What are the Top 4 Data Analysis Types and Which Tool to Use for Visualization?

Today’s world is generating immense amounts of data compared to two decades ago.

Undoubtedly, it requires adequate and effective data analysis types, methods, techniques, and tools that can respond to constantly increasing business research needs.

To analyze data faster and inexpensively, you need the best tools you can find in the market. We understand Excel is one of the go-to charts for business owners and professionals. But the spreadsheet application generates very basic charts, which can slow down your momentum.

Well, you don’t have to ditch your Excel for other expensive tools. In fact, you can supercharge it by installing a particular third-party application called add-in.

Yes, you read that right.

In this blog, you’ll learn:

• The four key data analysis types.
• Major uses of data analysis in the business world.
• Data analysis importance in today’s business environment.

Types of Data Analysis With Examples

Before we delve right into the heart of the blog, let’s explore the main roles of a data analyst.

What is the Role of a Data Analyst?

Check out the day-to-day tasks of a data analyst below.

• As a data analyst, one of your tasks is to objectively extract answers from data.
• The other key task of data analysts is to identify the important (and often mission-critical) trends.
• You’ll also be required to investigate raw data for growth-driven insights.
• Also, you have to distill insights from noise to create compelling data stories for decision-making purposes.
• Lastly, the crucial role of data analysts to provide businesses with an edge over competition using data.

Why is Data Analysis Important?

Data analysis is an internal organizational function you can perform and present key findings to the stakeholders, such as top management and investors.

The process requires a much more in-depth approach to recording, analyzing, and dissecting data for easily-digestible insights.

With solid data analysis skills, you can help your business or workplace to:

• Predict future trends and behaviors of key data points.
• Make informed and reliable decisions.
• Drive productivity and growth by minimizing costs and other risks.

4 Data Analysis Types and Examples

Below are the 4 data analysis types.

1. Descriptive Analysis
2. Diagnostic Analysis
3. Predictive Analysis
4. Prescriptive Analysis

i-Descriptive Analysis

Descriptive data analysis focuses more on historical data. You can use this methodology to track key performance indicators (KPIs), such as revenue, sales, leads, and more. In descriptive analysis, it’s essential to know how frequently a certain event or response occurs.

ii-Diagnostic Analysis

The key role of diagnostic data analysis is to explain the changes in key data points. Once descriptive research shows you that something negative or positive happened, diagnostic analysis uncovers the reason behind a particular behavior.

For instance, once you’ve discovered your growth is positive, it’s the job of the methodology to help you uncover the reasons behind the sudden shift in your KPIs.

iii-Predictive Analysis

Predictive data analysis can help you forecast the future behavior of key data points. For instance, you can use the current financial year’s data to predict the next year’s expected revenue.

If revenue has been going up by 20% for the last 3 years, you can confidently predict that the next year’s revenue will be 20% higher than this year.

iv-Prescriptive Analysis

Prescriptive data analysis combines the previous 3 data analysis types to help you formulate an action plan.

What are the Uses of Data Analysis in Business?

One of the key uses of data analysis is that it can help you to identify hidden trends and patterns in your business data quickly.

Use the four data analysis types to extract answers from your data, which you can use to create compelling stories for investors.

Bulky data provides unlimited opportunities for businesses to extract actionable insights. Yes, insights could spell the difference between success and failure.

Analyzing raw data can help you uncover the hidden relationships and patterns between metrics. Exploring these patterns helps you can enable you to save immense resources, such as time.

The reason why we analyze data is to create stories. Remember, poring over numbers in spreadsheets is monotonous, especially if you’re in front of an audience. You need to create a compelling story with insights extracted from the raw data.

How to Implement Data Analytics in your Business?

Figuring out the data to collect is the first step.

Data can be derived from multiple sources, such as social media, CRM, and websites, among others.  Evaluating the accuracy and pertinence of your data is also critical. It would help if you investigated how the data was collected to flag potential errors.

Lastly, you should conceptualize your raw data for actionable insights. In other words, you should transform your vast data sets into easy-to-understand informational representations and stories.

For all the analysis types you need a data visualization tool, you can click below to install visualization library for your desire tool.

How Visualization Can Help You During Data Analysis?

Our brains can process images faster than texts and numbers.

It makes perfect sense to use charts, graphs, and maps to display insights rather than work with raw data. Visualization charts are the bridge between raw data and strategic business decisions. In other words, to power data-backed decisions, you need a solid visualization strategy.

