Data analytics is about solving problems. Right in the first step where we Ask, we want to understand where the pain is. We need to ask questions. Typically data analysts work with six different problem types. After we’ve identified some problems or challenges you’ll need to ask the right questions.

1. Making Predictions (what will happen?)
2. Categorizing Things (grouping similar things)
3. Spotting something unusual
4. Identifying Themes (common concepts)
5. Discovering Connections (correlation? causation?)
6. Finding Patterns (correlation?)

Making Predictions

Using data to make informed decisions about how things may be in the future. Hospitals may want to predict patient levels by looking at population age. What would be the best advertising method to reach our potential customers?

Categorizing Things

Grouping data based on common features. Perhaps you want to put items into low medium and high categories. In Excel, you might use IF statements. In DAX you’d create a calculated column. You might think about using time to categorize customers by their age. How about days to delivery as slow medium and fast. What about categorizing employee performance? Analysts might classify customer service calls or surveys based on certain keywords or scores. You might have data on social media where you look at key words and determine the satisfaction level (category) of the customer.

Spotting something unusual

Sudden changes to rates. Sales are up or down. Hospital visits are up or down. Crime is very high in a certain part of the city and almost non-existent in another. Analysts may notice that diseases are much higher in a certain geographic area than in the rest of the country (Movie: Erin Brokovich). Smart watches give notifications of unusually high heart rates. Other medical equipment in hospitals do the same and much more.

Identifying Themes

Identifying themes takes categorization a step further by grouping information into broader concepts. 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. Categorizing things involves assigning items (or observations) to categories; identifying themes takes those categories a step further by grouping them into broader themes (categories of categories). There are many examples of this in the user experience field. User experience designers study and work to improve the interactions people have with products they use every day. Marketing research. What do customers really want?

Discovering Connections

Identifying similar challenges across different entities—and using data and insights to find common solutions. A company may find that a product has become unavailable. They may find that their supplier’s supplier has had trouble getting enough product because of a huge fire in a manufacturing plant in China. New cars may become scarce due to a shortage of computer chips. Optimizing scheduling falls in this category. How can we reduce wait time?

Finding Patterns

Using historical data about what happened in the past to understand how likely it is to happen again. Consumer spending on certain products changes when weather predictions and weather events happen. What can we learn about consumer buying habits? How about looking at manufacturing machine maintenance and failure times. Is there a pattern? Do break downs happen when maintenance falls behind schedule?