Features of Excel to Use in Data Analysis

Data analysis is the process of organizing, evaluating, and manipulating data so it can be used to inform strategic decision-making. Since its release in 1985, Microsoft Excel has become one of the most widely used data analysis software applications in the United States. Countless organizations use Excel for data analysis and computing, from finance and accounting firms to healthcare organizations and retail stores. 

Data analysts routinely use Microsoft Excel to organize, interpret, and evaluate data. The program includes numerous tools and features crucial to the data analysis process. Pivot tables transform chaotic, overwhelming datasets into useful, structured information while pivot charts allow you to visualize data using line graphs, bar charts, area charts, and other graphical representations. Conditional formatting is another tool for organizing data into easy-to-understand, eye-catching visuals. If you want to display the need-to-know information succinctly and beautifully, you can use an Excel feature called dashboards to do so.    

Functions such as MATCH, XLOOKUP, and RANK constitute the foundation of most Excel data analysis capabilities. Using functions, you can search for and retrieve data, detect errors, organize information, perform complex mathematical operations, and much more. 

Excel is also useful for regression analysis, a technique for understanding the relationship between dependent and independent variables and predicting the future relationship between the variables. You’ll need to enable the Analysis ToolPak add-in to perform regression analysis in Excel. The Analysis ToolPak provides advanced data analysis tools for performing a variety of sophisticated calculations.   

Pivot tables and pivot charts allow you to organize large quantities of data in meaningful ways, identify patterns and trends, and answer questions about the data. Data analysts often use these powerful tools to analyze income and expenses and understand how resources are allocated in a business. For example, pivot tables can be used to find the best-selling items at a store or understand how much of the company’s marketing budget is spent on print advertisements. Finance data analysts might use pivot tables and pivot charts to create financial reports that help a financial institution make strategic investment decisions. For many data analysts, pivot tables and charts are essential to their everyday job responsibilities. 

Conditional formatting is another useful tool for data visualization and analysis. You can use conditional formatting to highlight cells that meet certain conditions, allowing you to spot differences and patterns in data with a quick glance.  For example, in an expense sheet, you could use conditional formatting to display over-budget expenses in red text or bold format. 

Functions are at the heart of Microsoft Excel. They allow you to apply predefined formulas, perform complex calculations, sort information, and extract meaning from large datasets. While many data analysts and data scientists use Excel on a daily basis, the spreadsheet application also has countless uses in other contexts. 

Some of the most frequently used Excel functions include the following:

• XLOOKUP. As the name states, the XLOOKUP function finds, or “looks up,” values in a specified range. This function (which was previously called VLOOKUP) can be used to look for exact matches as well as partial matches. For example, a data analyst working in the healthcare industry may use XLOOKUP to find specific medical services in a large database of medical records. 
• IFERROR. The IFERROR function allows you to identify errors in an Excel spreadsheet and replace the errors with a specified value, message, or formula. Errors often occur because the values provided in a dataset lead to impossible mathematical equations such as finding the square root of a negative number or subtracting a larger number from a smaller number and getting a positive result. IFERROR can be used to identify and replace these types of errors so you’ve got clean data to work with.  
• MATCH. The MATCH function searches cells and finds the location of a value in a row or column. For example, a manager conducting an analysis of how employees spend their time may sort projects in order of how many hours employees spend on the projects. The manager can then use the MATCH function to search for a specific project and determine where it ranks on this list.  
• COUNTBLANK. The COUNTBLANK function does exactly what the name suggests: it counts the number of blank cells in a range. This function is useful for finding missing information in a dataset and avoiding errors caused by missing values. 
• DAYS. The DAYS function calculates the number of days between two dates. For example, a marketing professional may wish to use the DAYS function to determine the total number of days an advertising campaign will run. 
• NETWORKDAYS. Like the DAYS function, the NETWORKDAYS function calculates the number of days between two dates. However, NETWORKDAYS excludes weekends and specified holidays from the total. This function can be used to calculate the number of workdays before an important deadline, determine the total number of workdays in a fiscal quarter, and much more.
• RANK. RANK determines the relative position of a value in a list of values. Suppose a professional in the manufacturing industry creates a spreadsheet showing the cost of raw materials. The RANK function can be used to determine how expensive each material is compared to the other materials on the list. The RANK function assigns the most expensive material a value of one, the second-most expensive material  a value of two, and so on.  
• SUMPRODUCT. The SUMPRODUCT function shows the sum of the products of corresponding ranges. For example, a shop owner might use SUMPRODUCT to determine total sales in a given time period. 
• VSTACK and HSTACK. The VSTACK formula allows you to stack multiple columns of data into one column while the HSTACK function lets you combine data ranges horizontally. This is a useful tool for consolidating and simplifying large quantities of data. 

Big data helps businesses make decisions, solve problems, predict future outcomes, develop best practices, and much more. However, when the data contains errors, the solutions, decisions, and predictions resulting from the data may do more harm than good. Data cleaning is the process of finding and fixing errors within a dataset. Data scrubbing specifically refers to removing duplicate, redundant, or unneeded data from a dataset.

Microsoft Excel is frequently used for data cleaning and data scrubbing during data analysis. For example, the “Remove Duplicates” feature in Excel can be used to remove duplicate values from a specific column or cell range. This saves you the tedious process of reviewing and deleting duplicate values manually. Most importantly, removing duplicates ensures that you’re working with clean data and helps you avoid making inaccurate conclusions based on flaws within the data.    

Excel spreadsheets are often complex systems of interconnected formulas and functions. An error in a single cell can throw off dozens of calculations. Consequently, finding and fixing errors is a crucial component of developing useful Excel spreadsheets. 

As mentioned previously, IFERROR can analyze data in a worksheet to find errors and replace the errors with specified values. For example, suppose a store owner is using Excel to display the number of items sold divided by the number of items remaining in stock. If the owner is out of a particular item and the stock is listed as zero, this will create a situation in which a real number is divided by zero—a mathematical impossibility. Instead of displaying an error, IREFFOR can be used to display a predesignated value message instead. In this example, the store owner could set up IFERROR to display “Need to Order” in the cell showing an error. 

In Excel, the DATE and NETWORKDAYS functions calculate the number of days between two dates. The potential uses for these functions are nearly limitless. HR professionals may use these functions to determine an employee’s eligibility for employment benefits or paid time off. Project managers might use the NETWORKDAYS function to schedule tasks and milestones. Sales professionals can use the DATE and NETWORKDAYS functions to calculate interest on payments. 

Microsoft Excel is one of the most widely used software applications in the country. With Excel, you can organize and analyze data, perform calculations, identify errors, visualize information, and even make predictions about changing variables. Developing Microsoft Excel skills is crucial for the data analysis field, but Excel knowledge can also open doors to careers in computer programming, web development, cybersecurity and information assurance, and other information technology professions. 

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