What Is Diagnostic Analytics?

Key takeaways

Diagnostic analytics involves utilizing specific tools and processes to closely examine the data and the trends it reveals.

• Diagnostic analytics is one of four key approaches to data analytics, alongside descriptive, predictive, and prescriptive analytics.

• A few industries that use diagnostic analytics are health care, human resources, and entertainment.

• You can use diagnostic analytics to gain a better understanding of market trends.

Discover more about diagnostic analytics, including what it is, what it’s used for, and its pros and cons. If you’re ready to begin a career in data analysis, enroll in the Google Data Analytics Professional Certificate, where in as little as six months, you can learn about data structures, Tableau software, data cleansing, data storytelling, and more.

What is diagnostic analytics?

This technique helps you better understand what you're analyzing and interpreting and why specific results may have occurred. Diagnostic analytics involves implementing particular tools and processes to look closely at the data and the trends it reveals. You can learn about existing correlations, what factors could have potentially caused an outcome, and whether anomalies exist within the data set. Ultimately, this information helps you identify problems, find ways to address them, observe how well specific strategies work, and even test hypotheses. 

Descriptive analytics vs. predictive analytics vs. prescriptive analytics

Along with diagnostic analytics, three other types of data analytics exist:

• Descriptive analytics examines what happened rather than why it happened. You would typically use this before diagnostic analytics because once you understand what happened, you can focus on understanding why it happened. An example of descriptive analytics is how a business's sales fluctuated from quarter to quarter.

• Predictive analytics allows you to make predictions based on past outcomes and current data using various modeling techniques, such as machine learning. Because predictive analytics techniques are not as easy to implement, they are not as common as other types of data analytics. However, with the continued growth of artificial intelligence and machine learning, they will become more practical and widespread.

• Prescriptive analytics is another advanced form of analytics that uses artificial intelligence and machine learning to help determine what to do next and identify the best course of action to take. To implement this analytics style, you need significant amounts of data and the proper infrastructure in place.

Read more: What Are Machine Learning Models and How Do You Build Them?

What is diagnostic analytics used for?

Diagnostic analytics helps you understand the reason behind occurrences. What does this look like in practice? Let’s review specific examples of what you can accomplish with diagnostic analytics.

1. Understanding consumer and market trends

Diagnostic analytics lets you understand market trends and why product demand fluctuates between different consumer groups. For example, a business may sell a product that sells well in specific regions within the United States but poorly in others. You can use this information to optimize marketing strategies and gear your efforts toward consumers' preferences.

2. Learning the root cause of issues

You can use diagnostic analytics as a testing tool to identify errors within your technology and machinery. This allows you to more effectively manage your expensive equipment so you can address problems before they become too big and end up costing even more to repair. This type of diagnostic testing can be applied to large equipment and machines, as well as computers, and can help point out the specific cause of the issues.

3. Improving organizational operations

With diagnostic analytics, you can look closely at how effectively the various organizational departments communicate to understand where collaboration is lacking, allowing you to take steps to improve communication. You can also learn about employee satisfaction and concerns to create a better working environment. 

Diagnostic analytics examples: What industries use it?

Several industries benefit from using diagnostic analytics. The following offers a look at industry examples and how they benefit from this type of analytics: 

• Health care: In health care, patient data allows you to establish effective treatments and create a more individualized approach to treating patients. 

• Human resources: Human resources departments use diagnostic analytics to examine why employees leave an organization and whether it’s an issue worth addressing. 

• Information technology: When technical issues arise, diagnostic analytics helps quickly identify why the problem occurred and how to troubleshoot it to get systems back on track.

• Entertainment: The entertainment industry implements diagnostic analytics to learn more about what consumers want to watch, the time of day users watch different programs, and other information to develop strategies to keep viewers interested.

• Marketing and retail: Retailers and e-commerce businesses use diagnostic analytics to learn about consumer preferences, how to influence purchases, and whether customers purchase items in their online shopping carts.

Pros and cons of diagnostic analytics

While diagnostic analytics offers many benefits, some challenges exist when implementing it.

Pros

The ability to perform diagnostic analysis and learn why things happen allows you to develop better plans and correct errors, ultimately improving processes as you move forward. This information also helps reduce the amount of trial and error required when implementing data-driven plans. 

Cons

One of the challenges of implementing diagnostic analytics is that you need someone with the right skills and tools to perform this analysis. This means an increase in upfront costs will be required to enjoy the benefits. Other downsides of this technique are the need for large amounts of data and its shortcomings in developing predictions due to the reliance on historical data.

Understanding correlation and causation

To prevent issues from arising, it's important to differentiate between correlation and causation. During analysis, when observing the relationship between data, consider that just because a relationship is present, it doesn’t mean one variable caused the other. Keeping this in mind is critical when using the data to inform decisions.

How to get started in different types of analytics

If you want to use diagnostic analytics, you have multiple options for developing your data analytics skills. You can opt for the traditional path of earning a college degree to work as a data analyst. By gaining a bachelor’s degree in an area such as data science, computer science, mathematics, or statistics, you can develop the skills employers are looking for.

Alternatively, you can become a data analyst without a degree if you take the time to develop the necessary skills. For example, you can attend a data analytics boot camp. Boot camps are a great way to focus on acquiring the skills you will use on the job in three to six months.

Earning a certification is another option to help verify that you have what it takes to qualify for the job. As an aspiring data analyst, a certification such as the Certification in Business Data Analytics (IIBA-CBDA) will help prove you have the skills employers seek.

Stay current with trends and job opportunities in data analytics

Keep up with trends and job opportunities in data analytics and other career fields by joining Career Chat on LinkedIn. Check out these other free resources, as well:

• Watch on YouTube: Career Spotlight: Data Analyst

• Hear from an expert: 6 Questions with a Microsoft Data Analytics Leader

• Plan your career path: Career Paths in Data Analysis: Decision Tree

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.

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