Analyst in Action

In today's blog, we will take a deep dive into the basic functionality of Power BI. We will talk about the types of filters available in the Power BI whether in the query editor or at the visualization level. Let's start with the purpose of using filters that is limiting the rows on the basis of a condition or restrict the data which you don't want to showcase in your visualization. There are many types of filters available in Power BI. We will start with the filters available in the query editor. I am considering the sample superstore data. So, I have imported the Orders and Returns table and we will be applying the filter on the order date. When we select the date filters it will lead to a pop-up where you can select basic and advanced filtering currently I am selecting the advanced version. The filter will limit the data for a particular duration as you can see in the image below. You can remove the filter just by selecting the clear filter. Just to check the code of the

You must be aware of the purpose and significance of Vlookup in Excel. But when it comes to Microsoft Power BI there is no Vlookup in it. Power BI provides you Related and Lookupvalue which is quite similar to Vlookup in Excel. If you aren't familiar with the Vlookup kindly refer to our blog . Let's get started with the purpose of both functions. You will be shocked to know that both of them will give you the same result. Because it follows the same principle of Vlookup i.e. searching for a particular value in a column and returns a value from a different column (different table). In this blog, we will showcase how and when to use related and lookup values. We will be using Sample Superstore data. The question that comes to my mind is when to use the related functions? So there are certain criteria to be met before creating a column with related. One of the conditions is that both the tables (one where we are creating a column and the other will be from where the value will com

 Are you bored of using a basic Pie chart in your visualization? Then this blog is for you. In today's blog, we will discuss step-by-step procedures to create a donut chart. Along with the donut chart, there is a much more so stick around. Let's start with a basic donut chart which proves to be the best alternative to the pie chart. A donut chart is a type of Pie chart where the center is carved out so that you display text in the carved-out space.  Pro Tip -: Donut chart can be used to display your KPIs and you can use it to display ratios and percentages. But if you are trying to visualize more than 5 categories try to avoid the donut chart because it will hamper the quality of visualization. The stakeholders expect to see clear visualization where the graphs tell their story by themselves. ( For more pro tips do follow our blog   ) So let's get started!!!! To create the donut chart we are using the sample superstore data and will highlight all the steps from scratch.  To

Are you spending a big chunk of your time cleaning the data in excel? If yes then this blog is for you. All the cleaning and transforming of data can be achieved in Microsoft Power BI. Power BI offers a wide variety of options to clean your data. Power BI comes with a power query editor where you can play around with the data and I consider it as an ETL tool where you can transform your data according to the needs. We have discussed the data cleaning process in the last blog and in today's blog the focus will be on transforming data and the features that the power query editor offers. These steps are solely subjective according to the data type and to your needs. I am highlighting 6 steps to transforming the data. I am working on the sample superstore data. So let's get started!!!! Removing columns and rows-  We are aware that the datasets can have a lot of unwanted columns and rows. To get better insights you need to get rid of it. To do so you need to select the column. If y

Let's consider a scenario where you get the unstructured data and you need to derive the insights using it. This sounds so baffling to me and considering it for data-driven decision-making is one of the mistakes. This scenario can be intervened by a basic step i.e. data cleaning. As the name suggests it is the prior step you need to take before deriving insights and creating visualizations out of it.  In other words, it is a prerequisite for data visualization. In my novice experience, I have handled data that arrived from a variety of sources also entered manually which can escalate the chances of getting duplicate values and wrong values which is needed to be removed before taking it further. All these steps may include eliminating some values or replacing some data so that the data is befitted for further visualization. In today's blog, I will take into account 6 basic steps which prove out to be useful for me in every data cleaning step. All these steps are quite extensivel