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Data is increasingly becoming more valuable than oil.
And this is because it holds the solutions to the problems businesses face in the current landscape, such as cutthroat competition.
While others scratch the surface, your job as a data analyst is to explore insights using multiple perspectives. For instance, when analyzing your business’ strategic metrics, you can relate them from different perspectives, such as productivity and technological landscape.
The reason why data analysis is important to businesses like yours is that it backs decision-making processes. In other words, it replaces the old way of doing things, which is relying on gut feeling and intuition to decide key business issues.
As a data analyst, your main task is to connect the key decision-makers to actionable insights. To succeed in this profession, you need solid knowledge and skills in analyzing data for hidden insights.
Also, you need the best tools you can find.
Google Sheets is one of the popular tools among business owners and professionals. However, it comes with weaknesses that can set you up for letdown in the data analysis process.
For instance, it comes with basic charts that need extra time and effort to edit and customize based on your preferences.
However, you can transform Google Sheets into a reliable data visualization tool by installing third-party applications called add-ons.
In this blog, you’ll learn:
Before we dive right into the heart of the blog, let’s define a couple of terms associated with the ways of data analysis.
Data analysis is all activities associated with the cleaning, identifying scope, and displaying raw information (via charts, maps, and graphs) to draw meaningful conclusions.
Data is arguably more important than it was decades ago.
Businesses all over the world are increasingly leveraging data to understand the needs and preferences of their target markets. Besides, we are generating data at an unprecedented rate.
For instance, if your website is tagged, you can easily check the behavior of the visitors and optimize drop-off areas.
It’s through data analysis that you get a 360° view of all aspects related to your customers, such as:
The insights you gain from the data analysis process can help in optimizing problematic areas for more success. Besides, you can easily personalize your marketing messages to resonate more with the target verticals.
There’re 7 types of data analysis you can conduct in your raw data for meaningful insights.
Let’s check them out.
The cluster analysis (one of the ways of data analysis) entails grouping variables with similar attributes in common categories. Since there is no target variable when clustering, the method is handy, especially when probing for hidden patterns in the data.
You can use the methodology to add a perspective when analyzing data.
This is one of the data analysis methods you can use to probe data for actionable insights.
Cohort analysis uses historical data to examine and compare a determined segment of users’ behavior, which can then be grouped with other variables with similar characteristics.
You can leverage this method to gain a wealth of insights into consumer needs. Also, it can help you understand the impact of your campaigns on specific customer segments.
The regression analysis (one of the ways of data analysis) uses historical data to understand how a dependent variable is affected when one or more independent variables change.
By understanding key variables’ relationships and how they developed in the past, you can:
The neural network forms the basis for artificial intelligence (AI) and machine learning (ML).
More so, it’s a form of data-driven analytics that attempts (with minimal intervention) to understand how our brains process insights and predict actions. Neural networks learn from every data transaction, meaning they evolve.
The factor analysis (also called “dimension reduction”) is one of the ways of data analysis you can use to describe variability among key observed variables relative to the unobserved variables called factors.
The goal of the method is to uncover independent latent variables, an ideal strategy for streamlining specific data segments.
For instance, you can use this methodology to investigate customers’ evaluation of your offerings based on different variables like color, shape, usability, current trends, materials, comfort, frequency of usage, etc.
Data mining (one of the ways of data analysis) is an umbrella term for analyzing data points for additional value, direction, and context.
It uses exploratory statistical evaluation to identify dependencies, relations, data patterns, and trends in raw data. Adopting a data mining mindset is essential to success, especially when considering analyzing data.
Text analysis (also known as text mining) is the process of taking large sets of textual data and arranging them in a form that’s easier to comprehend.
This data analysis process can help you extract hidden insights into textual information.
Let’s check them out.
Qualitative data is made up of words and descriptions. Although you can observe the data type, it’s challenging to analyze it.
For example: To find out the significance of a resident doctor in a company, you can divide the collected data into:
Compare and contrast is the best-suited analysis method to investigate qualitative data with such responses.
