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Reference Lines and Analytics Pane

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Overview
Difficulty
Intermediate
Duration
59m
Students
134
Ratings
4.5/5
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Description

Power BI allows business users to analyze data and share insights across all levels of an organization. It gives an end-to-end view of important metrics and key performance indicators through intuitive and interactive dashboards all in one place.

In this course, you will learn about several Power BI tools that would help you enhance reports to expose insights and present them in a way that appeals to a wide range of end-users.

Learning Objectives

  • Understand how to apply conditional formatting, slicers, and filters
  • Perform top N analysis
  • Explore statistical summary
  • Use the Q&A visual
  • Add a Quick Insights result to a report
  • Understand when and where to create reference lines by using the Analytics pane
  • Learn when and where to use the Play Axis feature of a visualization
  • Understand how to personalize visuals

Intended Audience

  • Beginners to data analytics
  • Business analysts
  • Business intelligence developers
  • Business intelligence managers
  • Anyone who wants to learn about Power BI

Prerequisites

  • Microsoft Power BI Desktop for PC/Windows users (free download)
  • Familiarity with preparing data using Power BI
  • Familiarity with modeling data using Power BI
  • A basic understanding of Power Query, Power Pivot, and DAX is a plus but not required
Transcript

Let's talk about Reference Lines and Analytics Pane. After we've got our reports and visuals ready, we might still want to do some further analysis like zooming in on important treads or insights such as next year's sales treads. Analytics pane captures all the analytical options available for any selected chart to highlight a line indicating the minimum mean or maximum values in a line chart. In this lecture, we will identify the visuals that allow the use of reference lines such as the line chart, use analytics pane to add trend lines, add constant lines, show multiple series, and change line color and line type.

So, in order to get started, let's go to Power BI Desktop and connect to our sample report. The analytics pane is only available for certain visualizations like a line chart. We can click on this area chart which shows this year's sales by month and change it to a line chart. Let's go to the visuals pane first. And I want to point out that we need to set our X-axis to a date-time type and make sure it's continuous and not categorical for the analytics features to work. And then go to the analytics pane. It's this third tab here. We can see all of the individual items that we can add to this report. For example, we can create a trend line. Let's turn it on. And we see a tread line passing through the chart. We can change the color of this, the style from dashed to solid or dotted. We can show this as combined series if we have multiple series in our visualization with highlighted values.

Now let's turn off that trend line and go to the constant line. So, if we were to add a constant line, go to 'Line' and set it to 1st of July, 2014 and make that red. Then we have this vertical constant line here in case we wanted a reference to see if our total sales was above or below a threshold. Let's remove it now. We can also create a minimum line, a maximum line, an average line, a median line, a percentile line, or a forecast. And let's switch forecast on, and this is going to create an actual forecast based on historical data. So, we can make this forecast length go out 20 points instead of the default 10. And click 'Apply' to make the forecast go much further. We don't have to go by points, we can also go by other dimensions. For example, let's go by months and forecast by four months. We can do that if we click 'Apply', and now we have a four-month forecast.

We also have the options to ignore a certain number of points and this is based on our selection for the forecast length. We can even set a confidence interval. So, if you want to be extremely confident that this forecast is going to be within the range, we can change it to 99%. Let's click 'Apply', and it's going to change that range a bit to make sure that our forecast falls within that range. But if you want to be a little bit more pinpointed, we can select a 75% confidence interval. Let's click 'Apply', and that's going to be a very specific confidence interval. We also have an option to apply seasonality, and this is important in case our data is pretty seasonal and we are running a store, so we can assume that it's seasonal. For example, there might be more purchases around holiday times.

So, we can say that our seasonality is probably every four points. We see the sales rise around February and dips around July. Given that each point is a month, so we can say we have a four-point seasonality every 12 months. And we can click 'Apply', and we see that it changed a lot because Power BI was smart enough to know that our data was seasonal by 12 months, and now we have a different forecast because we actually provided a seasonality. I just wanted to show that there are a lot of options for this analytics pane, and do keep in mind that there are different analytics options for different visualizations. For example, let's turn our line chart into a bar chart and go to the analytics pane and click on 'Constant Line', go to 'Line', and set it to 1st of May, 2014. And that's going to create this vertical constant line, and if we turned this bar chart into a pie chart, let's go back to our visuals pane, click on 'Pie Chart'. Go back to the analytics pane. We see there aren't any analytics features available to us. So, keep this in mind that only the line charts will allow us to create all of the analytics options available.

So, in this lecture, we identified the visuals that allow us to use analytics features and used the analytics pane to create multiple lines that would help us do some further analysis on important trends or insights in our report.

 

About the Author

Moatasim has been a data and insight consultant since 2014, driving data culture strategies in enterprises, non-profit organizations and tech startups to improve their decision making. He has teamed up with Fortune 1000 companies, MBB and Big Four consultants on complex engagements in government and private sectors. He has been a data analyst, business analyst, BI manager, and instructor. To date, Moatasim has created learning content relating to business intelligence, data analysis and machine learning, mostly within Power BI, Azure, SQL and Python. His hobbies included heavy metal drumming and meditation.