This tutorial addresses the common problem of missing data in time series visualizations, particularly when dealing with progressive or accumulative data in Power BI. It explains that while missing data points might appear as zeros in standard charts, this can lead to inaccuracies when values represent cumulative totals. The video demonstrates two solutions: first, filtering out the missing dates entirely for a simplified view, and second, a more complex approach involving DAX calculations to impute values by carrying forward the previous day’s data. This ensures a continuous and accurate representation of the time series, even when data is missing due to business closures or system inactivity on certain days. The tutorial guides viewers through creating calculated columns to derive “previous day” values and “revised” columns that incorporate these values, ultimately producing a more reliable visualization.
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