26  Understanding Problem Types in Data Analysis

As a data analyst, problem-solving is at the heart of your work. Every project you encounter will involve identifying, exploring, and solving problems using data. These problems can vary in scale and complexity, but they all begin with one essential step — understanding what kind of problem you’re trying to solve.

Data analysts generally work with six common types of problems:

Let’s explore each of these in detail.

Making Predictions

This type of problem involves using data to make informed forecasts about future events or outcomes.
For example, a hospital system might use remote patient monitoring to predict health events for patients with chronic illnesses. By combining daily health vitals with data on age, risk factors, and medical history, the hospital can forecast potential health problems and reduce hospitalizations through early intervention.

Categorizing Things

Categorization means assigning data into different groups or clusters based on shared characteristics.
For instance, a manufacturing company may analyze employee performance data to create groups such as: - Most and least effective at engineering
- Most and least effective at repair and maintenance
- Most and least effective at assembly

This classification helps identify areas for training or reward programs.

Spotting Something Unusual

In this problem type, data analysts detect anomalies or data points that deviate from expected patterns.
For example, a school district might notice a sudden 30% increase in student enrollment. A deeper analysis could reveal that several new apartment complexes were built in the area, explaining the sudden growth. This insight allows the district to plan resources and staffing more effectively.

Identifying Themes

Identifying themes builds upon categorization by grouping related data into broader concepts.
Returning to the manufacturing example, after categorizing workers by task type, a data analyst could group those categories into higher-level themes such as high productivity and low productivity.
This helps management recognize trends and make decisions to reward top performers or support those needing improvement.

Discovering Connections

This type of problem focuses on finding relationships between different entities or datasets that share similar challenges.
For example, consider a scooter manufacturer facing production delays due to faulty wheels. Further investigation might reveal that the wheel supplier is struggling because of rubber shortages from its own supplier.
By recognizing these interconnected problems and sharing data across organizations, all parties can collaborate on solutions to improve the supply chain.

Finding Patterns

Finding patterns involves using historical data to understand recurring trends and behaviors.
For example, e-commerce companies regularly analyze transaction data to identify purchasing patterns.
They might find that customers buy more canned goods before hurricanes or fewer winter accessories during warmer months. These insights help companies manage inventory efficiently and anticipate customer needs.

Key Takeaway

Each problem type requires a unique analytical approach, but all rely on critical thinking and data interpretation.

The six key problem types every data analyst should master:

  1. Making predictions
  2. Categorizing data
  3. Spotting anomalies
  4. Identifying themes
  5. Discovering connections
  6. Finding patterns

Understanding these foundational problem types equips you to approach real-world business challenges strategically and creatively—turning data into actionable insights.