Data analytics is so much more than just plugging information into a platform to find insights. It is about solving problems.
To get to the root of these problems and find practical solutions, there are lots of opportunities for creative thinking.
No matter the problem, the first and most important step is understanding it.
1. Making predictions
Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can help predict the best placement of advertising to reach the target audience.
2. Categorizing things
An example 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.
3. Spotting 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.
4. Identifying themes
UX designers might rely on analysts to analyze user interaction data. Usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. In a user study, user beliefs, practices, and needs are examples of themes.
Categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.
5. Discovering connections
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.
6. Finding patterns
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.