Data Analysis vs. Data Analytics: What’s the Difference?

There’s a common misconception that data analysis vs. data analytics means the same thing.

Are these terms for the same concept? Or, different but related terminologies.

Here’s the main distinction:

Data analytics is the broad field of using data and tools to make business decisions. Conversely, data analysis, a subset of analytics.

In this blog, we’ll address the confusion by:

• What is the Difference Between Data Analysis and Data Analytics?
• Comparison Table Between Data Analytics and Data Analysis
• How Data Analytics Improve Business Decisions?
• How to Get Insights from Data with the Help of Visualization?
• Data Analysis vs. Data Analytics: The Best Visualization Tool for All

What is the Difference Between Data Analysis and Data Analytics?

What is Data Analytics?

Analytics are all activities related to investigating data for hidden answers.

The primary goal of the data analytics field is to make it easy for other stakeholders to access and understand insights.

You’ll agree when we say raw data has no value. Instead, it’s what you do with it that provides value.

Data analytics includes all the steps you take, both human and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data.

Data analytics can help you:

• Find trends
• Uncover opportunities
• Predict opportunities and risks
• Make decisions

It’s worth noting that the techniques and processes of data analytics have been automated by emerging technologies, such as artificial intelligence (AI) algorithms.

What is Data Analysis?

Data Analysis is observing, transforming, cleaning, and modeling raw facts and figures to draw actionable insights to optimize a strategy or process.

Here’s an interesting fact.

Data analysis is limited to an already prepared dataset. In other words, it entails inspecting, arranging, and questioning the data that’s readily available.

In today’s world, we’re used to letting the visualization tools do the first round of analysis. We further augment the process by investigating and interrogating data with more contexts.

In the coming section, we’ll provide the in-depth distinction between data analysis vs. data analytics using a table. You don’t want to miss this.

Comparison Table Between Data Analytics and Data Analysis

Following table illustrates the major differences between Data Analytics and Data Analysis.

Basic	Data Analytics	Data Analysis
Form	Data analytics is a ‘general’ form of analytics that businesses use to make reliable decisions.	Data analysis is a subset of analytics that can help you to analyze ready data.
Structure	Data analytics consists of data collection and analysis.	Data analysis entails extracting insights into ready data for storytelling.
Tools	R Tableau Public Python SAS Apache Spark Excel Google Sheets ChartExpo	OpenRefine KNIME RapidMiner Google Fusion Tables Tableau Public NodeXL
Sequence	Data analytics life cycle consists: Case evaluation Data identification Data acquisition & filtering Data extraction Data validation & cleansing Data aggregation & representation Data analysis Data visualization Creating data stories	The sequences followed in data analysis are: Data cleaning Data visualization Creating data stories using insights
Usage	Data analytics can help you to find: Masked patterns Anonymous correlations Customer preferences Market trends and other necessary information that can help you make data-backed decisions.	In data analysis, you can perform the following tasks: Descriptive analysis Exploratory analysis Inferential analysis Predictive analysis for insights.

How Data Analytics Improve Business Decisions?

Data analytics is valuable for businesses to refine their marketing strategies and personalize their communication.

Data plays a significant role in both the public and private sectors. Besides, with the ever-evolving collection and analytics tools available, you can easily use the data to streamline workflows, identify fraud, and much more.

Other applications include:

• You can leverage data analytics to create a customized shopping experience for your customers by optimizing your website.
• Data analysis and data analytics are two critical areas that can help you analyze your competitors and their actions in real-time.
• You can easily leverage data to create offers that are irresistible to the target market.
• Blue-chip firms, such as Google, use data analytics to optimize their workflow and boost their workforce’s performance.
• The processes in data analysis and data analytics can help you forecast opportunities and risks before they happen.

Data is the new fuel for businesses.

If you leverage it fully, you can easily drive the growth of key metrics, such as sales revenue and net profits.

To drive growth, especially in today’s competitive landscape, you need an in-depth understanding of the difference between data analysis and data analytics.

How to Get Insights from Data with the Help of Visualization?

At the center of data analysis vs. data analytics, there’s visualization.

Visualization is the process of displaying insights into data using visual images, such as charts and graphs. More so, it makes the data more natural for our brains to interpret. You can easily uncover trends, patterns, and outliers that exist in your data using charts and graphs.

Not long ago, the ability to create smart data visualizations was a nice-to-have skill. For the most part, it benefited design and data-minded businesses.

That’s changed.

Now data visualization is a must-have skill for all businesses because it’s the only reliable way to predict (or mitigate) risks and capitalize on opportunities.

Decision-making increasingly relies on data, which comes at an overwhelming velocity, and in such gigantic volume. You can’t comprehend it without some layer of abstraction, such as charts, graphs, and maps.

Like we said earlier, a huge chunk of data is primarily noise. And this implies you’ve got to visualize to display hidden relationships and associations between variables. Visualizing complex data comes with a set of challenges that can derail the goal of being fully data-driven.

Thanks to the growing number of affordable tools, translating raw data into visuals is now easy (and inexpensive) for everyone, regardless of data skills or design skills.

