Category: Dashboards

Power BI error bars for uncertainty visualization

How To Utilize Error Bars In Power BI To Visualize Uncertainty In Your Data

Posted on by Bb

How to Use Power BI Error Bars for Uncertainty Visualization: Step-by-Step Guide

Power BI error bars for uncertainty visualization are a game-changing feature, enabling data analysts and business users to represent confidence intervals, forecast uncertainty, and data variability directly on their line charts. In this comprehensive tutorial, we’ll explore everything you need to know: from enabling the feature to advanced interactivity through parameters.

What Are Error Bars and Why Do They Matter in Power BI?

When you’re visualizing forecast data or any measurement with natural variability, showing point estimates alone can be misleading. Power BI error bars for uncertainty visualization allow you to display possible ranges for each data point, communicating confidence and transparency in your data storytelling. This is especially critical for:

  • Sales forecasts with seasonality

  • Scientific measurements with instrument error

  • Survey results or estimates

By making uncertainty explicit, you empower your viewers to interpret results more accurately and make informed decisions.

Enabling Power BI Error Bars for Uncertainty Visualization

Before using error bars, make sure your version of Power BI Desktop supports them (this step is only needed in older versions):

  • Go to File > Options > Preview features.

  • Enable “Error Bars.”

  • Restart Power BI Desktop.

Pro tip: Error bars settings may continue to evolve, so always update Power BI for the latest enhancements.

Building Your First Power BI Error Bars for Uncertainty Visualization

1. Set Up Your Base Visual

Start with a basic line chart displaying your key measurement (e.g., Monthly Sales).

  • Drag your date/time to the X-axis and your main value (e.g., Sales) to the Y-axis.

2. Define Upper and Lower Bound Measures

You need two measures for each point—Upper Bound and Lower Bound—that will define the error bars.

Example DAX for relative error bars:

text
Sales Upper Bound = SUM(Sales[Amount]) + 5000
Sales Lower Bound = SUM(Sales[Amount]) - 5000

Place these measures in the chart’s “Error Bars” section.

You can also use dynamic calculations:

text
Sales Upper Bound = SUM(Sales[Amount]) * (1 + [Uncertainty Parameter])
Sales Lower Bound = SUM(Sales[Amount]) * (1 - [Uncertainty Parameter])

3. Configure the Error Bars Visual

Open the Analyze pane:

  • Under “Error Bars,” toggle On.

  • Choose “Relative” (fixed increase/decrease) or “Absolute” (direct upper/lower value).

  • Customize style: error lines, bars, or shaded areas for visual clarity.

Advanced Technique: Interactive Power BI Error Bars for Uncertainty Visualization with Parameters

Take uncertainty modeling further by letting viewers control the amount of uncertainty shown, using Power BI’s What-If parameters.

Steps:

  1. Create a What-If Parameter:

    • On the Modeling ribbon, select “New Parameter.”

    • Set as decimal, with a reasonable range (e.g., 0.0 to 0.3 for 0–30%).

  2. Reference the Parameter in Your Bounds:
    Update your upper/lower bound measures to multiply the main value by (1±parameter value).

  3. Add Parameter as Slicer:
    Place the parameter on the report canvas. Now, users can adjust a slider and watch the uncertainty range change interactively.


Why is this powerful?
Viewers can explore best-case/worst-case outcomes, stress test forecasts, or tailor visuals to their own risk tolerance—making Power BI error bars for uncertainty visualization remarkably interactive.

Practical Tips and Troubleshooting

  • Relative vs. Absolute: Use relative error bars for a fixed increment (±X), absolute for data-driven bounds (e.g., statistical deviations).

  • Labeling: Consider adding text or tooltip explanations so viewers grasp what the error bars represent.

  • Complex models: For forecast models with statistical confidence intervals, you can calculate upper/lower bounds using DAX or integrate with external R/Python forecasts.

  • Data Model: Store parameter values and error range calculations in your data model for auditability and reusability.

Real-World Scenarios for Power BI Error Bars for Uncertainty Visualization

  • Sales Forecast Dashboards: Show forecast ranges during high volatility periods.

  • Scientific Data: Display measurement error for each point, letting stakeholders see the instrument precision.

  • Customer Surveys: Represent margin of error due to sample size.

Conclusion

Embracing Power BI error bars for uncertainty visualization not only makes your reporting more honest but also improves trust and understanding among your audience. By combining error bars with interactive parameters, you offer viewers a dynamic, transparent, and engaging analytic experience.

