Explore the relationship between employee experience and salary growth through a simple regression analysis using Power BI's Python visualization tool. This step-by-step guide demonstrates how to source data from Excel, create visual regression models with Python, and interpret results that reveal how salary scales with experience. Perfect for those looking to optimize hiring and compensation strategies, this analysis highlights valuable insights into workforce trends.

In Power BI, we can use external tools to greatly optimize our work, such as checking reports, statistical report information, or querying report performance. These functions are not directly provided in Power BI desktop. We need to use some third-party tools to do these things. Here we mainly use pbiexplorer

Unlocking the potential of Copilot

• Suggest content for a report
• Assisted Report Creation
• Prompt Data Insights
• DAX Formulation and Description

There is nothing more frustrating for creating reports or analyses than extremely dirty data. By this, I mean poor data entry, usually coming from surveys or spreadsheets.

Power Query is a good ETL tool, but it’s important to use it properly to avoid ending up with unsustainable steps in our script. For that, we have already written a post to help reduce steps. What we will see in this article is focused on simulating what we can do within “Add Custom Column” but by transforming the column that needs cleaning without creating another column with the desired custom code.

In the process, we will be adding various conditions to see the power we have.

Process massive amounts of raw data files located on an on-premises server and build an end-to-end data analytics solution by leveraging various workloads of Microsoft Fabric.

To meet accounting standards like ASC 842 and IFRS 16, which require treating monthly operating lease payments as finance leases by recording right-of-use (ROU) assets and lease liabilities, many companies turn to off-the-shelf or internally developed software solutions tailored for lease accounting. After observing various software solutions in action, I decided to simulate and replicate these calculations using Power BI. This approach allowed me to accurately generate the necessary accounting assets and liabilities from monthly payments, similar to those produced by specialized software.

As of 2024-08 Power BI doesn’t offer easy mechanism to filter data based on a date that could be in past or future.

In the case of the report I did, we wished to show all the relevant data until a date, that can be a few days before or after today.

This solution shows how to create a slicer selection box of 14 days before and after current date.

After ingesting, cleaning and transforming data, it is essential to visualize it in order to explore its patterns, trends and relationships. Visualizing data can help identify outliers, anomalies, clusters and correlations that might not be evident from numerical or textual data alone. Data visualization can also facilitate the communication and presentation of the results of data analysis to different audiences, such as stakeholders, customers or decision-makers. By using appropriate charts, graphs, maps or dashboards, data analysts can convey insights and recommendations in a clear and engaging way.

Power BI has introduced an exciting feature that allows users to leverage the capabilities of Power Automate through buttons within Power BI reports. This enhancement significantly improves the user experience by enabling users to perform operations directly by clicking on a button from the report interface.

In this blog post, we’ll provide a step-by-step guide on how to utilize a Power Automate button to trigger automated tasks within a Power BI report. Here I have taken an example to demonstrate how a single click can send a new lead directly to an Excel spreadsheet.

Let’s see how we can implement this.

Hey there! Whether you're a seasoned pro or just dipping your toes into the world of data analytics, we all know there's a new player in town —Microsoft Fabric. Since the first Fabric Conference to the most recent updates, a lot of advanced capabilities have been introduced in the game. Let's take a moment to take a step back and brush up our Fabric basics, and ensure you are ready to revolutionize your data game.

Data plays an important role in decision making, and organizations that can harness its power can gain a competitive edge in the market. However, data alone is not enough. To extract value from data, organizations need to build end-to-end data analysis pipelines that can handle the entire data lifecycle, from data ingestion, data cleaning, data transformation, to data visualization and analysis. In this blog post, we will explore why end-to-end data analysis is important for organizations and how it can help them achieve their goals.

In Power Query, there are two functions to manage columns in a table, which are accessible through three different actions in the User Interface. Located on the Home tab, these actions allow you to perform the following operations:
👉 Choose Columns (which calls: Table.SelectColumns)
👉 Remove Columns (calls: Table.RemoveColumns) and
👉 Remove Other Columns (calls: Table.SelectColumns)

Choosing the right method depends on your specific needs. Let’s explore this with a practical scenario. Imagine you have an "Items" table from a small database. Most of your transformations are handled within a Dataflow using Power Query Online to prepare that table for easy use within various semantic models.

In Dataflows, you can opt for removing columns. This allows new columns "added" or made available at the source, to appear without needing to update the Dataflow manually, ensuring flexibility and reducing maintenance.

In semantic Models, you can opt for selecting columns early by choosing only necessary columns from the Dataflow "Items" table for that specific semantic model in development. This locks or limits columns, preventing unneeded columns from bloating the model.

To enhance reliability, make sure to pass an argument for the optional MissingField parameter, this enables you to specify how absent fields should be handled. For example:

• In Dataflows, use MissingField.Ignore to avoid errors when removing non-existent columns.
• In Models, use MissingField.UseNull to maintain a table's structure and allow investigation or reporting of completely empty columns.

Choosing the right method ensures your queries are more robust and reliable, while your models stay lean and manageable.

Learn more about the MissingField.Type

Take a look at straightforward solutions to top business intellignece (BI) planning challenges with tips on forecasting efficiency and strategic decision-making. The examples shared in this article hope to inspire creative thinking and problem-solving.

I recently came across a challenging task involving a Power BI semantic model with over 500 measures. Many of these measures contained hardcoded filter predicates. The data engineer had updated data in the database, which meant these hardcoded values were no longer valid. My job was to update all the affected measures with new values provided by the data engineer.

The problem was, I didn't know which measures needed updating, and I had just one day to complete the task. Despite my limited knowledge of this semantic model, I managed to complete the task on time using Power BI external tools. In this blog, I will explain the step-by-step process I followed to achieve this.