Analyst in Action

Heavy dashboards and a bad data model is a nightmare for every BI Developer. Heavy dashboards can be slow due to multiple reasons. It is always advised to stick with best practices. Are you still figuring out about those best practices then you should definitely have a quick read on Best Practice Analyser ( link ). One of the most common issues with slow dashboards is unused columns and unused measures.  It is very normal to load some extra columns and create some test measures in your dashboard but as a part of cleanup process those unused columns and unused measures should be removed. Why we are removing them? Because if you keep them then ultimately it will increase the size of your data model which is not a good practice.  How to identify the culprits (unused columns and unused measures)? In today's blog we will provide you with 2 most common external tools which will help you in identifying the culprits. More external tools😒. Who's going to pay for this? To your surprise

Do you want to save tons of efforts to check if your data model and PBIX file follows the standard best practices and norms? Then this blog is for you. If you are a follower of our channel we already deep dive into the importance of the DAX Studio as an external tool. If you are a beginner I would highly recommend to visit this blog . In today's blog we will check how Tabular Editor can help to optimize the data model.  Best Practice Analyser allows to define or import best practices. It will make sure that we do not violate the best practices while developing a dashboard. Isn't it exciting!! Before we start make sure you already have Tabular Editor version 2.24.1 installed on your system. To install it do visit this link and select the link for windows installer. Once Tabular Editor is installed it will reflect in your PBIX file under external tool. Also, we need to define the standard rules. To do so in your advanced scripting or C# script copy this and save it via Ctrl+S. An

Happy Friday!! If you have worked with other BI tools such as Tableau setting up the Top 10 is relatively easy but in Power BI you have dedicated functions such as RankX and Top N to do this. In this blog we will see how to highlight and filter out the TopN Sub Categories based on the Sales recorded in 2013. Before we start. Here are a few pre-requisites we are using Sample Superstore data and we have created an explicit measures called "Total Sales". If you aren't familiar with implicit and explicit measures do read this article . Total Sales is equivalent to Sum of Sales.  We will start with RankX. The goal here is to highlight the Top 10 subcategories based on the Total Sales occurred in the year 2013. Refer to the image below. How to make this? We will start creating a basic DAX which will provide rank to different sub categories. Make sure you provide an order in the DAX itself. In this case we have provided "DESC- descending". Once you have the DAX ready p

Blanks in the dashboards is one of the pain and impacts the user experience. It can occur due to multiple reasons - due to data unavailability which is something is out of control of Power BI developer. One of the best practices suggest to avoid blanks with different approaches in Power BI. In this blog, we will showcase the most common approaches to tackle the blanks. This blog is mainly divided in two parts - dealing with blanks in the KPI cards and dealing with blanks in table or matrix.  Let's start from the KPI cards with the updates dealing with blanks in the cards became very easy. In the new cards it is now possible to provide a separate value when a blank appears in the card. This feature is available under the formatting option of new KPI cards. Do keep in mind this feature is currently available for the new cards.  In the above example we are showcasing the total sales and total sales last year in the reference. Under the highlighted section we can provide the value that

Another week another blog!! This week we will be focusing on the optimising the bad performing measures and how to tackle the measures generating Call back ID. Before starting, this blog is intended for Users who are a bit familiar with DAX Studio if not then you can do a quick read on our most read blog on our website ( link ). Performance of report is a great factor in improving in the user experience and to check how well your visuals and DAX formulas are performing you can utilise the benefits of Performance Analyser. In my experience Performance analyser along with the DAX Studio is a match made in heaven. You can copy the query from Performance analyser and evaluate in DAX Studio. To know more about how performance analyser works do visit this interesting article ( link ). Let's get our hands dirty and see how you can evaluate your DAX formulas. To do so I have created a basic DAX with help of Filter function  Now, we have included this measure in a table along with the year