With these steps, you’ll unlock the full potential of Power BI error bars for uncertainty visualization, turning simple line charts into robust storytelling tools.

Would you like a downloadable sample file, sample DAX, or even deeper dives into the DAX logic? Let me know in the comments!

Chart Screenshot/Example

Power BI error bars for uncertainty visualization

Do You Need Personalized Help and Custom Solutions?

If you get stuck or you would like to explore solutions and automation possibilities, please can reach out to me for help as I do offer consulting services as time allows.  I have over 20+ years’ of expert level experience delivering excellent, custom, strategic solutions in Excel, BI, Access, SharePoint and more. 

I have been called a guru and hero more times than I can count, as I am a great listener and truly have a knack for asking the right questions to understand unique business challenges. I am very passionate about crafting tools and processes that work for users of all levels and experience. 

Reach out today and let’s discuss how I can help you and your business!

I also offer one-on-one tutoring for customized learning and upskilling. Visit my consulting page and send a message if you are interested.

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Solution for Power BI Refresh Error in the Web Service

Posted on by Bb

Web Service Power BI Refresh Error When Trying to Refresh On Premises 32-bit Database/Source

I recently worked with a client who is still using an old 32-bit Oracle instance with lots of dependencies. Upgrading is not currently an option. In her Power BI report/model, she is querying several tables in the Oracle database. Unfortunately due to the 32-bit architecture she was not able to schedule her refreshes, even using the desktop gateway.

The Power BI refresh error she received was “Failed to update data source credentials: An error happened while reading data from the provider: ‘Attempt to load Oracle client libraries threw BadImageFormatException. This problem will occur when running in 64 bit mode with the 32 bit Oracle client components installed.'”

Workaround for Power BI Refresh Error in Web Service When Trying to Refresh On Premises 32-bit
Power BI Architecture Error

As a result, she found she could only refresh this report using the Power BI desktop client. She would then manually publish to the cloud. This was cumbersome as the data refresh took quite some time, and she needed it done daily. I came up with two solutions for her, both of which we implemented and tried. One proved to better for us, and ultimately she is successfully using this first solution/workaround that I will cover.

Solution #1 – Export Query Results to CSV and Post on SharePoint

  • So just as the title of the section states, the final workaround was for us to automate the generation of CSV files to ultimately be consumed by Power Query.
  • We did this by using a combination of scripts pulling data via SQL in VBA (Access, Excel) and via SQL in Python. You can pull the data any way you are most comfortable (macros, etc.).
  • The query results are then outputted to the CSV files, which are written directly to SharePoint folders.
  • After all this was set up and the initial files were changed, we then changed the source of the data in Power Query, to now navigate to SharePoint folders and then process the CSV files.
  • It is important to note that you are not required to use CSV files. These made sense in our case due to the amount of data we were working with (generating several very large files nightly). You can write to any supported file format if that works better in your case, as long as you can place it on SharePoint and Power Query can process it.
  • Also as noted we used a separate folder for each unique query. If you are also replacing several queries, you can either do this (write to separate folders) or if you would prefer just one, then utilize different naming convention patterns to filter.
  • Everything is then kicked off via a command line batch script (.bat) using the Windows task scheduler on her desktop to run overnight. We also set up the same script on a colleague’s PC as a backup/for redundancy in case something were to happen, but it is not actively running.
  • After refreshing and publishing the updated report with the new SharePoint sources, I then went into settings in the Web Service and added the credentials there for SharePoint. I saved and kicked off the refresh in the browser.
  • The refresh was successful, so I then set a daily schedule for the web service to refresh automatically. No intervention has been needed since implementation!

Solution #2 – Automate the Power BI Desktop Refresh Process Using Python

  • This method is much less convenient in my opinion, as after it had been implemented, it failed often and required manual intervention several times before we moved on to the other solution.
  • Another caveat is that this solution requires the user to have at least some familiarity with Python (or finding someone with Python knowledge to tap as a support resource).
  • Also, obviously someone needs to install Python, the required package and then set up the automation to run the scripts.
  • Important note – it is possible to install a portable version of Python if you do not have admin rights on the desktop.
  • The package is called pbixrefresher (repo is linked).
  • This gentleman does a great job of walking through setting it up in his YouTube video about it: https://youtu.be/wtXXpicCUlY

I hope this post has been helpful and has maybe given you some ideas on how to proceed if you or an organization you are supporting is facing this same situation with older data sources. Also, I’d love to hear about other solutions if you’ve faced the same issue and handled it differently.

