Most companies are flying blind when it comes to understanding their data. Sure, they have fancy BI dashboards and reports that tell them what happened, but they’re terrible at figuring out why things happen. It’s like having a doctor who can tell you you’re sick but can’t diagnose the illness – these businesses spot problems but can’t find their root causes. They end up treating symptoms instead of the real issues, making decisions based on guesswork rather than solid analysis. As a result, they are oblivious to the real risks all around them. If companies want to stay competitive, they need to get better at diagnostic analytics.

In this article, I’ll first specify what diagnostic analytics is and what answers it can provide to decision-makers. Also, I’ll show you how diagnostic analytics fits within other forms of analytics and how it is underutilized within most businesses and organizations. Further, I’ll detail the reasons why businesses need to use diagnostic analytics over other forms of analytics. Indeed without it, businesses are missing out on crucial insights for better decision-making and staying competitive.

“A correct diagnosis is three-fourths the remedy.”

Mahatma Gandhi

1. What is Diagnostic Analytics and What Answers Can It Provide to Decision-Makers?

Diagnostic analytics examines data to identify the root causes of trends, anomalies, and other issues. To explain it best, I’ll first provide a definition and then detail the types of questions decision-makers ask that are most effectively answered by this form of analytics.

a. Diagnostic Analytics Defined.

Here’s a definition for diagnostic analytics:

“ a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations.”

Gartner

So basically, diagnostic analytics answers the question, “why did it happen”. It does this by examining an event or anomaly, conducts a root cause analysis that results in insights about what happened. To further detail, let’s look at the specific types of questions that diagnostic analytics can answer.

b. Diagnostic Analytics Answers Questions About Why Things Happened and Their Significance.

When leveraging diagnostic analytics, the decision-maker needs to know why something happens and what its significance is. With clear business direction, diagnostic analytics is invaluable at answering questions like:

Questions that Diagnostic Analytics Answers

• Why did an event or outcome happen?
• What caused the issue?
• Where is the problem occurring?
• When did the issue start?
• Who is affected by this problem?
• How widespread or significant is the problem?
• What previous events correlate with the problem?

It is these types of questions that are best answered by diagnostic analytics. Now, let’s look at how diagnostic analytics fits in with other forms of data analytics.

2. How Does Diagnostic Analytics Fit Within Other Forms of Data Analytics?

There are four basic forms of data analytics: descriptive, diagnostics, predictive, and prescriptive. Also, these specific types of analytics rely on historical data. However, the end results of these analyses are not all focused on the past. First, both descriptive and diagnostics analytics have reflective characteristics, centered on what happened in the past. However, predictive and prescriptive analytics are forward thinking, focused on the future. Moreover, each of these different forms of analytics are geared toward answering a specific set of questions. See below, for more details on each form of data analytics and how diagnostic analytics fits within analytics and a business decision cycle.

a. Four Forms of Data Analytics: Their Focus and the Questions They Answer.

To illustrate, let’s break down these four types of analytics by whether they are forward-looking toward the future or reflective of past events. It is also crucial to understand the primary question each form of analytics addresses to fulfill a decision-maker’s information needs. This will help you see where diagnostic analytics fits within other forms of data analytics. See below:

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Different Forms of Data Analytics – Their Focus

• Reflecting on Past Events and Anomalies
  • What Happened? – Descriptive
  • Why Did This Happen? – Diagnostic

• Forward Thinking Toward Future Trends, Events, and Actions
  • What Is Most Likely To Happen? – Predictive
  • What Action Should We Take? – Prescriptive

b. The Analytics Continuum: How Diagnostics and Other Forms of Analytics Supports Decision-Making.

Another useful chart for understanding how diagnostic analytics fits within other forms of analytics is the Gartner’s Analytics Continuum depicted below. This chart shows how each form of analytics supports business decision-making, and subsequent actions. Indeed, at least within businesses, the purpose of all data analytics is to help with better decision-making that lead to superior outcomes aligned with corporate objectives. 

1) Diagnostic Analytics Provides the Root Cause Analysis For Decision-Making.

