How to Analyze Data with Excel?

In the world of technology, data is how machines communicate. It’s a language of numbers and metrics; a simple, yet hauntingly complicated system that gives us non-machines many headaches.

Microsoft Excel helps us break this language barrier and turn raw numbers into ideas, trends and insights.

Analyzing data with Excel is also useful when you need to transform your data into charts and other visuals. These are invaluable tools that help you see the story behind the numbers and highlight your best assets and most significant shifts in reports to clients and stakeholders.

When it comes to creating effective charts for your data, seeing is believing. Very few managers have the time to analyze data with Excel by hand. Charts make the insights jump off the screen.

This discussion will look at how analysts, researchers, and managers can use Excel to transform their data into helpful charts and stunning visuals.  It is the most effective way to detect trends, patterns, outliers, and other notable events in your data sets. In this blog you will learn:

Table of Contents:

1. What is Analysis in Excel?
2. Why Analysis in Excel is Important?
3. How to Analyze Data with Excel?
4. Analysis in Excel: Best Practices
5. Wrap Up

What is Analysis in Excel?

Data analysis with Excel involves using Microsoft Excel, a powerful spreadsheet tool, to examine, interpret, and derive insights from datasets. It encompasses a range of techniques, from basic calculations and formulas to advanced statistical analysis and visualization. Excel provides various functions, pivot tables, charts, and tools that allow users to organize data, perform calculations, identify patterns, and create visual representations of information. Through sorting, filtering, and summarizing data, Excel enables users to uncover trends, correlations, and outliers, aiding in informed decision-making across industries such as finance, marketing, science, and more. Its user-friendly interface makes it accessible for beginners while offering extensive capabilities for professionals in data analysis and manipulation.

Why Analysis in Excel is Important?

Data analysis holds significant importance within Excel due to its capacity to transform raw data into meaningful insights. Excel serves as a versatile platform that allows users to handle, manipulate, and analyze data efficiently. By employing various functions, formulas, and tools within Excel, individuals can organize vast datasets, perform calculations, and visualize information through charts and graphs. This analysis is crucial across industries as it aids in identifying patterns, trends, and correlations within data, enabling informed decision-making. Whether it’s in finance, marketing, science, or any other field, Excel’s data analysis capabilities empower users to extract valuable insights, derive conclusions, and present findings effectively, thus contributing to improved strategies, problem-solving, and overall business or research outcomes.

How to Analyze Data with Excel?

Excel has a lot of benefits to business owners and marketers. It is a convenient tool for collecting and organizing data. However, it does have its limitations. One of these deficiencies is in charting. Excel only offers a small handful of charts for marketers to use.

As we’ve covered, being able to display data as a chart is extremely valuable. It can save you hours when it comes to detecting insights and understanding the story behind the raw numbers.

So how do you get the most out of your data with this lack of charting options? With the ChartExpo add-in for Excel, you can gain new functionality from Excel.

ChartExpo adds over 80 different Excel visualizations. Many of these charts are designed specifically for digital marketers and PPC advertisers.

Manually analyzing data requires a lot of surfing through spreadsheets. You may even need to have some coding abilities. ChartExpo for Excel makes it simple to gain insights from even a complex spreadsheet.

Here’s how you can create a visualization of the data with ChartExpo and glean insights.

1. • To get started with ChartExpo, install ChartExpo in Excel.
   • Navigate to the INSERT menu and select My Apps.

1. • Select ChartExpo and click the “Insert” button.

1. • Once it loads, choose the “Likert Scale Chart” from the charts list.

1. • Select the data from the sheet and click the “Create Chart From Selection” button.

1. • ChartExpo will generate the visualization below for you.

1. • Click the “Edit Chart” button to modify your chart.
   • Click the pencil icon next to Chart Header to add a title.
   • This will open the properties dialog. Add a heading in Line 1 under the Text section and enable the Show option. Then click the “Apply” button.

1. • Let’s say you want to add text responses instead of numbers against every emoji.
   • Click the pencil icon next to the respective emoji, expand the “Label” properties, and write the required text. After that, click the “Apply All” button.

