The Six Data Problem Types

 

Data analysts typically work with six problem types:

Making Predictions

• Using data to make an informed decision about how things may be in the future.

• A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions.

• Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can't guarantee future results, but they can help predict the best placement of advertising to reach the target audience.

Categorising Things

• Assigning information to different groups or clusters based on common features.

• An example of a problem requiring analysts to categorize things is a company's goal to improve customer satisfaction. 

• Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.

Spotting Something Unusual

• Identifying data that is different from the norm.

• A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual.

• Analysts who have analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn't trend normally.

Identifying Themes

• Grouping categorised information into broader concepts.

• User experience (UX) designers might rely on analysts to analyze user interaction data. 

• Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. 

• Themes are most often used to help researchers explore certain aspects of data. In a user study, user beliefs, practices, and needs are examples of themes.

Discovering Connections

• Finding similar challenges faced by different entities and combining data and insights to address them.

• A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. 

• By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.

Finding Patterns

• Using historical data to understand what happened in the past and therefore likely to happen again.

• Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data.

• For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.