Everybody who works with Power BI and page tooltips knows that, after period of time the report can be overwhelmed by too many pages and it could be quite a chaos.

In this article I am showing easy an alternative way how to lower number of tooltip pages to minimum and in some cases to only ONE single page!

Many accountants consider cash flow statements to be the most important indicator of a company’s performance, as they do not rely on estimates or judgments by the preparers.

While indirect method cash flow statements can be easily prepared from the P&L and the opening and closing balance sheets, they do not provide much insight into where the money is coming from or going to. Because of this, users of financial statements and accounting standard setters alike (e.g., FASB and IASB) have always preferred the direct method over the indirect method for cash flow statements.

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.

While headcount analysis seems a conceptually straightforward topic, to do it flexibly, for example, by preparing a dynamic list of employees at any given point in time, contains some technically challenging aspects. 

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.

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.

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

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.

Curious about PBIP folder structure in Power BI? Dive into the Mahabarata-inspired story on Power BI Developer Mode!

Buidl your own Real Time Intelligence Demo with Sample Data in Microsoft Fabric.