What Is Diagnostic Analytics? A Complete Guide

While the internet is awash with breathless claims about the unrivaled power of data, the truth is that data has very little inherent value on its own. It’s what we can learn from data that makes it powerful. This involves mastering not only the tools we need to identify patterns and trends, but also those that help us understand why they occur. 

This is where diagnostic analytics comes in. One of the cornerstones of data analytics, diagnostic techniques are some of the most fundamental skills data analysts use. But precisely what is diagnostic analytics, and how important is it? In this guide, we’ll answer all your questions:

  1. How does diagnostic analytics work?
  2. How is diagnostic analytics used?
  3. Advantages of diagnostic analytics
  4. Disadvantages of diagnostic analytics
  5. Diagnostic analytics use cases
  6. Key takeaways

Ready to dive deep into diagnostic analytics? Let’s jump in.

1. How does diagnostic analytics work?

By summarizing a data set’s characteristics, descriptive analytics—the most basic form of data analytics—helps us identify what has happened. However, to get a more in-depth understanding, we require diagnostic analytics. This involves drilling deeper into data to identify not only what has occurred, but why. This focus on cause and effect is why diagnostic analytics is sometimes known as root cause analysis.

Diagnostic analytics is similar to descriptive analytics in that it also uses historical data. However, its unique feature is that it aims to identify and explain anomalies and outliers. For instance, perhaps a fashion brand sees an unexpected surge in profits. By applying diagnostic analytics, the company can develop and test various hypotheses about why that has happened. Perhaps one of their clothing ranges has been promoted by a celebrity influencer, or maybe it has appeared on a Netflix series. By sourcing and analyzing additional data, they can identify the most likely cause for the profit surge, in turn, informing their future strategy (for instance, by actively pursuing product placement deals with Netflix).

Diagnostic analytics employs various techniques, ranging from probability theory to regression analysis, clustering analysis, filtering, time-series analysis, and more. It’s not just about statistics, though. It involves thinking laterally, considering external factors that might be impacting the patterns in your data, finding additional sources to help you build a broader picture, and then checking these conclusions against the original dataset.

2. How is diagnostic analytics used?

Much like descriptive analytics, which also focuses on retrospective data, diagnostic analytics has many possible applications. In one way or another, practically all industries and disciplines use it. For example, diagnostics analytics can be used by:

  • Sales teams—to determine why a company’s profits are dropping or growing.
  • Marketing teams—to figure out why a website has seen a traffic increase.
  • IT—to diagnose technical problems within a company’s digital infrastructure.
  • HR—to understand the factors contributing to why employees may leave a company.
  • Big pharma—to evaluate the effectiveness of different drugs.
  • Hospitals—to understand why patients are admitted for particular ailments.

You can apply diagnostic analytics to pretty much any type of data you can imagine. If you’re in a situation where you want to know why something has occurred, and you have a suitable dataset from which to draw conclusions, you can use diagnostic analytics. But this begs a question: why exactly is it so beneficial? Let’s find out.

3. Advantages of diagnostic analytics

By now, we understand what diagnostic analytics involves, and how companies use it. But why is it so commonly used? Here are the main advantages of diagnostic analytics:

  • By drilling down into data, you can obtain more granular insights than via descriptive analytics alone.
  • Diagnostic analytics lets you form (and test) hypotheses using hard evidence about what happened in the past.
  • By comparing input and output data, you can determine whether data points are merely correlated or if they represent a clear cause and effect.
  • By identifying outliers and anomalies, you can determine whether they represent significant findings or merely erroneous data.
  • Understanding what triggered past events means that you can avoid repeating costly mistakes—or, conversely, repeat actions that led to unexpectedly positive outcomes.

Diagnostic analytics is more complex than descriptive analytics. But there are a growing number of platforms available specifically geared towards helping organizations conduct data-driven diagnostics. Read about some of these data analytics software tools here.

4. Disadvantages of diagnostic analytics

As a data analyst, using diagnostic analytics is unavoidable. It’s crucial, then, to understand not just its benefits but its shortcomings. The main drawback of diagnostic analytics is that it relies purely on past data. This has several knock-on effects, including:

  • Necessary limits on its ability to draw conclusions about possible future events.
  • A need to supplement your analysis with additional sources, including real-time data and third-party historical data.
  • It requires more time and higher-level skills than descriptive analytics (although, as mentioned in the previous section, new platforms are emerging to mitigate this issue).
  • It’s easy to mistake correlation for causation, which can have costly impacts.

Despite these drawbacks, diagnostic analytics can be a powerful tool. To demonstrate what we mean, let’s explore a few use cases.

5. Diagnostics analytics use cases

In reality, diagnostic analytics—along with descriptive, predictive, and prescriptive analytics—rarely sits alone. Instead, it’s one ingredient in the proverbial soup of analytical techniques. That said, its anomaly detection capabilities are unrivaled. With this in mind, let’s explore some typical use cases for diagnostic analytics.

Marketplace analysis

The most common use of diagnostic analytics is marketplace analytics. While descriptive analytics can summarize metrics like a company’s profit, sales, and other industry data, diagnostic analytics helps compare and correlate these data to identify market trends.

Companies might determine, for example, which past products have been most financially successful. By looking at things like when the product launched, which demographic it targeted, and how much it cost, companies can gain invaluable insights into why previous products succeeded and how they might replicate this success.

Financial fraud and insurance

Diagnostic analytics is vital to detecting financial fraud. For example, if a credit card company detects an unexpected overseas transaction, diagnostic analytics can spot this outlier behavior, alerting the credit card provider to the issue. The user’s card will often be blocked or suspended until the provider can determine whether or not the transaction was fraudulent.

Similarly, insurance providers can uncover common factors in burglaries by analyzing theft claims. For instance, a surge of break-ins may occur in a particular location. Other common factors could be unlocked windows and doors. Identifying at-risk customers allows insurance providers to alert these homeowners; nudging them to be more vigilant, possibly leaving lights on when they leave the house, locking doors, and so on. This can reduce break-ins and costly insurance payouts.

Healthcare analytics

Within healthcare, diagnostic analytics finds a very literal application—disease diagnosis. Doctors are highly trained, but also very busy. Diagnostic algorithms can correlate symptoms (such as a rash, sore throat, inflammation) against known diseases. This can provide doctors with possible illnesses to narrow down their work.

While the outcome of these diagnostic algorithms may not be 100% accurate, that’s not the point. Instead, diagnostic tools allow doctors—who are better at detecting the nuances of disease than algorithms—to focus on a smaller handful of possible diagnoses. This makes it easier for them to diagnose the correct illness. The result is a more efficient clinical process, freeing doctors to diagnose other patients while ensuring that existing ones receive the care they need.

You can learn more about the other applications of data analytics within the field of healthcare in this article

6. Key takeaways

So there we have it, all the key facts you need about diagnostic analytics! In this introduction, we’ve learned that:

  • Diagnostic analytics involves drilling down into historical data to identify why something has happened.
  • Diagnostic analytics is excellent for exploring anomalies and outliers and identifying correlation, cause, and effect.
  • Diagnostic analytics employs various techniques from probability theory to regression, clustering, and time-series analysis, to name a few.
  • The main advantage of diagnostic analytics is that it provides more granular insights than descriptive analytics (which merely summarizes data).
  • One disadvantage of diagnostic analytics is that it’s possible to mistake correlation for causation, skewing your insights.
  • Diagnostic analytics has many uses in fields like finance and insurance, healthcare, and market research.

The most important thing to know is that diagnostic analytics rarely stands alone. Instead, it’s part of a broader arsenal of techniques that all contribute to the broader field of predictive analytics. 

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