Tell about the difficulties and mistakes in the project.

Challenges and errors related to data and analysis processes often arise in projects:

• Data quality: gaps, duplicates, inconsistency, errors in source data. This requires additional cleaning and validation.

• Incomplete requirements: sometimes tasks are formulated unclearly, leading to misinterpretation and the need to redo work.

• Technical limitations: lack of resources, slow queries, issues with integrating different systems.

• Analysis errors: incorrect choice of methods, misinterpretation of results, which can lead to erroneous conclusions.

• Communication: difficulties in conveying results and explaining technical details to non-specialists.

For example, in one project, I encountered that sales data contained duplicates due to errors in the collection system. This led to inflated figures. To resolve this, an additional step of cleaning and verifying the uniqueness of records was implemented.