6 Types of Data Analysis Methods: Which to Use When?

Businesses need to make informed decisions to stay competitive in this data-driven world. That’s why data analysis has become necessary for many companies in almost every industry. Multiple data analysis methods focus on various business needs—some for predicting the future, others for analyzing past trends, and some for deriving actionable insights. 

This blog post will explore all of them, their benefits, and their challenges. Read on for an in-depth understanding of each data analysis technique. 

What is Data Analysis?

Data analysis refers to the process of turning raw data into meaningful insights. It involves cleaning data, processing it, and generating patterns and visualizations. 

The key aspect of data analysis is identifying trends, allowing businesses to make informed decisions in various operational processes.

For example, as a data scientist at Target, I extract historical sales data and analyze it to identify factors contributing to higher sales. This analysis can be used to predict the best possible markdown or discount to improve profits and revenue.

Learn how data analysis differs from data analytics.

Importance of Data Analysis

Raw data takes you nowhere unless it’s cleaned, transformed, and analyzed to draw meaningful insights that help in business decision-making. Here are the use cases where data analysis is important.

• Resource allocation: Analyzing historical expenses enables businesses to allocate budgets more effectively for future years. Additionally, examining workforce data supports better decision-making in hiring and project allocation.
• Customer services: You can analyze customer data to understand their behavior and buying patterns. This will help you create personalized recommendations and targeted ads to improve sales. 
• Finance and banking: Anomaly detection techniques can help identify fraud transactions and improve risk management. 
• Healthcare: Healthcare service providers can use data and analyze it to predict diseases and their severity. 
• Supply chain: You can utilize data analysis to identify delivery patterns and optimize routes for faster delivery. Additionally, the insights from historical sales and inventory data will help you with effective demand planning. 

6 Types of Data Analysis Methods

Data analysis is primarily categorized into descriptive, diagnostic, predictive, and prescriptive analysis. Exploratory data analysis and qualitative and quantitative analysis are also widely used techniques. Let’s understand each of them in detail. 

1. Descriptive Analysis

Descriptive analysis summarizes raw data and draws some meaningful insights from it. This is a basic analysis performed on current and historical data to analyze what has happened in the past. 

Many companies use descriptive analytics daily. Historical data analysis performed in Microsoft Excel or visualizations in Tableau often comes under this category. This can be the starting point for companies to understand trends in their data and decide on the next steps or further analysis. 

2. Diagnostic Analysis

Diagnostic analysis emphasizes the root cause for any event, trend, or behavior. This highlights the reasons behind unexpected problems or outcomes. 

The diagnostic analysis addresses questions like why a particular event occurred. And identifies key factors contributing to the issue. These insights make it easier to resolve the problem effectively. 

3. Predictive Analysis

Wouldn’t you take the necessary steps to improve the future if you knew what would happen? That’s exactly what predictive analytics does. It leverages statistics, machine learning, and artificial intelligence to create models that determine future outcomes. 

For example, analyzing customer buying behavior using customer relationship management (CRM) data can show spending patterns. You can use these insights to offer personalized product recommendations and tailored discounts, increasing purchase rates and higher revenue.

4. Prescriptive Analysis

While predictive analysis emphasizes what will happen, prescriptive analysis focuses on what we can do next. Prescriptive analysis analyzes data to determine actionable steps for improving metrics such as customer retention and preventing fraud, revenue, or sales.  

By considering various scenarios and multiple opinions, prescriptive analysis guides decision-makers to invest in the right strategies that maximize desired outcomes. Other focus areas for prescriptive analysis are allocating budgets, managing inventory, supply chain planning, scheduling workforce resources, and driving optimal resource utilization and cost savings.

5. Exploratory Data Analysis (EDA)

Exploratory data analysis is a well-known technique that contains various data analysis methods to understand key characteristics of the data. Typically, Python pandas has various in-built methods to conduct initial investigations, generate visualizations, and determine relationships within the data. 

