In my projects, I’ve often dealt with missing data and used key strategies to address it: 1. Identify alternative sources: I look for complementary datasets to fill gaps. 2. Use imputation methods: Techniques like mean substitution or regression help estimate missing values. 3. Consult stakeholders: I assess the impact with stakeholders and adjust the analysis if necessary. These approaches help maintain project integrity despite missing data.

The approach depends on the nature of the missing data. The analyst may proceed to check for discernable patterns within the missing observations. If the data are missing in a 'systematic' manner, the analyst should revisit the data generation process and check for errors in recording observations, sampling and measurement. The analyst may use imputation methods to fill in missing observations based on the available data, or exclude such rows from the analysis. A thorough comparison of the two methods is crucial to making an informed decision about the missing data.

If I assume that this Key data mean that the project hold value only by relying on it, these are the way I address it just like suggested : Ask for new data set : The first solution is to look for the missing data or a substitute. Missing data doesn't always mean non existent data. Pull up with data engineer or data sourcing lead. Discuss with stakeholders : Discussing with stakeholders in other to address the issue and evaluate if the scope of the project can be changed or if the analysis may be proceded only taking into account the existant and relevant data. Most of the time, because project relying on data, we must ensure the availability, source and integrity of data before starting a project. This is where it start ! Thanks!

When my high-priority statistical project faced missing key data, I took a structured approach. First, I assessed the nature of the missing data, identifying if it was random or had patterns. For smaller gaps, I used simple imputation methods like mean or median, while for larger gaps, I applied advanced techniques like multiple imputation or regression. I also explored alternative data sources to find datasets that might contain similar information. To validate my approach, I cross-checked results with sensitivity analysis. Throughout, I communicated transparently with stakeholders about the limitations and strategies I used to address them, ensuring the project stayed accurate and on schedule.

I think the impact of the missing data determines your next line of action. I needed to do some analysis on claims and the data shared was an incomplete one and unfortunately there were no alternative sources. The first step I took was to perform the analysis for the previous period and compare with the available results then to measure the impact of the missing data. The impact was much, so I tried using another methodology that uses premiums and claims. So periods where the claims data was missing were completely pulled out. The result was better but still not accurate. So I suggested we use another methodology that gives a better approximation while we try to get those missing data or at least build more data for the analysis.

Determine the importance of the missing data and explore potential adjustments to the project's scope or objectives. • Leverage domain expertise: Collaborate with experts to make informed assumptions or derive insights from available data. • Incorporate sensitivity analysis: Assess how missing data could affect outcomes, helping to gauge reliability. Missing data can undermine analysis, but proactive steps, such as alternative sourcing, imputation, and stakeholder collaboration, mitigate risks and maintain project integrity.

HARIA LO SIGUIENTE: 1.REALIZAR UN ANÁLISIS COMPLETO, DESCARTANDO CASO CON VALORES FSLTANTES EN CUALQUIER VARIABLE PARA CONDUCIR A UNA REDUCCIÓN SIGNIFICATIVA EN EL TAMAÑO DE LA MUESTRA Y AL POSIBLE SESGO SI LOS DATOS FALTANTES NO SON COMPLETAMENTE ALEATORIOS, CONSIDERANDO DETENIDAMENTE SI EL CONJUNTO DE DATOS RESTANTES ES REPRESENTATIVO DE LA POBLACIÓN ANTES DE ELEGIR ESTE ENFOQUE. 2. EMPLEARIA LA ELIMINACIÓN POR PARES QUE ES UN MÉTODO QUE SE OCUPA DE LOS DATOS DISPONIBLES PARA CADA ANÁLISIS, conserva una mayor parte del conjunto de datos porque sólo excluye los casos en los que faltan datos relevantes para las variables especificas que realizan. Esto da lugar a diferentes tamaños de muestra para diferentes análisis del mismo estudio.