The quantitative data comes in numerical form. This type of data can be:
You can easily visualize survey questions with numerical attributes, such as age, rank, cost, length, weight, scores, etc., using graphs, tables, maps, and charts.
For example: “How much did that laptop cost?” is a question that requires quantitative data. You can easily use values associated with standard measuring parameters, such as pounds or kilograms for weight, dollars for cost, etc.
Quantitative data makes the measurement of various parameters controllable due to the ease of their mathematical derivations.
Categorical data are displayed in groups. For instance, a person responding to a survey by describing his lifestyle, marital status, smoking, or drinking habit falls under the categorical data.
You can use the Chi-square test (one of the ways of data analysis) is to analyze categorical data.
For example, categorical variables include race, sex, age group, and educational level.
While the latter two variables may also be depicted using numbers, it’s often more informative to categorize them into a relatively smaller number of groups.
The data analysis process entails gathering, exploring, and probing raw information for meaningful answers. The process includes the following:
Sources of data include:
Not all of the data you collect will be useful. In fact, a considerable chunk of it is made primarily of noise. And this means you need to clean anomalies, such as white spaces, duplicate records, and basic errors.
Data cleaning is a mandatory step before analysis.
It’s one of the processes you need to leverage tools to save time and extract reliable insights. Some of the popular tools of analysis include Excel and Google.
Now that you have your results, you need to interpret them and develop the best courses of action.
Data visualization entails using maps, charts, and graphs to display insights into data. Use visualization designs that are easy to interpret in your data stories. You can click on below links to check the visualization library.
Whether you run an online or offline business, data analysis can help you draw a solid and reliable marketing strategy.
Essentially, you can target the right audiences with offers that resonate with their needs. Most companies use ways of data analysis to optimize their marketing campaigns.
With a solid data analysis process, you can easily re-design your products or services to meet the needs of your target market. Also, it can make your offers more appealing to the market.
Innovation is driven by the insights extracted from raw data.
Yes, you read that right.
Data analysis is extremely important for businesses whose goal is to expand their operations across international borders. You need data from the new markets to formulate reliable marketing entry strategies.
For example, if you want to venture into a foreign market, you can give samples and gather customers’ opinions and sentiments.
Also, you can leverage data collected to understand demographics, interests, and purchasing behaviors.
Data analysis can provide you with reliable insights into how much money you’re spending on attracting every customer. Also, you can learn more about the return on investment (ROI) of your marketing efforts.
For example, if your business spends $100 and attracts 40 customers, it means you’re paying $2.50 per customer. Your profit per customer has to be more than $2.50.
If you spend $100 to attract 200 customers, you’ll run a very profitable campaign. And this is because you’ll pay only half to get much more in return.
Unless you know the cost of customer acquisition, it’s pretty challenging to plan for an advertising or promotional campaign.
Let’s check out the easy-to-follow steps for analyzing data for meaningful conclusions.
Before jumping into your data analysis, ensure you’ve defined your goals are clearly defined.
The questions above can help you identify the data type you need to collect (and ways of data analysis you’ll use). Design your questions around a specific problem and possible solutions.
Goal: To improve customer service
Questions
Once you’ve defined your goals, decide how to measure them.
For example, if you want to measure performance, dig into the call center data to determine how long it takes each agent on average to respond to a customer.
Then, measure each agent’s performance relative to the overall average (using one of the ways of data analysis we mentioned earlier).
Now that you know your goals and how to measure them, start collecting the correct data type.
While it’s advisable to collect both quantitative and qualitative data types, focus on data that are relevant to the questions you’re trying to answer.
Excel and Google Sheets are popular data visualization tools worldwide, especially in crunching numbers. These tools allow you to plug in your quantitative data and generate visualizations designs, such as charts and graphs.
However, these tools come with weaknesses that can slow your data analysis processes (you’ll learn this in the coming sections).
Visualization and interpretation of insights is arguably among the most significant processes in data analysis.
Displaying insights using charts and graphs is a smart way of making data stories appealing to your target audiences (or readers). Besides, it transforms data into a form that can be easily used during the decision-making process.
