What Is Diagnostic Analytics? How It Works and Examples

Data analytics uses data to provide insights into all types of business situations. Diagnostic analytics is a subset of data analytics that helps uncover the root causes of a specific event. It answers the question, “Why did this happen?” When you understand why something happened—whether it was a positive or negative event—you can make strategic decisions going forward.¹

Other types of data analytics include descriptive analytics and predictive analytics. Descriptive analytics answers the question, “What happened?” Predictive analytics answers the question, “What will happen?” Combined, these forms of data analytics can give you deep insight into what’s happening in your business and what steps you need to take to improve outcomes.

This article will explore diagnostic analysis, including how it’s used and examples.¹

How Diagnostic Analytics Works

Businesses use diagnostic analytics to identify and resolve problems, improve processes, evaluate performance, test hypotheses, and manage risks. It’s an iterative process, so as you uncover insights, you can use them to refine your hypothesis and further explore your data.²

You’ll start by clearly stating the problem or issue you want to examine. It’s important to include the context and related questions you need to answer. Then you’ll determine the best sources to collect data and how to process it effectively. An exploratory analysis will help you understand your data better and decide what methodologies you should use in your analysis:²

• Root cause analysis is one of the most common methodologies, in which you identify the most likely contributing factors to an event
• Anomaly detection allows you to find unusual patterns in data that might indicate issues or unexpected behavior
• Regression analysis helps you understand relationships between independent variables and dependent variables, so you can see how changes in one affect the other 

You can use a combination of tools to implement these methodologies. Programming languages such as R and Python allow you to create statistical models, manipulate data, and create data visualization. Tools like Tableau help you create intuitive data visualizations, so stakeholders can make sense of data. You can also use tools such as Microsoft Excel and SAS to perform diagnostic analytic computations.²

Applications of Diagnostic Analytics

Almost every industry and department can benefit from diagnostic analytics. When leaders understand why an event occurred, they can use that information to repeat it or avoid it in the future. Some business use cases include the following:³

• A retail operation may find that an increase in sales of a specific product was driven by a new regulation; its leaders can use that information to market specifically to customers who are buying their product for that reason 
• In healthcare, diagnostic analysis can help determine why a hospital has a high readmission rate; it might uncover that patients aren’t being adequately instructed on how to manage their condition after discharge
• Manufacturers can use diagnostic analytics to optimize their processes and reduce wasted effort

Benefits of Diagnostic Analytics

With diagnostic analytics, you can home in on the factors driving successes and failures, especially those that may not be obvious at first glance. Getting to the root cause of problems allows you to address them more effectively. It also makes it easier to promote a data-driven culture in your organization. When business leaders know that they can use analytical tools to derive insights into performance issues, they’re more likely to make use of those tools. This will lead to better decision-making, increased efficiency, and reduced costs.³

Challenges and Limitations

 
Despite its many advantages, there are also significant challenges and limitations to diagnostic analysis. If your data is incomplete or inaccurate, you can’t rely on the conclusions. Flawed insights lead to flawed decision-making. It’s also only effective if the data needed to analyze the issues is available to you.⁴

Diagnostic analytics is a complex challenge, requiring considerable skill and expertise. Smaller businesses may not have people who are qualified to perform data analytics. Another issue can be biases in your data or in the interpretation of it. Businesses need to implement strong ethical guidelines for how they collect, process, and analyze data to protect data privacy and eliminate biases.⁴

How to Get Started with Diagnostic Analytics

The first step in implementing diagnostic analytics in your organization is to identify an area you want to improve. Look at areas such as marketing, customer service, or sales to find problems to address. Then use diagnostic analytical methods to determine the root causes. At this point, make sure you’re differentiating between causation and correlation. Causation involves one event causing or affecting another. Correlation occurs when two events occur together or one after the other. Just because one event occurs after another doesn’t mean the first one caused the second.⁵

Use what you’ve learned to create a strategic action plan. When you identify the root causes of problems, develop a plan to mitigate them. After you’ve implemented your plan, you’ll need to monitor your results carefully. You’ll get the best result from diagnostic analytics if you develop an iterative process and continue to use it. This allows you to make adjustments quickly to refine your strategy.⁵

You can use a combination of tools and platforms to simplify the process of diagnostic analytics. Business intelligence platforms such as Tableau, Power BI, and Qlik Sense provide tools you can use to perform diagnostic analytics using your historical data. You can also use statistical analysis to create models with tools such as SAS and programming languages such as R and Python.⁶

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