5 types of data analysis explained with examples

Even with a team of analysts and scientists diligently collecting data, how do you know if you’re making the right decisions from it? Data analysis is the bridge between raw information and strategic action, enabling your company to make data-backed decisions that boost profitability, efficiency, and accuracy. 

In this guide, we’ve laid out the five main types of data analysis, alongside examples, to help you build a data foundation that operates on foresight, not hindsight.

What is data analysis, and why is it important?

Data analysis is the inspection, cleaning, and transformation of raw information. Until it’s processed, raw data is tricky to extract any information from, but effective analytics helps companies draw more accurate conclusions about their products, their operations, and sometimes the wider industry.

Businesses can use these insights to support decision-making, personalize customer experiences, mitigate risks, and drive more profit.

Data analysis benefits

Some ways effective data analysis can help your business include:

• Effective decision-making: With more context about your products, customers, and industry, you can make better, data-backed decisions. 
• Increased operational efficiency: Data analysis often reveals inefficiencies, such as bottlenecks in sales processes. 
• Competitive advantage: If you spot a data-backed trend before your competitors, you can exploit the advantage more quickly and effectively. 
• Cost reduction: By tracking spending (and how it leads to revenue), you can find opportunities to cut budgets.

Data analysis challenges

There are a few common challenges to watch for when analyzing data, such as:

• Data management scalability: Managing more data means more complexities, which can lead to crucial data being overlooked.
• Data silos: Scattered, disconnected data can be tricky to analyze. Data integration platforms like Fivetran are a great solution to this
• Data quality and skills: Without high-quality data and people with the skills to interpret it, your data collection efforts could be in vain.
• Real-time data processing: Not all data is processed immediately. Delays can make data less valuable to your company.

5 types and examples of data analysis

Here are five of the most common types of data analysis, alongside pros, cons, and example use cases.

1. Descriptive analysis

What it is: As the simplest form of data analysis, descriptive analysis is the foundation of all database analytics. It summarizes raw data into something interpretable, answering the question, “What happened?”

When to use it: Companies often use descriptive analysis to generate reports, track KPIs, or understand general performance. 

Pros: 

• Easy to implement and understand
• Provides a clear, high-level overview of your data

Cons:

• Only looks at the past
• Doesn’t explore the reasons behind why something happened

Example: An online retailer might use descriptive analytics to generate a Q4 sales report showing total revenue, units sold per product, and sales by region.

2. Diagnostic analysis

What it is: Diagnostic analysis takes findings from descriptive analysis and digs deeper to find the root causes. It answers the question, “Why did it happen?”

When to use it: Use diagnostic analysis when you have an unexpected outcome and need to understand the root cause, like a sudden drop in sales or a spike in website traffic.

Pros: 

• Helps identify the root causes of problems through data
• Provides context for a problem that you can act on

Cons: 

• Can be time-consuming
• Correlation doesn’t always equal causation, so it's easy to draw the wrong conclusions

Example: A company’s quarterly sales report shows a 20% drop in sales for a specific product. Diagnostic analysis revealed that a major competitor ran a promotional campaign simultaneously and that a new software bug was causing checkout errors. 

3. Predictive analysis

What it is: Predictive analysis uses historical data, statistical algorithms, and machine learning to identify future outcomes and their likelihood of occurring. It answers the question, “What is likely to happen?”

When to use it: Predictive analytics software is useful for forecasting, risk assessment, and identifying opportunities like new products that customers are likely to buy. It differs from descriptive and diagnostic analysis in being proactive rather than reactive. 

Pros:

• Helps you anticipate future trends and events
• Proactive decision-making can give you an advantage over purely reactive competitors 

Cons:

• Predictions are never 100% accurate
• Requires clean, high-quality historical data and expertise to build models

Example: A company uses past sales, seasonality trends, and other economic data to build a model that predicts which products will be bestsellers during the next holiday season, helping them decide which products to focus marketing efforts on.

4. Prescriptive analysis

What it is: The most advanced form of analysis, prescriptive analysis not only predicts what will happen in the future but recommends the actions needed to achieve a desired outcome. It answers the question, “What should we do about it?”

When to use it: Use prescriptive analytics to optimize decision-making when there are so many variables that humans simply can’t process them all, like with supply chain logistics or dynamic pricing.

Pros:

• Provides clear, actionable recommendations
• Can automate complex decision-making processes

Cons:

• Highly complex and expensive to implement
• Requires massive amounts of high-quality data

Example: A company’s prescriptive analytics model recommends specific discount levels for underperforming products, helping them maximize revenue and automatically adjust pricing in real-time based on customer behavior.

5. Time series analysis

What it is: Like the name suggests, time series analysis focuses on data points collected over time. It identifies trends, cycles, and seasonal patterns to understand changes and forecast future events.

When to use it: Use it when your data has a time component and you need to understand patterns over days, weeks, months, or years. Some common applications are sales forecasting, stock price prediction, and demand planning.

Pros: 

• Excellent for identifying seasonal patterns and long-term trends
• Widely applicable to several industries like finance, energy, manufacturing, and retail 

Cons:

• Assumes that past patterns will continue into the future
• Requires consistent, regularly collected data over a long period

Example: A business analyzes three years of weekly sales data. They identify that demand for outdoor furniture spikes in April and May and that sales have been growing 8% year-on-year. This understanding of seasonality allows them to adjust inventory levels and prices according to demand. 

How to choose the best data analysis method

To help you identify the best data analysis method for your team, we’ve put together a simple step-by-step checklist.

Step 1: Define the objective

First, determine what you’re hoping to find out from the analysis. Are you trying to understand past performance or why something happened? Or hoping to forecast the future or get a recommendation?

Step 2: Check data quality

Next, ensure you have the appropriate data to complete the analysis. For instance, predictive and prescriptive analytics require large volumes of clean, historical data. If your data is messy or incomplete, you’ll need to start with a data integration strategy that includes a solid pipeline architecture.

Also, consider whether your data needs quantitative or qualitative data analysis, such as from customer interviews or survey responses. While companies often focus on quantitative data, analyzing sentiment can also be useful.

Step 3: Choose the appropriate technique

When choosing your analysis method, start with the simplest method first and work up from there. There’s no reason to carry out predictive modeling if all you need is a descriptive report.

Step 4: Evaluate and iterate

As you gather more data over time, your models will need to evolve to reflect the new information. Frequently question whether your data is helping you make better decisions, and if not, go back to step one. 

How Fivetran accelerates data analysis

To carry out effective analysis, you need a solid foundation of clean, fresh, and accessible data. Fivetran’s automated data movement platform ensures that analysts always have ready-to-use data in their warehouse, automating the entire extract, load, transform (ELT) process. 

With Fivetran Transformations, teams spend less time on pipeline management and more time on data analysis that increases revenue. When you automate your data infrastructure, analysts can instead focus on moving up the maturity ladder from descriptive to predictive to prescriptive analytics. 

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