So, from a business decision-cycle perspective, diagnostic analytics is only one step or stage that helps businesses move toward making a final decision. At the same time, the diagnostic results are critical to make sure the business has identified the right problem, the root cause that needs to be resolved. Hence, per the chart above, at the conclusion of the diagnostic analytics stage (BLUE) , a human (LIGHT GREEN) ideally will have crucial insights about the problem, and now they need to determine what to do with this information. 

2) Root Cause Analysis Provides Direction and Confidence To Support Future Decision-Making.

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Once the diagnostics is complete, the decision-maker can have the confidence to pursue several options. This can include not pursuing the issue further as the event or problem is not that significant to warrant action or more analysis. On the other hand, the decision-maker can opt for more analysis to better understand the problem, or move forward with pursuing a decision on the best action to take.

For more on different forms of data analytics, see my article, A Data Analytics Perspective To Better Empower Managers. Also, this article looks at two more types of analytics, on-demand (real-time) and AI-powered. These types of analytics are more focused on the now versus the past or future like other analytics. Moreover, these types of analytics directly support the decision-maker answering questions like, “What should I do now?” and “what are the answers to the questions I did not ask?”

3. Diagnostic Analytics: Why Is It an Underutilized Decision Tool in Organizations?

Despite its importance, diagnostic analytics is often the missing link in business analytics. Below are three reasons I think diagnostic analytics is under utilized.

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a. Businesses Have Developed a Habit of Performing Superficial Diagnostics.

In many cases, diagnostics is treated more as a passing thought than a deep dive into the root of the problem. For instance, a typical diagnosis of the problem may just consist of a couple of canned reports that amount to “rounding up the usual suspects”.

b. More Technical Advancements in Other Forms of Analytics.

For instance, organizations have made major investments in Business Intelligence (BI) reporting and dashboards. As a result, we have placed increased emphasis on descriptive analytics. Then, lately, we are now obsessed with Artificial Intelligence (AI) which works hand-in-hand with predictive analytics. 

c. Directionless, Inexperienced Analysts.

The primary challenge with diagnostic analytics is that it can become counterproductive without well-defined business objectives or experienced analysts. This type of analysis may inadvertently lead down the wrong path, the “rabbit hole”, making it resource-intensive while yielding limited insights. Therefore, diagnostic analytics can often be an expensive way to satisfy one’s curiosity rather than furthering the business’s interests. 

For more tips to improve an organization’s diagnostic analytics capabilities, see João António Sousa’s article, The Diagnostic Analytics Gap.

“Only the inquiring mind solves problems.”

Edward Hodnett

4. Reasons to Use Diagnostic Analysis.

So, diagnostic analytics is a valuable tool for organizations. To list, below are the benefits of diagnostics analytics for businesses:

• Identify Root Causes of Issues. Here, organizations can pinpoint the underlying reasons for problems, rather than just treating the symptoms.
• Understand Historical Data Trends and Patterns. In this case, diagnostic analytics reveal the “why” behind trends and patterns.
• Reduce Risks and Uncertainties. Uncertainty can occur because of unusual events, problems, anomalies, or outliers. With a clearer understanding of what happened and why, organizations can better mitigate risks and navigate uncertainties.  
• Support Continuous Improvement Initiatives: Also, diagnostic analytics is crucial for assessing areas of inefficiency, bottlenecks, and opportunities for improvement in ongoing operations.
• Validate Hypotheses: Diagnostic analytics allows organizations to test and validate assumptions and hypotheses about their operations, strategies, or initiatives.

“If a tree falls in a forest and no one is around to hear it, does it make a sound?”

George Berkeley

Summary and More References.

So, there is a significant gap in the data analytics capabilities of most organizations. Indeed, most businesses only have rudimentary diagnostic skills. Thus, they are not particularly effective at root cause analysis. Without a doubt, to stay competitive, businesses desperately need to acquire the analytics capabilities to diagnose both problems and opportunities. Indeed without a strong diagnostic analytics capability, businesses are missing out on crucial insights for better decision-making and staying competitive.

For a more detailed discussion of the different types of data analytics used within supply chains, see my article. A Data Analytics Perspective To Better Empower Managers. Also, for more detailed discussion on diagnostic analytics to include specific techniques, see rudderstack’s article What is Diagnostic Analytics? Lastly, see Amplitude’s article, What is Diagnostic Analytics? for a detailed discussion on both diagnostic techniques and tools.

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