Data Analysis vs. Data Analytics: The Best Visualization Tool for All

Excel is a powerful tool for data visualization because it comes with a familiar user interface (UI).

In other words, it has been there for years and has proven time and again to be amazingly reliable in storing and visualizing data.

But Excel produces pretty basic charts that require a lot of tweaking to be appealing or communicate insights fast. Besides, this spreadsheet tool lacks advanced charts that can increase your chances of success in a data analytics career.

Here’s the kicker.

You don’t have to do away with your Excel.

You can easily boost its use ability by downloading and installing third-party add-ins.

We’ve tested over 50+ data visualization add-ins available in Excel’s My Apps Store. The criteria we used to judge the viability of the add-ins are the cost of access, ease of use, and the quality of charts generated.

ChartExpo meets all our conditions.

ChartExpo does not need coding or programming skills, unlike other data visualization-based add-ons. More so, it has a super friendly interface for everyone to use. ChartExpo has over 50 ready-made charts to increase your odds of success in processes in data analysis vs. data analytics.

What is ChartExpo?

ChartExpo comes as an add-in for Excel.

Essentially, it turns your Excel into a reliable data visualization companion capable of simplifying communication and reporting to your stakeholders (clients and colleagues).

ChartExpo is cloud-hosted, which makes it extremely light. You have a 100% guarantee that your computer or Excel won’t be slowed down. Besides, you can export your charts in JPEG and PNG formats.

Use ChartExpo if your goal is to save time and access ready-to-use visualization designs highly relevant to data analysis and data analytics.

How to Get Started with ChartExpo Add-in?

To get started with one of the best tools in the data analysis vs. data analytics field, follow the steps below:

• Open your Excel application.
• Open the worksheet and click the Insert button to access the My Apps option.
• Click the My Apps button and then click the See All button to download ChartExpo.

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

• It may ask you for the log in for the first time and once you are successfully logged in you, following charts will appear on your screen.

In the coming section, we’ll use the Radar Chart (one of the popular charts in data analysis and data analytics field) to visualize data.

You don’t want to miss this.

How to Visualize Data using ChartExpo’s Radar Charts?

Let’s visualize the data set below using Radar Charts in ChartExpo.

Year	Subject	No. of Enrollments
2019	Biology	80
2019	Music	65
2019	Programing	75
2019	Art	80
2019	Geography	90
2019	English	85
2019	Sport	65
2019	Mathematics	70
2019	Physics	80
2020	Biology	93
2020	Music	99
2020	Programing	80
2020	Art	100
2020	Geography	60
2020	English	95
2020	Sport	75
2020	Mathematics	100
2020	Physics	60
2021	Biology	95
2021	Music	75
2021	Programing	109
2021	Art	80
2021	Geography	109
2021	English	75
2021	Sport	50
2021	Mathematics	55
2021	Physics	65

•  Copy the table in Excel to get started with Radar Charts (one of the best charts for processes in data analysis and data analytics).
• Click the Insert buttonto access the My Apps option.
• Click the My Apps optionto access the ChartExpo add-in.
• Type “Radar Charts” in the Search box.

• Alternatively, click the Lists button to access all the charts and then scroll slowly to find Radar Charts in the General Analysis Charts, as shown.

• Select data and click the Create Chart from Selectionbutton to visualize the data, as shown below.

• Check out the final Radar Chart below.

 Insights

• In 2021, students enrolled in Biology, Programming, and Geography subjects more than others.
• In 2020, the leading subjects with regard to student enrollments were Math, English, Art, and M
• During 2019, all the subjects recorded a decline in student enrollment rates.

FAQs:

Do data analysis and analytics mean the same thing?

Data Analysis is the technique of observing, cleaning, and modeling raw facts and figures to draw actionable insights. Conversely, data analytics includes all the steps you take, both human- and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data.

Why is data visualization important in data analysis?

Data visualization is a must-have skill for all businesses because it’s the only reliable way they can predict opportunities and risks. Decision-making is increasingly relying on data, which comes with overwhelming velocity and volume. You cannot comprehend raw data easily without some layer of abstraction, such as charts.

Wrap Up

We hope we’ve cleared the air regarding data analysis vs. data analytics.

Data analytics is the broad field of using data and tools to make business decisions. Conversely, data analysis is a subset of analytics.

As a professional in data analytics, your role is to curate compelling stories from raw data using an array of tools and methodologies, such as visualization. And this means you need tools that are reliable, easy, and pocket-friendly.

Freemium tools, such as Excel come with basic charts, which require more time and effort to customize. Essentially, you cannot find ready-to-use charts in the spreadsheet application.

To succeed in key processes in data analysis vs. data analytics, download and install add-ins in your Excel. One of the proven and tested data visualization add-ins for Excel is ChartExpo.

ChartExpo has over 50 graphs ready-made and visually appealing charts to get you started in the data analytics profession. The intuitive tool is trusted by more than 28,000 professionals and business owners worldwide.

Besides, it has an ultra-friendly user interface (UI), which means you don’t need coding or programming skills to visualize your data.

Sign up for a 7-day free trial today to increase your odds of success in data analytics.