Secondly, images without an accompanying story are less like to pique interests or even arouse the emotions of the target audiences. It would help if you had a combination of easy-to-interpret charts and an interesting narrative to persuade your audience (or readers). Data visualization is a quick and easy way to explore and display insights into raw data.

Leverage data visualization to enjoy the benefits below:

• You can easily identify the aspects of your business (or workplace) that need urgent attention or improvement.
• Charts and graphs can provide in-depth insights into the behavior of your target market.
• You can easily forecast the performance of key metrics using data visualization designs, such as Dot Plots.
• Data visualization designs, such as the Radar Chart, convey information faster than tables. Besides, they transform raw data into a digestible form.

The Best Tools to Use to Analyze Data

According to statistics released by Microsoft, Excel has over 750 million users worldwide. More so, it’s one of the go-to data visualization tools for professionals and business owners. However, Excel lacks ready-made and visually appealing charts you can use to conduct the four data analysis types.

Besides, the spreadsheet tool lacks advanced charts that you may need to create compelling data stories.

Yes, you read that right.

You can supercharge your Excel to access ready-to-use and visually-appealing charts by downloading and installing add-ins.

The add-in we recommend you install in your Excel is called ChartExpo.

So what is ChartExpo?

ChartExpo is a super user-friendly application you can easily install in your Excel. Also, the intuitive tool has over 50 other advanced charts to help you succeed in data storytelling.

How to Get Started with ChartExpo Add-in?

Complete guide is available to install ChartExpo in Excel.

Once it is installed you can find it as mentioned below:

• Go to Apps option and then click the See All button.

• Select ChartExpo add-in and click the Insert button.

• The add-in will be added to the Excel application, as shown.

Like we said, use ChartExpo to conduct the four data analysis types without wasting time, so you will be able to find all the available list of charts in front of you.

Let’s talk about the best charts in Excel you can create using ChartExpo.

Double Bar Graph Example by Using ChartExpo

Let’s visualize the tabular data below using the Double Bar Graph (one of the examples of data analysis’ charts).

Brand	No. of Cars Sold	Average Price Per Car
Toyota	336	$29,217
Honda	180	$29,899
Hyundai	264	$30,303
KIA	330	$31,605
Suzuki	306	$32,480

• Copy the table in your Excel to get started.
• Click the Insert buttonto access the My Apps option.

• Click the My Apps optionto access the ChartExpo add-in.

• Select your sheet data and click the Comparative Analysis Charts tab to access the Double Bar Graph, as shown.

• Select data and click the Create Chart from Selection buttonto visualize data, as shown above.
• Check out the final Double Bar Graph (below).

Insights

• Toyota was the best-selling car. Also, the car brand had the lowest average price.
• Although, Honda was the worst-selling car brand, it had the second-lowest average market price.
• Suzuki had the highest average price, although its sales performance was the third-highest.

FAQs:

What is data analysis?

Data analysis is an internal organizational function you can easily perform and present the key findings to the stakeholders, such as top management and investors.

The analysis process requires a much more in-depth approach to recording, analyzing, and dissecting data for easily digestible insights.

What is the importance of data collection, analysis, and interpretation in scientific research?

The purpose of data collection is to acquire useful and usable information for analysis. On the other hand, analyzing raw data can help you to uncover the hidden relationships and patterns among key data points.

Exploring these patterns can save you immense resources, such as time.

Wrap Up

Data analysis is incredibly critical in today’s business world.

Why?

It can help you cut the amount of time you spend fishing for answers in raw data tremendously. Besides, it transforms data into a form that management and other stakeholders can leverage to make strategic business decisions.

There’re four types of data analysis, namely:

• Descriptive analysis
• Prescriptive analysis
• Predictive analysis
• Diagnostic analysis

Tools you can use to make full use of the four data analysis types (mentioned above) are a must-have. Yes, you read that right.

Excel should not be your go-to tool if your goal is to conduct a comprehensive investigation using the (four) types of data analysis. And this is because the spreadsheet application lacks ready-to-use charts you can use to get started with data analysis.

There’re tools you can leverage to create insightful, visually appealing, and clear charts.

We recommend you to install a particular third-party application called ChartExpo into your Excel to access advanced charts.

ChartExpo is an add-in you can easily download and install in your Excel app.

More so, it’s loaded with over 50 more advanced charts to get you started with four data analysis types. You don’t need coding skills to visualize your data using ChartExpo.

Sign up for a 7-day free trial today to access easy-to-interpret and visually appealing charts for your data analysis process.