What Is Diagnostic Analytics?

Although immense amounts of data are accessible today, it needs to be analyzed so that it can impact decision-making. One approach to accomplishing this is diagnostic analytics, a technique that helps guide businesses and organizations in several industries, with applications in information technology, health care, retail, and more. Read on to learn more about this analytics technique.

What is diagnostic analytics?

This technique lets 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 closer 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. 

3 other types of data analytics

Along with diagnostic analytics, three other types of data analytics are descriptive, predictive, and prescriptive.

• 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 then turn your focus to understanding the why. 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. With the continued growth of artificial intelligence and machine learning, it 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.

What is diagnostic analytics used for?

Diagnostic analytics helps you understand the “why” behind occurrences. What does this look like in practice? Let’s review specific examples of what you can accomplish from 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 closer 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. 

What industries use diagnostic analytics?

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 the most 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 to quickly identify why the problem occurred and how to troubleshoot the issue 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 this technique. 

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 diagnostic 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 bootcamp. Bootcamps are a great way to focus on acquiring the skills you will use on the job in a matter of months, with bootcamps typically lasting anywhere from three to six months. 

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

Read more: How to Become a Data Analyst (with or Without a Degree)

Getting started with Coursera

On Coursera, you will find numerous highly-rated data analytics courses. Introduction to Data Analytics from IBM covers the steps of the data analysis process, from collection to visualization, and introduces you to various careers within data analytics. You can also earn a Google Data Analytics Professional Certificate, which allows you to develop critical analytical skills, including using R programming to perform data analysis.