A data analyst is responsible for a monthly sales performance dashboard used by the executive team. The dashboard has become extremely slow to load, with refresh times exceeding five minutes. The analyst investigates and discovers the dashboard's source query pulls all transactional data from the company's entire five-year history, even though the visualizations only display data for the most recent month. Which of the following is the MOST effective initial step to address this performance issue?
Set up monitoring alerts to check for stale data in the source tables.
Initiate a peer review of the report's calculations to ensure their logical accuracy.
Apply data filtering to the source query to limit the dataset to only the relevant time period.
Recommend changing the source database's data structure to create a pre-aggregated summary table.
The correct answer is to apply data filtering. The scenario explicitly states the report is slow because it is querying five years of data for a one-month view. The most direct and efficient first step is to filter the data at the source to retrieve only the necessary records. This significantly reduces the data volume that the reporting tool needs to process, which directly addresses the performance problem caused by a large data size.
Creating a pre-aggregated summary table is a valid and powerful optimization technique, but it involves changing the data structure and is a more complex, time-consuming project. It is not the most effective initial step compared to the simplicity of adding a filter to a query. Peer reviewing calculations addresses logical correctness, not performance issues related to data volume. Setting up monitoring alerts for stale data addresses data freshness, which is a different problem from the one described.
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