In this video I’ll demonstrate how to use the forecasting analytics option in Power BI. Although Power BI’s forecast algorithm is a black box, it’s more than likely using exponential smoothing to generate results. At a very high level, exponential smoothing is an algorithm that looks for patterns in data and extrapolates that pattern into the future. To help exponential smoothing perform at an optimal level, it is very important to pick an accurate seasonality estimation, as this will have an outsized effect on the time series forecast.
If your data points are at the daily grain, then you’d use 365 as your seasonality value. If your data points are at a monthly grain, then you’d use 12 as your seasonality value. Generally, the more seasonality cycles (e.g., years) that you provide Power BI, the more predictive your forecast will be.
Without giving away the whole video, here is a pro and a con of using forecasting in Power BI.
Con: As I stated earlier the exact algorithm is a black box. Although based upon a Power View blog post, we can reasonably assume exponential smoothing is involved. Furthermore, the results cannot be exported into a spreadsheet and analyzed.
Pro: The ability to “hindcast” allows you to observe if the forecasted values match your actual values. This ability allows you to judge whether the forecast is performing well.
Check out the video; I predict you’ll learn something new.
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In this latest video, I’ll explain how to use a handy DAX function in Power BI in order to group dates together for reporting. We’ll examine a dashboard that contains fields corresponding to purchase item, purchase date and purchase cost. We’ll then create a calculated column and use the SWITCH function in Power BI to perform our date grouping on the purchase date.
Watch the video to learn how to group dates into the following aging buckets, which can be customized to fit your specific need.
- 0-15 Days
- 16-30 Days
- 31-59 Days
- 60+ Days
If you are familiar with SQL, then you’ll recognize that the SWITCH function is very similar to the CASE statement; which is SQL’s way of handling IF/THEN logic.
Even though we’re creating a calculated column within Power BI itself, best practice is to push calculated fields to the source when possible. The closer calculated fields are to the underlying source data, the better the performance of the dashboard.
The humble bar chart is the heart and soul of any visualization tool and is the most effective way to compare individual categorical values. We as humans are very adept at detecting small differences in length from a common baseline .
To quote the Harvard Business Review , “The ability to create smart data visualizations was once a nice-to-have skill. But in today’s complex business world, where the amount of data is overwhelming, being able to create and communicate through compelling data visualizations is a must-have skill for managers.”
If you’re going to start learning a new visualization tool, there is no better place to start than with bar chart basics. In this video I will share how to place a “percent of total” measure (i.e. value) on a Power BI bar chart. We’ll also briefly touch upon customizing the chart’s diverging color scheme.
Since Microsoft is basically giving away Power BI Desktop for free, it may become as ubiquitous as Excel. Don’t be left out!
 Cotgreave, A., Shaffer, J., Wexler, S. (2017). The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Hoboken, NJ: John Wiley & Sons, Inc.
Use the unpivot functionality in Power Query (a free Excel add-in) to easily turn your cross-tabbed data into a more normalized structure. The normalized data structure will grant you the flexibility to create additional analyses in a more efficient manner.
Download: Power Query Excel Add-In
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