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Analyzing data is vital for running a successful business. You can utilize data in many ways for your company’s operations. Understanding the different types of data analysis and tools can help make better-informed decisions.
There are different types of data analysis that various companies use globally. Whether you are a growing business, data scientist, or analyst, understanding data analysis types is essential. Utilizing the right data analysis types and tools can help deal with complex data, minimize risks, and reduce human bias.
Have you ever experienced challenges in comprehending raw data? Well, it’s because you need the right know-how and tools. Gain more knowledge from this article and improve your business operations.
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Although different experts, organizations, or groups have various methods of approaching data analysis, they all refer to a single definition. Data analysis is inspecting data sets to know their trends and make conclusions from the information they contain.
A common question that arises in this context is, what does a data analyst do? Essentially, a data analyst is responsible for examining this data to uncover valuable insights. Analyzing data helps reduce risks in decision-making by providing useful statistics and insights, mainly presented in graphs, tables, images, and charts.
Data analysis must provide helpful insight into data from its raw form. The data analyst will help organize, analyze, and transform data into coherent and intelligible information.
The top 4 types of data analysis are:
Have you ever asked yourself why a particular problem happened?
Solving the question ‘why?’ is when diagnostic analysis takes place. Diagnostic analysis helps solve the cause of the outcome. Growing businesses use this analytic type to identify the behavior of patterns.
A crucial step in diagnostic analysis is the creation of detailed information. In case problems come, there will be a high chance that you have already collected data about the problem. Therefore, you will not need to repeat collecting data because the problems are interconnected.
Descriptive analysis is the foundation of data insight. Most organizations commonly use it due to its simplicity. Descriptive analysis helps to answer ”˜what happened’ by summarizing past data in the form of dashboards.
Why descriptive analysis?
Descriptive analysis is vital because it helps track Key Performance Indicators (KPIs). The KPIs help describe business performance based on benchmarks. The other application of KPIs is in sales leads overview and monthly revenue reports.
Prescriptive data analysis combines insights from previous analysis to help in determining the course of action, which will then help in problem-solving. Only a few organizations use prescriptive analysis.
If you want to use this type of data analysis, you should be ready to put in much effort and resources.
Predictive analysis helps in answering what is likely to happen. It uses previous data to make future predictions. The predictive analysis relies heavily on models of statistics. The prediction accuracy also relies on the quality of the data.
Examples of Data Analysis are as follows:
An example is when an eco-friendly store that sells products notices a dip in revenue. On the cause of the event, the company learns that the decline in revenue was caused by a drop in single product sales plastic carrier bags.
The research and analysis can reveal a relationship: the government changed the law by making carrier bags illegal, making the sale of plastic carrier bags go down. This analysis is an example of diagnostic analysis.
Financial statements are periodic reports on business financial information; they help provide a good view of the company’s financial health. Here you can compare sections of reports.
Here, prescriptive analysis plays a crucial role through lead scoring (lead ranking). The lead ranking assigns the process to value points to different actions on the sales funnel, enabling you to rank the leads according to how likely they will convert to potential customers.
Here, the customer outflow and inflow depend on multiple factors, which all depend on the number of hotel staff members a hotel needs at a particular time. To determine the number of staff at a given time, the team can develop a regression model where several factors are considered. The model will help in preventing overstaffing and understaffing.
Data visualizations can help explore data structures, clean errors, detect unusual groups and outliers, identify clusters and trends, evaluate model output, spot local patterns, and present results.
Data visualizations like graphics can reveal essential features that models and statistics may miss. These features include local patterns, clusters, unusual data distribution, missing values, gaps, and implicit boundaries.
Sounds good, right?
Before we dive deeper, what are data analysis tools?
Data analysis tools, including data visualization tools, are designed software and programs that help collect and analyze data to uncover valuable insights and improve the process.
No matter the type and size of your business, you can improve the quality of your strategies and experience, products and services, etc., by using the correct data analysis tool from ChartExpo.
Excel is the best tool to use. For example, if you want to persuade your customers why the information follows a hierarchical order, you can create a Sunburst chart using ChartExpo in Excel.
The chart is easy to read and interpret.
Let us use a real-time example to teach you how to create a Sunburst chart.
For example, you own various bicycle shops across your country. You want to know the bicycle brands that contribute to your sales revenue.
Which chart will you use to visualize your data?
Sunburst can be the right option. You can arrange your bicycle sales across your country as shown.
| Orders | States | Cities | Total Orders |
| Orders | California | Fresno | 450 |
| Orders | California | Irvine | 524 |
| Orders | California | Malibu | 551 |
| Orders | Texas | Austin | 565 |
| Orders | Texas | Dallas | 445 |
| Orders | Texas | Plano | 454 |
| Orders | Florida | Miami | 522 |
| Orders | Florida | Tampa | 463 |
| Orders | Florida | Naples | 521 |
To get started with ChartExpo, install ChartExpo in Excel from this link.
Follow the steps below to use ChartExpo in Excel.
Here is what the final chart will look like.
The four types of data analysis are diagnostic, descriptive, prescriptive, and predictive analysis. These tools help you track the key performance of your business.
To find relevant information, make recommendations, and aid decision-making, data analysis is the process of looking at, purifying, transforming, and modeling data. We can stop speculating and start making decisions based on data analytics.
Data analysis is vital for running a successful business. We’ve learned that the analyzing process involves collecting, organizing, and interpreting data so that you can solve problems or answer questions concerning your business performance.
To make a successful analysis, you also need a charting tool. ChartExpo is here for you. You can use ChartExpo in Excel to create charts like the Sunburst. You can also use Google Sheets to create other charts and graphs depending on your data variables.
Charts and graphs are crucial in minimizing errors by displaying outliers, and gaps, detecting unusual groups, identifying trends and clusters, spotting local patterns, evaluating model output, and presenting results.
You can use Google Sheets to create bar charts, line charts, pie charts, and treemaps.
Start your 7-day free trial to make your data chart. ChartExpo is here to help solve your problem.
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