5 Briefed Excel steps to engage audience with visual: Data Analysis Guide

One of the most used programs for data analysis is Microsoft Excel. They unquestionably doubt the much more sought-after analytical tool available since they include built-in pivot tables. You can simply import, browse, clean, analyze, and visualize your data using this all-in-one data management tool. We will talk about numerous Excel data analysis techniques in this article. 
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Table of Content:
Advantages Of Data Visualization Techniques & Skills:
1) Increases engagement:
2) Accessible data: 
3) Save time:
Visual Methods:
1) Charts:
2) Heat Map:
3) Highlight Table:
4) Word Cloud:
5) Correlation Matrix:
Other options for data visualization:

Advantages Of Data Visualization Techniques & Skills:

By integrating every team member and employee in the research process, employing graphics to support the analytical strategy can greatly increase your company's return on investment and set it apart from rivals. This is made feasible by the user-friendly design of contemporary online data analysis tools, which enable the typical user to use data in the form of interactive charts and graphs without the need for technical expertise. Let's examine a few advantages data visualization expertise can offer a company.

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1) Increases engagement:
Since businesses & analytics came together, creating reports has been a laborious and time-consuming activity. Static presentations full of figures and text are not only quickly out of the current, but they're also more difficult for non-technical people to comprehend. Since they may not comprehend the aims of the organization, how can you urge your staff to work toward them? Intuitive dashboards built on data visualizations may keep audiences interested while also enhancing the analytical process's dynamism and understandability.

2) Accessible data: 
Building on the accessibility concept, consider an employee who has never before worked with data; trying to draw meaningful inferences from a spreadsheet's collection of numbers can be excruciatingly difficult. They are relieved of that load by data visualizations, which make it simple to get pertinent performance insights. Employees can identify possibilities for improvement in real-time and incorporate them into their strategy by glancing at the good graphs and charts.
How to Use Excel Charts for Data Analysis

3) Save time:
They save time because you almost certainly work with raw data from numerous sources, regardless of the size of your firm. Dealing with this original data as it is can be difficult for numerous reasons, one of which is the time required to evaluate and draw conclusions from it. a period of time that could have been used for other crucial organizational or operational responsibilities. This is not a problem with the correct data visualization tools and methods because you can instantly visualize key performance indicators in beautiful charts in a matter of seconds.

Visual Methods:

1) Charts:
Charts are a fantastic tool for presenting a narrative with graphics. Data sets are summarised to make them simpler to understand and evaluate. Excel is renowned for its capacity to manage and compute numerical data. Any set of information can be graphically represented in a chart. A chart is a graphic representation of data that employs symbols to represent the data, including bars in a bar chart or lines in a line chart. Excel has several different chart types available for you to choose from, or you can use the Excel Suggested Charts feature to look at charts specifically made for your data and choose one of those.

2) Heat Map:
A heat map is a style of visualization that uses color to highlight contrasts in data. These graphs make it simple for the observer to rapidly spot trends by using color to communicate values. For a user to correctly interpret and read a heatmap, a clear legend is required. Heat maps have a wide range of potential uses. For instance, you can use a heat map with the day of the week as the vertical axis and the time of year on the horizontal axis to determine when the time of day a retail business generates the greatest sales. Then, you may spot patterns in the data that enable you to pinpoint the precise times your store enjoys the highest sales by filling in the matrix with colors that correlate to the volume of sales at each hour of the day.

3) Highlight Table:
A highlight table provides a more interesting option than a standard table. You can make it simpler for users to rapidly see trends & patterns in the information by coloring-coding the table's cells. Comparing categorical data is a good application of these representations. You might be able to apply conditional formatting rules to a table that cause cells that satisfy certain circumstances to be automatically colored, depending on the data visualization tool you're using. For instance, if you're employing a highlight table to visualize sales statistics for a corporation, you might color the cells red if they fell short of the target or green if they exceeded it. The colors in a highlight table, as opposed to a heat map, are discrete and each reflects a single value or meaning.

4) Word Cloud:
A word cloud, also known as a tag cloud, is a graphic display of text data where the size of each word corresponds to its frequency. A word's size increases in the display in proportion to how frequently it occurs in the dataset. Depending on their frequency, words may also be bolder or have a certain color scheme in addition to changing in size. Word clouds are frequently used on blogs and websites to examine textual changes between two sources and find important keywords. Additionally, they are helpful when examining qualitative datasets, including the precise terms customers always seemed to describe a product.

5) Correlation Matrix:
A table that displays correlations between variables is called a correlation matrix. A color scale is used to convey whether and how strongly two variables are associated in each cell, which reflects the relationship between the variables. Large data sets can be summarised and patterns found using correlation matrices. In commerce, a correlation matrix may be used to examine the relationships between various product-related data points, including such price, advertising expenditure, launch date, etc.

Other options for data visualization:
While the aforementioned examples represent some of the most widely used methods, there are several other ways you can visualize data to improve your communication skills. Other choices for data visualization include:

• Bubble clouds
• Cartograms
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Treemaps
• Wedge stack graphs
• Violin plots

The feature has been renamed to Analyze Data to better convey how Ideas makes data analysis easier, quicker, and more intuitive.