1. • Your final chart will appear as below.

Remember, if you face any issues with ChartExpo library in Excel 2013, make sure to install office service pack 1 on Windows.

Navigating through data can feel like a nightmare. It can be quite tricky to explore and process data when dealing with large chunks of it all at once.

After all, not all data is useful or relevant. To make matters worse, data in its raw form can often cause more confusion than clarity.

Before you can derive any sort of actionable intelligence from your data, it needs to be gathered, filtered, cleaned, visualized, analyzed, and reported. All of these steps form the data analysis process.

The data analysis process can be different each time you perform it. Unique obstacles and challenges can arise that make conclusions difficult to obtain. Thus, it is best when you have dynamic solutions to handle all of the unexpected bumps in the road.

Excel’s robust toolset offers an excellent kick-start to the process. Not only is it a convenient way to collect, arrange and organize data, but you can also perform complex computations and visualize the data with some basic charting options.

How to analyze data in Excel? From your spreadsheet data, you may even be able to glean some basic insights. In other words, you can perform some rudimentary analysis right from your spreadsheets, before charting or digging deeper into the numbers.

Analysis in Excel: Best Practices

To extract the most value from your data, you need to consider how data analysis actually works. Here is an overview of the step-by-step process to analyze data with Excel.

1. Specifying Data Requirements

To perform effective data analysis, you need to set the requirements of the data. This means establishing the structure and categories of the data that will be pertinent to your analysis.

For example, if you analyze your marketing audience, your data requirements may include things like their age, income, location, etc. These requirements will dictate what data needs to be collected.

2. Data Collection

Once you have defined the variables and organized them into categories, you need to collect all of the relevant data related to these areas. Your data needs to be complete and as accurate as possible.

Ultimately, it is up to you to source your data correctly and ensure its relevance, quality and accuracy. The information still needs to be filtered and cleaned.

3. Data Processing

After you collect the raw data, you need to organize it for further analysis. You must structure the data into proper categories. This is the stage when you need to enter the data into a spreadsheet or develop some type of data model to arrange the information logically.

Organizing the data in this fashion makes it possible to filter and clean the data.

4. Data Cleaning

The data you have organized may look nice and neat, but it is likely incomplete and may contain errors or duplicate items. Data cleaning is the process of reviewing your collected data and fixing any errors or inaccuracies you may find.

The cleaning process will depend on the type of data you’ve gathered. For example, if it is financial data, you can simply sum up totals and make sure they match your records. This is a pre-review step that is important for establishing the truth and reliability of your data points.

5. Analysis

Once your data passes through all of the phases above, it is ready for analysis.

You can manually perform the data analysis process by physically examining each row and column of data and comparing the totals and recognizing patterns and other correlations. If your data set is extensive, this is extremely challenging and can border on the impossible.

That is where data visualization tools come into play. By charting the data, you can visually see the patterns, outliers, trends, etc. It is an extremely quick method for gaining a deeper understanding of your data.

6. Communication

While the analysis of the data may seem like the final step, you still need to be able to share and communicate your findings. You may need to report your data insights to stakeholders, clients, team members or other parties.

Whoever is reading your data needs to be able to reach and understand the same conclusions. Sometimes, the data may be too complex and difficult to explain without the help of charts and other tools to simplify the information and communicate the findings effectively.

FAQs:

Is Excel the best tool for analysis?

Excel offers versatility and familiarity, making it a popular choice. However, for complex analyses, specialized tools might be more suitable.

What are the common errors encountered during analysis in Excel?

Errors like inaccurate formulas, incomplete data, or improper data formatting are common and can impact analysis accuracy.

How do you analyze large data in Excel?

You can use a quick analysis tool in Excel for larger data sets but if you convert your data to awesome visualization, it will become much easier to analyze large datasets. ChartExpo library has a wonderful visualization collection for almost all types of large dataset analysis.

Wrap Up:

Excel remains a cornerstone for data analysis, catering to a wide spectrum of analytical needs. Continuous exploration and adaptation to Excel’s evolving functionalities foster an environment conducive to insightful data analysis.