For example, the info() method details the dataset, including column names, data types, and the count of non-null values in each column. The describe() method generates statistical summaries such as mean, median, variance, and percentiles for each column. You can also generate correlation heat maps to identify how multiple columns are related. 

6. Qualitative and Quantitative Analysis

Quantitative analysis involves analyzing numerical data to discover statistical insights like mean, median, correlation, and confidence. It quantifies the findings, focusing on “when” and “how much”. In contrast, qualitative analysis explores deeper insights, answering questions like “why” and “how”. 

How to Choose the Right Data Analysis Method?

1. Define the objective: Clearly describe the end goal you want to achieve through data analysis, whether discovering opportunities for improvement in the current system or drawing actionable insights for further optimization. This will allow you to choose the right tools and techniques to achieve it.

2. Understand your data: Different data analysis techniques excel in different data formats. Therefore, it’s essential to understand your data type—whether structured or unstructured, qualitative or quantitative, discrete or continuous.

3. Choose the right technique: From the various data analysis methods discussed, choose the one that aligns with your defined objectives and is suitable for the data format you have.

4. Evaluate: Once done, evaluate your analytics insights and ensure your objective is fulfilled. If not, try another data analysis method that’s relevant and suitable for your data. 

Benefits of Using Data Analysis Methods

Different data analysis methods are suitable for different scenarios. Here are a few advantages and use cases of various data analysis techniques.

1. Know your customers better

Analyzing customer data helps companies understand how well a product is performing in the market, where the customers are spending, and price-sensitive instances. 

Knowing this data helps you assess your current business processes and conduct further data analysis to optimize them. Descriptive and exploratory data analysis techniques are widely used in these scenarios.

2. Enable Intuitive decision making

Predictive analytics uses current and past data to determine future outcomes. By forecasting the future, you can make more informed decisions based on facts rather than assumptions. 

For example, companies can use predictive analytics to forecast sales for the next quarter. This information allows them to plan inventory levels much better, avoiding excess inventory or early stockouts.  

3. Increase revenue 

Data analysis methods like predictive analytics and perspective analysis give you insights that help you strategically plan resource utilization, demand planning, and investment strategies, resulting in enhanced business operations and improved revenue. 

Challenges in Data Analysis

Getting meaningful and actionable insights using data analysis optimizes your business operations. However, there are significant challenges along the way. Here are the most common ones. 

1. Data management 

Today, large volumes of data are available in various formats for you to collect, store, and manage. You need suitable and scalable storage solutions that support changing data volumes and formats. 

Moreover, your data should align with security and data governance principles that protect your data and consumers’ information. Data analysis comes later when you can set up these things and manage the data well, which is not easy. 

2. Collecting real-time data

Whether you analyze external data or your own customer’s data, data collection is the first step. You might gather business data from internal CRM tools, your website, or other internal databases. External data can be sourced from the internet. In either case, collecting accurate data efficiently from the source is crucial. 

While custom scripts can be written to extract the data effectively, it is complex and requires technical experts. That’s why there are ETL tools in the market to simplify the process. One such good example is Hevo Data. 

3. Data quality

Many of us provide false data on online forms or surveys or leave questions unanswered, either out of laziness or to protect our personal information. But if a company relies on this inaccurate data to make decisions, there’s a significant risk of processes becoming more inefficient. 

So, you should always monitor the accuracy of the data you are processing. Moreover, ensuring consistency and keeping validation checks while extracting data can also help you maintain data quality.

4. Real-time analytics

Real-time analytics has surged in this modern world. Many video streaming, social media, and IoT applications function on real-time data. 

However, leveraging it requires you to utilize technologies like in-memory computing and streaming data processing because it needs to be processed as it is generated and made accessible without any delay.

Conclusion 

Data analytics is utilized in many use cases, from improving decision-making to removing fraudulent transactions. Multiple data analysis methods have been developed to cater to different business needs. 

By utilizing the right data analysis technique on the right data for the right use case, you can effectively harness the power of data in your domain. 

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