Please reach out with any questions or if you would like to see further coverage of any of the concepts/steps mentioned in this post. I am also available on a limited basis for consulting as my schedule is full as of late (a good thing!). Just fill out the contact form and I will get back to you.

rank in Access

Rank in Access Query (Sales Ranking)

Posted on by Bb

A common request I get these days is for ranking of sales in Access by region, state and item combination for analysis and space management.  In my business, we have some overlap, where a state can be part of two separate areas or regions, for example Southern CA vs. Northern CA.  It’s helpful for us to show separate rankings for each region and state combo, as the results often tell a different story for each.

The Pass-Through query below enables me to determine the ranking at each of these levels.  By entering as a Pass-Through query, you can utilize the Rank and Partition SQL commands.  After running this query, I bring the data into Excel for further analysis, but this query is a good starting point for any similar analyses.

Here is the full Access pass-through query, color coded for the explanation below:

Select Region, State, Product_ID, SumSaleQ, StoreCount,Rank
FROM (Select Region, State, Product_ID, SumSaleQ, StoreCount, Rank() over (Partition By state, Region Order by SumSaleQ DESC)as rank
FROM (Select s.Region, s.state, m.Product_ID, sum(m.sale_quantity) as SumSaleQ, count(distinct(m.site_num)) as StoreCount
FROM Store_monthly_sales m, FR_Site_Master s
WHERE m.Site_num = s.Site_num AND m.accrual_month in (‘202007′,’202006′,’202005’)
GROUP BY s.Region,m.Product_ID,s.state)
WHERE SumSaleQ >5)
WHERE rank <205
Order by State ASC, Region Asc, Rank Asc

Explanation

Query 1

Diving right in, since we are measuring at multiple levels, we have some nested queries.  Working from the inside out, my first nested query is this one:

(Select s.Region, s.state, m.Product_ID, sum(m.sale_quantity) as SumSaleQ, count(distinct(m.site_num)) as StoreCount
FROM Franchise.Store_monthly_sales m,Franchise.FR_Site_Master s
WHERE m.Site_num = s.Site_num AND accrual_month in (‘202007′,’202006′,’202005’)
GROUP BY s.Region,m.Product_ID,s.state)

Query 2

We then nest that within this query (the WHERE clause is optional – I am dealing with sales in the thousands, and want to exclude items with a sales quantity less than 5.  Note the Rank() over and Partition By Commands here:

(Select Region, State, Product_ID, SumSaleQ, StoreCount, Rank() over (Partition By state, Region Order by SumSaleQ DESC)as rank

FROM …

WHERE SumSaleQ >5)

Query 3

The final nesting and main query is selecting the rank from the second query, along with the other pertinent attributes.  The WHERE clause is again optional, I am looking to limit my results to the top 200 results only for each region and state combination. I use 205 as the figure to allow for ties.  The Order by is also optional.  I like to order the results by state, region and then rank ascending.

Select Region, State, Product_ID, SumSaleQ, StoreCount,Rank

FROM …

WHERE rank <205
Order by State ASC, Region Asc, Rank Asc

Full Query (no color coding)

Select Region, State, Product_ID, SumSaleQ, StoreCount,Rank
FROM (Select Region, State, Product_ID, SumSaleQ, StoreCount, Rank() over (Partition By state, Region Order by SumSaleQ DESC)as rank
FROM (Select s.Region, s.state, m.Product_ID, sum(m.sale_quantity) as SumSaleQ, count(distinct(m.site_num)) as StoreCount
FROM Store_monthly_sales m, FR_Site_Master s
WHERE m.Site_num = s.Site_num AND m.accrual_month in (‘202007′,’202006′,’202005’)
GROUP BY s.Region,m.Product_ID,s.state)
WHERE SumSaleQ >5)
WHERE rank <205
Order by State ASC, Region Asc, Rank Asc

Conclusion

I hope this post will help you in your efforts to rank in Access.  It can be a bit of a challenge, but hopefully the breakdown of the steps here will help you achieve your ranking goals.  Please comment any questions, feedback, or what you would like to see next!  Share this post if you feel it would help someone else! 🙂

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