Who doesn’t know about Microsoft Excel! It’s a robust tool everyone uses, from beginners to professionals and even business analysts. Yes, you’ve read it correctly! Business Analysts heavily leverage Microsoft Excel in their day-to-day lives. Do you know? Business analysts receive proper training in Microsoft Excel during their preparation phases. Now, you can understand the importance of ‘sheets’ in business analysis.
Are you planning to become a business analyst? It’s best to have a sound knowledge of Microsoft Excel from the beginning of your career. But, how do business analysts use Microsoft Excel? Or, more precisely, how to use Excel for business analysis? You will get answers to these questions and more right below.
This article is all about Microsoft Excel for business analysis. It discusses the use and importance of Microsoft Excel in the lives of business analysts. You will have a sound knowledge of how you can implement the robust tool to analyze business data and draw meaningful conclusions. Let’s dive into using Excel for business analysis!
An introduction to Excel for business analysis
While larger organizations have moved away from spreadsheets on a massive data scale, spreadsheets are still utilized daily. Excel maintains data points in each cell in its most basic form. Raw data exports, sales dates, SKUs, and units sold are all imported (or entered) into a spreadsheet for better viewing and categorization.
A successful Excel spreadsheet will organize raw data into a digestible format, making actionable insights easier to extract. Excel allows you to modify fields and functions that perform computations for you when working with more complex data. Segmented data may be examined and shown without additional software, even with more extensive data sets. Calculate hypothetical profit margins or budgets for departments. While it won’t be able to create a full-fledged data product on its own, it can provide easy-to-understand graphics and precise calculations.
You want to be an analyst or need to work with data to create a report, but analytics isn’t the most straightforward concept to understand in one sitting. Using data spreadsheeting is a microcosmic form of a more significant data endeavor.
- What is the objective? Overview? What information do you require?
- What is the source of the data? What exports and imports are needed?
- Is it necessary to translate the data?
- What are the potential stumbling blocks? Limitations?
- How do you arrive at your conclusions? What decisions must be made following the analysis?
A Big Data project necessitates far more people, skills, and attention to detail, but Excel is a terrific place to start.
Using Microsoft Excel for Business Analysis
Businesses involve massive datasets. Managing and analyzing them using simple tools is complex. However, Microsoft Excel resolves the challenge optimally. Business analysts use Microsoft Excel in the following ways.
- Pivot Tables
With a simple drag and drop and no equations, pivot tables are a powerful tool for interactively and quickly grouping, aggregating, filtering, and visualizing data. They are simple to modify to meet the needs of the customer. Since pivoting brings attention to critical data, it is essential for data analysis. When new data is appended to the Excel pivot table you’re looking at, you can hit the refresh button to update all values.
- Analyze Data
This feature was previously known as Ideas. It allows you to ask questions in English and interpret the data using a natural query language. You don’t have to waste time creating complicated formulas. The most appropriate chart is used to present visualizations for the query you ask.
‘Analyze Data’ is also available under the Home tab. As a result, Excel will either suggest questions or allow you to fill in your own.
- Analysis Toolpak
It is, for example, a free add-on that you can use to undertake extensive statistical or engineering analysis. Consequently, all you have to do is give the correct range of data to which you can apply the function. So, you don’t need to be familiar with the underlying equations. Excel will also choose the necessary features and present the findings in tabular and graphical formats.
The Toolpak must be loaded and activated via File -> Options -> Manage -> Excel Add-ins.
Correlation, descriptive statistics, histogram, smoothing, rank, moving average, regression, and more functions are available.
- Power Query (Get & Transform)
It’s an Excel add-in you can use to identify, cleanse, alter, and combine data from many sources. It prepares
information for later examination.
For analysis, you can sort data across multiple columns. It is one of the most looked-upon functionalities of Microsoft Excel among business analysts. They heavily rely on the sorting option to get clarity in the dataset.
A filter allows you to choose data and displays the unique values and any blank values.
- Conditional Formatting
Depending on the cell’s value, you can visualize data using data bars, color changes, and icon sets. It’s known as conditional formatting.
You can, for example, use pre-built conditions or develop your own. As a result, conditional formatting can be applied to a specific set of cells, Excel, or pivot tables.
- Remove Duplicates
In Data -> Remove Duplicates, we may pick which columns we want to check for duplicate values and which column names we want to examine for redundant values. It is a necessary step in the data cleansing process.
Excel makes creating data visualizations a cakewalk. With a chart, analyzing rows and rows of data is a lot easier!
For your analysis, Excel provides a choice of graphs.
With headers and alternate coloring or banding of rows, Excel tables also aid in the systematic management and analysis of datasets. Data analysis is more straightforward since they have structured references and dynamic ranges. As a result, you can transform data in Excel into a table by simply pressing Ctrl + T and clicking any cell in the data.
These were the few use cases of Microsoft Excel in business analysis. It’s high time to use Microsoft Excel in your projects and derive more value from a given dataset without any difficulties.
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