How to Extract Web Data with Power BI

By now you’ve probably heard that the Los Angeles Lakers were a pretty solid dynasty in the latter half of the 90’s. I was never a Michael Jordan and Bulls fan during their reign of terror in the 90’s. It all started with the Bulls first title at the expense of Lakers’ fans back in 1991.

So while I must admit that “The Last Dance” was a well executed documentary focused on a team I didn’t care for, it did evoke nostalgia for the 90’s.

Kobe Shaq

Although we suffering Lakers’ fans had to wait our turn, we did get the last laugh as “The Next Dance” revolved around a young Kobe Bryant and prime era Shaquille O’Neil.

I built a ribbon chart visualization in Power BI showcasing the top scorers from 1995 to the three peat years ending in 2002. Thank you Spencer Baucke for the ingenious web scraping technique!

Lakers Ribbon Chart Thumbnail

Follow along in the video and make a ribbon chart for your favorite NBA team.

 

As always, do great things with your data.

Anthony B. Smoak, CBIP

 

Inspiration ► https://bit.ly/2WZFWCA

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Check out other Power BI videos of interest definitely worth your time:

All views and opinions are solely my own and do not necessarily reflect those my employer.

Kobe & Shaq Image: David Sherman / NBAE via Getty Images file

Build a Power BI Pop Out Slicer

Save more screen for your team! The pop out slicer panel is a perfect way to conserve space while building out your dashboard (i.e., reports) in Power BI desktop. It really is a slick feature that allows you to conserve limited reporting space by hiding your slicers until the user presses a button to reveal your data filtering options.

In this video you can watch me build out the slicer panel step by step using bookmarks, selection panel and buttons.

Power BI Pop Out Slicer (Short GIF)

  • Bookmarks are a configured view of a report page, including filters, slicers, and the state of visuals.
  • The selection panel allows you to show and hide current objects on the current report page.
  • Buttons enable users to hover, click, and further interact with Power BI content

The data sample used for this tutorial is here: https://docs.microsoft.com/en-us/power-bi/sample-financial-download

As always, do great things with your data.

Anthony B. Smoak, CBIP

 

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Check out other Power BI videos of interest definitely worth your time:

All views and opinions are solely my own and do not necessarily reflect those my employer.

 

How to Drill Through in Power BI

One of the great options available in Power BI is the capability to “drill through” to another report page. In this manner you can focus on a particular entity such as a customer, internal division, supplier or any other dimension of importance.

Different users have different data needs. When designing a dashboard typically the Key Performance Indicators (KPIs) are aggregated at a high level on the initial visualization. This offers executives and management types a “bird’s eye view” of performance.

Personally, I am a fan of BANs (look up the term in a dashboard design context if you are not familiar) when I want to highlight key takeaways.

BANs

Subsequent lower level dashboard pages can offer analysts and others the ability to either explore data with additional interactivity or simply display a static detailed report. The point is to start at a high level and allow your user to drill to a more granular level of data.

In this video I demonstrate the use of the drill through functionality in Power BI. In this scenario, you are the Chief Supply Chain officer trying to gauge your Perfect Order Percentage KPI for several internal divisions. When it’s time to sit down with your four division mangers to discuss their performance on this metric, you want the ability to start at a high level and then drill through to a static report based upon their respective internal divisions or on a specific shipping error.

DrillThrough

Drill through on “In Full Delivery” error category

Do not try and cram every visualization, chart, table or gauge under the sun into a dashboard! Take advantage of drill through functionality and tailor your data presentation for specific user groups. This general concept applies to any data visualization tool, but if you’re using Power BI then this video will help you understand the specific steps required to enable drill through functionality.

I’m frequently questioned where I obtain mock data for my scenarios. My secret source is mockaroo.com which is a great starting point for developing test data.

As always, do great things with your data.

Anthony B. Smoak, CBIP

 

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Check out other Power BI videos of interest definitely worth your time:

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Sum Top and Bottom 10 Products by Sales in Power BI

In this video we will cover how to calculate the aggregate sum of only the Top and Bottom 10 Product Sales using DAX in Power BI. There are always multiple ways to accomplish a task with Power BI and DAX but I will share the technique I used to visualize the Bottom 10 Sales Products when there is a rare single tie among the products. The solution may be a bit over-engineered to my data-set but the aim is to share an approach you can use to tackle similar data issues in your dashboards. It’s well worth the watch!

I won’t give way the whole video but I’ll share the DAX formula to sum the Top top products by Sales Price from my table named ‘Company Sales Data’.

