Data analysis is a process of testing and standardizing data. This process is widely applied in many areas of life such as automation, finance, healthcare, or marketing processes…

Data analysis is highly appreciated for its reliability and accuracy when it can determine both quantitative and qualitative data, supporting the accurate identification of what is ineffective in your process.

To learn more about the characteristics, roles, and how to build this process, follow this article!

What are the types of data analysis?

Based on the purpose and application, data analytics can be divided into four main categories:

• Predictive data analytics – This category uses predictive and statistical models to estimate future outcomes, such as forecasting revenue from different target audiences in an advertising campaign. It is often used to predict trends and identify correlations, and causes.
• Prescriptive data analytics helps recommend actions based on predicted outcomes, incorporating artificial intelligence and machine learning to optimize business decisions, test variables (e.g. product color), and improve results.
• Diagnostic data analytics – To want to understand the causes and effects of past events. This classification is often used for data mining and deep analysis to find out why a problem occurred, such as determining the cause of a drop in sales.
• Descriptive Data Analysis – To summarize data and answer basic questions such as “how much, when, where, and what”, descriptive data classification will report results in the form of canned (pre-designed) reports or custom reports that quickly and specifically provide information on demand.

What are the benefits of data analysis?

Data analysis is a supporting tool and a decisive factor in helping businesses develop sustainably and compete more strongly. This factor will contribute to bringing many important benefits, helping organizations improve operational efficiency thanks to outstanding benefits such as:

• Informed decision-making: Data analysis has helped leaders make decisions based on data instead of emotions. Decisions integrate internal data and external sources for a more comprehensive and accurate view.
• Operational efficiency: Analyze workflows to find weaknesses and opportunities for improvement. Support supply chain optimization, and reduce unnecessary losses and costs.
• Improve customer experience: This tool has improved satisfaction by identifying and correcting errors in workflows. At the same time, personalize customer interactions, and increase the effectiveness of marketing campaigns.
• Revenue growth and competitive advantage: Data analytics accurately identifies market trends, competitors, and new opportunities. From there, it is easy to optimize products, services, and business strategies to increase profits.
• Risk management and forecasting: Data analytics also identifies potential risks such as fraud, cybersecurity, or process errors, and can provide more effective preventive measures.

What are the biggest limitations of data analytics?

Despite the benefits, data analytics also faces some significant challenges:

• Unifying data sets from multiple sources requires a tight integration strategy. Supporting IT infrastructure and cross-departmental collaboration are needed to share data effectively.
• Scalability: Many data analytics projects are resource-intensive, making it difficult to scale as needs increase. Requires detailed planning of infrastructure and deployment processes.
• Data privacy and security: Risks arise when data moves between environments or is accessed without authorization. Role-based access, compliance with regulations such as GDPR, and strict security protocols should be implemented.

How to analyze complete data?

To make the data analysis process go smoothly, you need to go through the following 7 basic steps:

Set clear goals

 Start by defining the analysis objectives. For example, you need to evaluate the performance of the production line or optimize energy consumption. Setting goals will help you focus on the necessary data.

Choose the right type of analysis

You can base the goals to determine the type of analysis such as

• Descriptive analysis: Summarize existing information.
• Predictive analysis: Find out the problems.
• Projected analysis: Expected future trends.

Plan data collection

You need to identify data sources (sensors, SCADA systems, production reports) and prepare to collect budgets, tools, or supporting software. A detailed plan helps you save time and resources.

Collect real data

Integrate data sources from automation systems or manufacturing machines. Ensure data is stored securely and completely, and use data management software to increase efficiency.

Clean

You need to check the data to remove duplicate loops, format errors, or irrelevant information. Accurate data is the foundation for any analysis.

Analyze and evaluate

You can also use analysis tools such as Excel, Python, or specialized software (such as Tableau, or Power BI). Search for trends and relationships between numbers to give the perceived values.

Visualize results

You can turn data into easy-to-understand charts and graphs. For example: Temperature charts and applications in product lines help you quickly get anomalies.