Microsoft Excel is one of the most popular and accessible data management tools available today. While Artificial Intelligence (AI) is revolutionising the way we collect, manage, and analyse data at scale, Excel remains a fundamental tool for data analysis and visualisation by helping you organise and process your data into clean slices of readable information, essential for drawing out insights that can inform your research, business strategy, or any number of other data-driven projects.
Business Intelligence (BI) applications like Tableau and Microsoft Power BI focus on managing and analysing large datasets. However, Excel is keeping pace with more advanced tools by using AI to enhance entry-level data analysis and visual storytelling. Both are essential for drawing clear insights, understanding the factors that support them, and communicating them to yourself and other stakeholders. Using Excel, even beginners can manage data such as budgets, surveys, customer data, and academic results.
In this post, we’ll give a brief intro to Excel and some of its features, whether you are new to Excel or looking to upskill. We’ll also share some more reasons why it is still a valuable tool in an age of AI, and how you can learn more about it by taking a short online course in digital skills at SACAP.
Do I need Excel for data analysis?
Excel is a fundamental software tool in many businesses. As part of the Microsoft 360 package, Excel offers a simple, effective way to manage data in spreadsheets. As the software has evolved, built-in tools help organise, clean and interpret data.
If you’re wondering what Excel is used for, it all depends on what information you’re working with. Whether you’re managing numerical data, such as budgets, or qualitative data, such as customer survey responses, Excel workbooks let you import and organise data effectively.
Below, we’ll share some insight into how to analyse data in Excel step-by-step, and share some tips for managing and visualising data using this programme.
Excel basics for beginners
To provide some insight into Excel basics for beginners, the first step is to import data. One of the easiest ways to import data is to collect it in CSV format, which means the data is already delimited and categorised. However, it is also possible to import raw text data. You may need to do some processing in that case, but Excel offers numerous data formatting tools to streamline the process.
Once you have imported the data, you can organise this information into rows and columns. Rows run horizontally, and columns run vertically, and it’s critical to manage your data in a way that makes the most sense for easy reference. For example, columns can be used to group products, while rows provide information about them.
With information organised into rows and columns, you can start to make more sense of this data using basic formulas such as SUM and AVERAGE. SUM allows you to add up numerical data in a row or column, while AVERAGE helps you find the average of a series of numbers in a row or column. To make analysing data quick and efficient, understanding formulas is one of the best Excel skills to learn.
Collecting data and making sense of it is just the first step; data cleaning in Excel is another crucial step in making data easier to analyse. Some simple data cleaning tips include checking for duplicates, removing blank cells and trimming spaces between words or numbers. Formulas such as TRIM and CLEAN can remove unnecessary spaces and unreadable characters. Once you have cleaned your data, you can proceed to fundamental data analysis.
Basic data analysis tools in Excel
Excel provides many tools for data analysis, helping you identify trends in large datasets that would otherwise be time-consuming to analyse manually. There are many Excel tools for making sense of messy data, including Sort and Filter, Conditional Formatting, Pivot Tables and Excel Tables.
The Sort and Filter tool lets you create separate views of the same data by adding drop-down menus to filter data. Conditional formatting highlights data in workbooks for easy reference. For example, you can highlight numbers that exceed a certain threshold or other data that contains a specific word.
After practising managing workbook data using the tools we’ve discussed, you will likely want to take things further by creating Excel tables, which help to organise your data more efficiently. It is easier to manage large data sets in tables, which you can take further by using Pivot Tables, which allow you to analyse data more dynamically without formulas.
Data visualisation for beginners
Excel not only provides fundamental tools for organising and analysing data, but also offers many ways to tell the story behind the data in a compelling way. Your data may be sound, and your conclusions may be valid, but you still need to communicate them and present the evidence to other stakeholders before they have an impact. Learning how to visualise data and tell compelling data stories is a skill you can build on, starting with simple graphs and Excel data visualisation tools.
What graphs are best for data storytelling really depends on the information you are presenting. Overall, bar graphs can help compare data, while line graphs can show trends over time. Pie charts help to indicate percentages, and you can also use a combination of all of these, depending on the data you have collected.
Common Excel mistakes beginners make
As you learn how to manage, analyse and present data more effectively, you’ll likely make some mistakes along the way. To prevent any mishaps, however, you can take note of some of the common mistakes that beginners make when using Excel.
Some of these mistakes include deleting source data, which can be problematic when using formulas. To avoid this, always save a copy of your work. Other challenges can include learning to use formulas, which requires paying attention to spelling and other details, such as row and column references.
Presenting data also requires practice and trial and error. One of the most important tips is not to overload graphs and charts with too much information, as this can hinder efficient data analysis. In this case, it’s more effective to compartmentalise data rather than cram too much information into a single visualisation.
Why Excel still matters in the age of AI
As Forbes’ article “Why Data Analysts Remain Relevant in the Age of AI” notes, tools may grow and evolve, but learning to analyse data is a timeless skill. Therefore, while it’s possible to perform more advanced analysis with tools like Power BI or Tableau, Excel remains the most accessible tool for beginner data analysis. Therefore, it remains the starting point for people looking to build confidence in their digital skills.
If you’d like to learn more about Microsoft Excel or hone your skills, SACAP Global offers a short online course, Intro to Excel: Data Analysis & Visualisation. This course will equip you with the capabilities mentioned above, as well as provide you with basic data analysis skills that you can expand on with more advanced tools if you choose.
SACAP Global also offers online courses to further expand on your digital skills and abilities in an increasingly AI-driven world, such as Using AI Tools in Everyday Life.
FAQ:
1. Do I need to be an expert to analyse data in Excel?
Not at all — basic functions like SUM and AVERAGE can help you start making sense of numerical data. Sort and Filter, and Conditional Formatting also assist in cleaning up and organising data.
2. How does Excel compete with more advanced AI tools?
Excel enables entry-level data analysis, which remains essential for day-to-day business and data tasks. While advanced AI tools handle large datasets, Excel is valuable because it’s vital to learn how data management and analysis work and how to do it yourself, so that you are capable of doing your own work should the relevant tools be unavailable.