Google Sheets is one of the most used data visualization tools by most professionals and businesses. This spreadsheet app is familiar to many.
If your goal is to create insightful, read-to-use, and easy-to-interpret charts, you’ve got to think past the Google Sheets.
We suggest installing third-party apps (add-ons) to access insightful and appealing visualization designs.
There are thousands of add-ons purposely for data visualization in the Google Workspace Marketplace. To save you time, we’ve tested a ton of add-ons to find the best one for day-to-day needs.
ChartExpo stands out from the pack because of the following consumer-friendly attributes:
If your goal is to create a lean visualization dashboard and extract insights into two different variables in your data, your go-to chart should be ChartExpo.
ChartExpo is a trusted tool that over 20,000 professionals worldwide use to conduct data analysis processes.
Besides, it comes as an add-on you can easily install in your Google Sheets to get the most from your data. Also, it has 50-plus ready-made and clear data visualization designs to ensure you never struggle to visualize your data.
In the coming section, you’ll learn how you can leverage ChartExpo (and ways of data analysis) to extract actionable and reliable insights.
You don’t want to miss this.
In this example, we’ll use ChartExpo’s Radar Charts to gain insights into the tabular data below.
| Products | Months | Orders |
| Lip Gloss | January | 487 |
| Powder | January | 327 |
| Eyebrow Pencil | January | 286 |
| Lip Gloss | February | 160 |
| Powder | February | 427 |
| Eyebrow Pencil | February | 479 |
| Lip Gloss | March | 357 |
| Powder | March | 466 |
| Eyebrow Pencil | March | 213 |
| Lip Gloss | April | 1385 |
| Powder | April | 419 |
| Eyebrow Pencil | April | 1001 |
| Lip Gloss | May | 1407 |
| Powder | May | 499 |
| Eyebrow Pencil | May | 211 |
| Lip Gloss | June | 881 |
| Powder | June | 306 |
| Eyebrow Pencil | June | 219 |
| Lip Gloss | July | 544 |
| Powder | July | 434 |
| Eyebrow Pencil | July | 458 |
| Lip Gloss | August | 365 |
| Powder | August | 1096 |
| Eyebrow Pencil | August | 228 |
| Lip Gloss | September | 1088 |
| Powder | September | 228 |
| Eyebrow Pencil | September | 369 |
| Lip Gloss | October | 858 |
| Powder | October | 570 |
| Eyebrow Pencil | October | 379 |
| Lip Gloss | November | 448 |
| Powder | November | 393 |
| Eyebrow Pencil | November | 404 |
| Lip Gloss | December | 981 |
| Powder | December | 396 |
| Eyebrow Pencil | December | 695 |
The data analysis process uses analytical and logical reasoning to extract insights from raw information.
In other words, the key purpose of data analysis is to uncover hidden insights, which you can use in your decision-making process or optimize problematic areas in your business.
Data analysis is all activities associated with the cleaning, identifying scope, and displaying raw information (via charts, maps, and graphs) to draw meaningful conclusions.
Leveraging the ways of data analysis to enjoy the benefits should never be complex, time-intensive, or nerve-wracking to you.
There’s a broad collection of tested and proven visualization designs you can use to create irresistible data stories. Besides, to succeed in data analysis processes, you need a reliable tool on your side.
Google Sheets should not be your go-to tool if your goal is to access ready-made and easy-to-read charts for analyzing data.
Why?
The spreadsheet application has basic charts and graphs, which require a lot of time and effort to customize. But we’re not recommending you to do away with Google Sheets.
So what’s the solution?
Install third-party add-ons, such as ChartExpo in Google Sheets to access ready-to-use, insightful, and visually appealing charts.
ChartExpo is an add-on you can easily download and install in your Google Sheets. More so, you don’t need programming or coding skills to visualize your data using the add-on.
Also, you can easily export your ready-to-use charts in JPEG and PNG formats.
Sign up for a 7-day free trial today to access a complete list of ready-made visualization designs for your data narratives.
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