Your project data is incomplete and inconsistent. How will you salvage your analytical process?

To salvage an analytical process when dealing with incomplete and inconsistent project data, you can follow these steps:Assess Data Quality: Identify and categorize the types of data issues (missing values, duplicates, inconsistencies, outliers).Data Cleaning:Handle Missing Data: Use techniques like imputation, deletion, or interpolation to address missing values.Remove Duplicates: Identify and remove duplicate records.Correct Inconsistencies: Standardize data formats, correct typographical errors, and ensure consistent data entry.Data Integration: If data comes from multiple sources, merge datasets carefully, ensuring consistency in schema and format.Use Statistical Methods:Imputation: Fill missing values using mean, median, mode.

Spot the Issues: Scan your data for inconsistencies and gaps to establish a starting point for corrections. Review Collection Methods: Examine how the data was gathered to identify potential flaws. Verify Accuracy: Confirm key data points to catch early mistakes and prevent larger issues. Check Completeness: Assess the dataset for missing pieces and their importance. Revisit Sources: If needed, revisit original sources for clarity and context. Use Insights: Use the audit to identify issues and salvage reliable data.

Para abordar esta situación, propondría los siguientes pasos: 1. Realizar una revisión exhaustiva de los datos disponibles para identificar las áreas específicas donde faltan datos o incosistencia 2. Buscar y recopilar datos adicionales de fuentes confiables. 3. Implementar procesos de limpieza de datos para corregir errores y eliminar duplicados. 4. Aplicar métodos estadísticos para estimar los datos faltantes basados en los patrones observados en los datos disponibles. 5. Realizar controles de calidad para validar la precisión de los datos corregidos y estimados. 6. Implementar herramientas y algoritmos de automatización para continuar monitoreando la calidad de los datos. 7. Mantener una documentación detallada de los cambios realizados

In my experience, to identify the initial thing to do is to find out what the E2E of the data flow is like. This implies knowing how the business is using that data and working on that basis (if we can count on a member of the business as a source of information, even better). The next step is to use tools and skills that allow us to build a model or tool or, if it exists, a tool to measure the use of data in its different states (corrupted, unused, used, etc.).

You can delete missing or inconsistent data points. This dan be an effective strategy, if the missing data is not critical. You can also estimate data, with information that is available.

The imputation method substitutes reasonable guesses for missing data. It's most useful when the percentage of missing data is low.

In my experience, all data sets should be normalized, so that the data is complete, concise and in line with the desired purposes and objectives. On the other hand, normalizing data is a process that should always be done whether the data is inconsistent, dirty and just needs to follow the patterns of the data governance team. For this purpose, it is very useful for the teams to jointly develop the famous data quality rules.

one of the important things for the teams that use the data is to normalize as I indicated in the previous point. The data quality rules help a lot in this aspect. For data cleansing it is always critical to have a team that delivers the guidelines of how you want the data. In essence, the data analyst or data engineer should use data cleansing tools, either programmatic tools like python or visible tools like qlik or public cloud cleansing tools like gcp or aws.

I would suggest to start by evaluating the quality and reliability of each data source before diving for stats techniques for instance consider the statistical significance of quantitative findings, favoring those that meet accepted standards for validity. Techniques like outlier detection can help identify and manage anomalies, ensuring that these don't skew your analysis. For incomplete data, statistical methods such as maximum likelihood estimation or multiple imputation can be employed to make informed inferences about missing information, thereby minimizing the impact on the validity of your analysis.

In addition to leveraging technology, incorporating domain expertise is crucial in the data cleaning and transformation process. Subject matter experts can provide context that automated tools might overlook, ensuring that data corrections and transformations are meaningful and appropriate. Collaborating with domain experts can also help in identifying the most relevant variables and understanding the implications of missing data, ultimately leading to a more accurate and insightful analysis.