¿Cómo puede convencer a las partes interesadas de la exactitud de sus predicciones?

Persuading stakeholders requires trust and validity in predictions. Here's how: 1. Transparentize Your Thinking: Share methodological journey and acknowledge limitations. 2. Bridge the Knowledge Gap: Use historical data and consider external factors. 3. Craft a Compelling Narrative: Tailor communication, use visuals, and focus on actionable insights. 4. Foster Open Dialogue: Encourage questions, be open to learning, and refine predictions. 5. Close the Loop with Outcomes: Track and communicate results, maintaining transparency. Bonus Tip: Showcase past successes for credibility. Trust is built through transparency, clear communication, and open dialogue.

Predictions should be based on long term historical data analysis and mining . This large sample size helps bring out consistent patterns which help with prediction . Historical data should be blended with any external factors which has direct impact on past results . So these patterns may cause similar effect for future scenarios as well . If you are talking to board room leadership , they might be interested to know aggregate level predictions , while someone from Technology area might look at more details around it .If you are talking to business stake holders they will be interested in both qualitative and quantitative support around these predictions .Operation teams would need some insight into how their Business workflows impact.

It is important to call out assumptions and level of confidence around them . You also need to articulate Data quality of sources and what additional measure you take to make sure data is reliable , consistent and if highest quality . You need to cite external or internal case studies around similar predictions . Data collected should be a combination of historical data and current data. Predictions should be provided with caveats and disclaimers. They should be indicative of the theory or concept . But there is possible to have alternate prediction for outlier cases .End to end data lineage should call out for Business logic around the data .Right level of data enrichment and integration is needed in order to make well rounded prediction.

Creating a feedback loop is important . Feedback can be about work you presented or audience’s recommendation or inputs . One of the effective ways of doing that can be to have a face to face interaction and create workshop like forums, which can have targeted breakout session. Based on next steps and action items , make sure changes are incorporated into underlying models , data and data quality . From this point on have recurring follow ups and reviews . Work iteratively improve quality of the predictions . Understand . Review any near term and long term roadmap they may have . Apply any machine learning algorithms in order to continually input I’ve quality . Invite the feedback on whether presentation of these estimates .