Analyst in Action

What if I told you there's no need to write laborious DAX expressions in Power BI? Doesn't that sounds great? I personally enjoy writing the DAX expressions from scratch because it helps to get to know the data well and you can tailor your calculations. But if you don't like writing the DAX expressions from square 1 then you should consider "Quick Measures". Power BI always makes your task easy in visualizing your data and quick measures are something that helps you to create measures in few seconds. Doesn't that sound amazing? Yeah!! As the name suggests creating measures just by dragging and dropping is an accentuating feature of quick measures. I consider it to be the future of measures in Power BI. In this blog, I will showcase step by step process to create quick measures. I will be working on the Sample Superstore data. You can find the quick measures right next to the new measure on the top or else you can right-click any of the available table options

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

Source You must be aware of various customer segmentation techniques and analyses. Let's discuss one of the most important analyses today i.e. RFM Segmentation. What is this 3 letter term can do? The answer is it can do wonders let's get to know more about it. RFM analyses are the method of separating your customers based on RFM scores which will help you in identifying your best customers and lost customers. RFM is an acronym for Recency, Frequency, and Monetary. The main idea behind this analysis is to understand your customers well because it will separate your customers based on how recently your customer made any purchase, how often they are buying, and what is the average cost of their orders. RFM analyses can be performed in various analytical and programming tools such as Python, R, and Excel. I have done it in Excel on sample data. How to do it in Excel?   Select the right data and insert a pivot table and select the pivot table field accordingly in my case I have se

Source Does excel qualify as a Business Intelligence tool? Excel is a spreadsheet tool but it offers features such as graphs, charts, pivot tables, and a programming language called Visual Basics for Application (VBA). What kind of tools are considered BI tools? The primitive answer is the tools that help in collecting data and analyzing it to help in informed decision making. According to this excel qualify as a BI tool. But when you compare excel and other BI tools such as Microsoft Power BI it offers much more than excel. But professionals will argue that excel is always a go-to tool for analysis. If you compare a BI dashboard against the excel final reports you can easily gaze that the latter one is not that intuitive in nature which is a key feature that BI tools offer. Other than that excel reports can do the same work but in today's world, it looks obsolete in nature. BI dashboard is much more interactive in nature. Power BI sample dashboard Sample excel report As a user mys

  Source Today, every electronic device is a source point to some sort of data. In the era of big data, you need certain tools to uncover the story behind those mere numbers. Business Intelligence and analytical tools make our life easier in devising actionable insights. When I started my analyst journey I was introduced to R Programming which comes under the umbrella of data analysis and later on I shifted to Tableau and SQL. I have worked more with BI but I won't let BA loose in this data battle. This blog mainly highlights the difference between Business Analytical tools and Business Intelligence tools. Both of them perform in a similar manner but they are different in functionality. Business Intelligence (BI) is a much wider category which includes Business Analytics (BA) and Data Analytics but overall all of them are data management systems. The very basic difference between them is BI focuses on descriptive analytics while BA focuses on predictive analytics. Descriptive analy