubts helps us in enhanced understanding, build confidence, promote critical thinking, improving communications, facilitate learnings

Transparent communication is important when stakeholders are unsure of the results of their search for information. We begin by reviewing the methods, assumptions, and limitations of the analysis. Addresses specific questions asked by stakeholders and reviews documents or revises reviews as necessary. Provide additional details, examples, or visualizations to clarify findings and demonstrate their relevance to business objectives. Encourage open discussion to understand perspectives and provide insight into how findings align with the organization's broader trends or goals. Finally, building trust through open communication and collaboration is key to addressing concerns and engaging stakeholders.

Neste ponto é que se torna últil a proposição de métricas de negócio, para avaliar a qualidade da solução. No momento de apresentar os resulados para os stakeholders, torna-se um pouco mais fácil demonstrar os impactos em algo tangível, muito mais do que apenas as métricas do modelo. A forma de apresentação e a história contada sobre a análise também são muito importantes, buscando explicar em termos simples conceitos complexos.

I think educating stakeholders can go a long way when creating shared understanding of success from predictive models. It is equally important to understand the main objectives of the stakeholders and what they really care about or know about the problem, as this can help to really guide the data mining process whether it means understanding better the domain knowledge that is required for accurate identification of the right features, biases in data or temporal patterns to watch out for or use the same shared understanding to contextualize the outcomes for the shareholders and earn their trust by showing that you understand enough about their domain to create credible models.

I think one of the most important factors in the data mining is really spending time to understand the domain in which data is collected, the main objectives of the stakeholders and how they define success. This process allows to create trust through dialogue and also use know facts in that space to validate any data patterns or predictions. For example, once I used data mining models for a financial landing company and our clustering algorithm found very distinctly separated group of customers, which we later found were actually related to a fraud that they were aware of. Since then they trusted the clustering algorithm to separate their customers into groups based on their similarities in spending patterns.