27  Applying the Six Common Problem Types

As a data analyst, you’ll frequently encounter a variety of problem types. Understanding how to recognize and approach these problems helps you select the right tools and methods for effective analysis. Let’s look at six common types of problems data analysts solve—using real-world examples that demonstrate their application in business and everyday life.

Making Predictions

Making predictions involves using existing data to forecast future outcomes.
Consider the example of Anywhere Gaming Repair, a company that wanted to attract new customers. Their problem was identifying the best advertising method for their target audience.
By analyzing past data on where and how customers discovered their services, the company was able to predict which advertising channels—such as social media, search ads, or gaming podcasts—would bring the most customers.
While no one can see the future, data helps businesses make informed, evidence-based predictions that increase their chances of success.

Categorizing Things

Categorizing means grouping information into logical clusters based on shared traits.
Imagine a customer service team that wants to improve satisfaction scores. Data analysts could review recorded customer calls and identify keywords or phrases like “helpful,” “confusing,” or “satisfied.”
They could then categorize these terms into themes such as politeness, empathy, and dissatisfaction.
By doing so, the company can pinpoint its top-performing representatives and provide additional training to those who need improvement—leading to better customer experiences and higher satisfaction rates.

Spotting Something Unusual

Spotting something unusual involves detecting anomalies or outliers in the data.
For instance, smartwatches and fitness trackers collect data about heart rate, sleep, and activity. When a user’s resting heart rate suddenly spikes far above normal levels—say from 70 to 120 beats per minute—the system recognizes this as unusual.
In one real-life case, this alert prompted a woman to seek medical help, leading to the early detection of a serious condition.
This example shows how data analysis can not only spot irregularities but also prevent critical outcomes by triggering timely interventions.

Identifying Themes

Identifying themes involves grouping insights into broader, conceptual categories.
For example, a user experience (UX) designer wants to improve a company’s coffee maker. After reviewing hundreds of user survey responses, the designer looks for recurring feedback.
He notices a common issue: many users can’t easily tell whether the coffee maker is turned on or off.
By identifying this recurring theme, the designer recommends adding an illuminated on/off button, improving usability and customer satisfaction.
This illustrates how identifying themes enables companies to find actionable insights hidden within large sets of qualitative data.

Discovering Connections

Discovering connections means uncovering relationships between separate entities or datasets.
A great example comes from the transportation and logistics industry.
Third-party logistics companies often face delays when picking up shipments that aren’t ready on time. By sharing data between shipping partners, both sides can identify where inefficiencies occur—such as mismatched delivery schedules—and coordinate their operations.
For instance, if one company only ships on Mondays, Wednesdays, and Fridays, while the other ships on Tuesdays and Thursdays, aligning their schedules can dramatically reduce wait times and costs.
This collaborative approach creates value for both businesses and customers.

Finding Patterns

Finding patterns involves analyzing historical data to detect recurring trends or relationships.
In the oil and gas industry, data analysts review maintenance logs to determine when machinery tends to fail.
They discover that equipment breakdowns increase significantly when maintenance cycles exceed 15 days.
By recognizing this pattern, the company can schedule preventive maintenance, reduce downtime, and improve efficiency.
Patterns like these help analysts anticipate problems before they occur, saving time and resources.

Key Takeaway

Each of these six problem types—
1. Making predictions
2. Categorizing data
3. Spotting anomalies
4. Identifying themes
5. Discovering connections
6. Finding patterns

—represents a core aspect of analytical thinking.
By mastering these approaches, you’ll be able to transform raw data into meaningful insights that help businesses make better, evidence-based decisions.