1_SumSalesTop10Products = 
CALCULATE(
          SUM('Company Sales Data'[Sales Price]),
          TOPN(
               10,GROUPBY('Company Sales Data','Company Sales Data'[Product]),
               CALCULATE(sum('Company Sales Data'[Sales Price]))
              )
         )

I have created a variable named 1_SumSalesTop10Products that uses the CALCULATE function to

  • SUM the [Sales Price] variable from the [Company Sales Data] table (see the first argument to the CALCULATE function);
  • But it only sums the [Sales Price] for the TOP 10 highest selling products, because we use the TOP N function to create a temporary table that only returns the products with the 10 highest aggregated Sales Prices;
    • The GROUP BY function is used to aggregate the table rows by product and then the CALCULATE argument sums the Sales Price for the aggregated products;

Don’t let this scare you off, watch the video to get a better understanding, and to learn how I sum the Bottom 10 products by Sales Price.

As always, get out there and do some great things with your data!

 

 

Top and Bottom 10 Products by Sales Using RANKX in Power BI

In this video we’ll venture outside of the default Power BI TOPN functionality used to isolate the top and bottom N values in a visual. Because you’re an astute follower of my blog and YouTube channel, you want to know more than default functionality. The key to pulling off this feat lies with the RANKX function. By using RANKX to provide a ranking to each row in our data set, we can then determine the TOP and BOTTOM 10 values.

Of course watch the video for further breakdown, but they key to using RANKX effectively in Power BI is to use the ALL function as a parameter. In this way the contents of the entire table are considered for appropriate ranking.

The DESC or ASC parameters will enable the calculation of a rank in either descending or ascending sort order.

And finally the DENSE parameter tells RANKX how ties should be handled in the data. For example if you provide DENSE as a parameter, if 10 values are tied with a rank of 1 then the next value will receive a rank of 2.

Power BI Top 10 and Bottom 10 Thumbnail

Here is an example use of RANKX that will return a ranking of Sales Price by Product, that ranks the results in descending order (highest Sales receive the lowest ranks) and ties are in contiguous order. Watch the video to determine how to calculate the BOTTOM 10 ranking and to use RANKX to enable the top or bottom 10 values in a visualization.

ProductRank = RANKX(ALL('Company Sales Data'[Product]), calculate(sum('Company Sales Data'[Sales Price])),,DESC,dense)

As a refresher, check out this popular video to build the calendar table referenced in this video: Power BI Dashboard Tutorial: Year over Year Difference Analysis

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Create a Customizable Heat Map in Power BI

In this video we’ll learn how to create a customizable heat map in Power BI without using the prepackaged downloadable visual. A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors. A heat map helps draw your eye to the most and least popular areas within the matrix. The cells contained within the table either contain color-coded categorical data or numerical data, that is based on a color scale.

Matrix

Wrong Matrix

I have some fun in the video with a dashboard that I constructed using a publicly available data set from Microsoft, but in the lesson we’ll create the following:

Heatmap

Make sure to watch the video, download the data set and follow along with the instructions.

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Power BI Dashboard Tutorial: Year over Year Difference Analysis

I want you to increase your efficiency and to stop using spreadsheets for every single analysis.

Everybody works with time series data at some point in time. Year over year (also known as YoY) analysis is one of the most useful analyses you can perform to determine changes, analyze growth and recognize trends in quantity on an annual basis.

Unfortunately, most data preparers are used to performing some unaesthetic flavor of this analysis using only Excel (looking at you FP&A). Without the benefit of using visualization to easily recognize trends, data consumers are forced to work harder to tease out the most salient information.

If you have access to Power BI Desktop (available for free), then you can perform a tabular year over year difference calculation and then tie that information to a bar chart that will help you visualize the variances.

In this video I will show you how to create a calendar table in DAX (Microsoft’s formula expression language) and use that table to enable a year over year analysis of customer orders at fictional Stark Industries. You don’t need to be an expert in DAX to take advantage, just type in the date calendar formula you see in the video and tweak the simple calculations to fit your data.

You could obviously perform a simple YoY analysis in Excel, but I want you to stay relevant and learn something new!

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

 

Filter Top N Values with a Slicer in Power BI

In this video you will learn how to filter the top N values shown in your bar chart visualization using a slicer.

  1. This technique uses one measure that generates a number 1-10, that will be applied to a slicer.
  2. Another measure will basically rank all of the values associated with your data bars and only return the values that are less than or equal to the number you select in the slicer.

The comments that I apply to the DAX function should help make it easy to understand. I have to give a shoutout to GilbertQ from the PowerBI community for coming up with the  initial approach which I tweaked for the video.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel!

How to Generate a Forecast in Power BI

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.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Use the Power BI Switch Function to Group By Date Ranges

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.