What is Diagnostic Analytics?

Diagnostic analytics examines data to understand the root causes of events, behaviors, and outcomes.

Data analysts use diverse techniques and tools to identify patterns, trends, and connections to explain why certain events occurred. Its main goal is to offer insights into the factors contributing to a particular outcome or problem.

Diagnostic analytics aims to answer questions like:

• Why did an event or outcome happen?
• What were the key factors that influenced it?
• Were there any anomalies or deviations, and what caused them?
• What correlations or relationships exist?
• How did actions or changes impact the outcomes?

Diagnostic analytics fills the space between knowing what happened—descriptive analytics—and foreseeing potential outcomes—predictive analytics. These insights add context and detail to the data, helping you make more precise choices by fully understanding influencing factors.

What is the purpose of diagnostic analytics?

Diagnostic analytics provides deeper insights into why specific events, outcomes, or behaviors occurred. It aims to reveal the origins and elements that led to particular results.

Some primary objectives of diagnostic analytics include:

• Finding the root cause: Identify the main drivers influencing events, problems, or successes.
• Identifying and resolving issues: By pinpointing the factors that contributed to an issue, you can fix the problems and prevent them from reoccurring.
• Improving processes: Insights can highlight inefficiencies or bottlenecks so you can optimize workflows and operations.
• Evaluating performance: Assess the effectiveness of strategies, campaigns, or initiatives by analyzing what worked well and what didn’t.
• Validating hypotheses: Test your hypotheses against actual data to validate or refine your understanding.
• Assessing data quality: Clean and improve your datasets by analyzing anomalies or inconsistencies.
• Managing risk: Understand the risk of specific outcomes and develop strategies to mitigate them.

You might use diagnostic analytics to meet several objectives, combining it with other data science branches for a well-rounded and accurate understanding of your business.

How diagnostic analytics work

Diagnostic analytics is an iterative process. So, as you uncover insights, you can refine your hypotheses, perform additional analyses, or delve deeper into the data to better understand it.

Here’s a general overview of how the process works.

1. Define the problem or objective: Clearly define the event, outcome, or issue you want to investigate. Understand the context and the questions you need to answer.
2. Data collection: Gather relevant historical data related to the event or outcome. This data can come from various sources, such as databases, spreadsheets, logs, and other repositories.
3. Data preprocessing: Clean and preprocess your data to ensure its quality and reliability. This process might involve handling missing values, removing outliers, and reformatting the data.
4. Exploratory data analysis (EDA): Conduct initial data exploration to understand its characteristics, distributions, and fundamental trends. Data visualization techniques like histograms, scatter, and box plots can help.
5. Hypothesis formulation: Develop hypotheses or initial theories about the factors that may have contributed to the event or outcome. These hypotheses guide your analysis.
6. Statistical analysis: Perform relevant tests and analyses to validate or invalidate your hypotheses.
7. Data visualization: Create visualizations to help illustrate relationships between variables and trends in the data.
8. Anomaly detection: Identify unusual or unexpected patterns in the data that may have influenced the outcome. These anomalies can provide valuable insights into potential factors that contributed to the event.
9. Causal inference: If possible, establish causal relationships between variables.
10. Root cause analysis: Based on the results of your analyses, identify the most likely root causes or contributing factors to the event or outcome. Consider direct and indirect influences.
11. Validation and interpretation: Evaluate the findings against the initial hypotheses and context. Interpret the results to give meaningful explanations as to why the observed event occurred.
12. Communicate insights: Present your findings to stakeholders using clear visualizations, data summaries, and explanations to ensure they’re easily understood.
13. Recommendations and action steps: Based on the insights gained from the analysis, suggest actionable recommendations to address issues, optimize processes, or leverage future opportunities.

Using diagnostic analytics involves applying several techniques. These help you understand the “why” behind different scenarios, usually focused on uncovering relationships you might otherwise miss.

You’ll likely use a combination of techniques to deepen your understanding of the data, give you a better picture, andreach a solid conclusion.

Let’s look at some practical examples of where you might apply diagnostic analytics.

Hypothesis testing

In hypothesis testing you create a hypothesis and test it against the available evidence to help you validate or reject assumptions about relationships between data (A/B testing—a form of hypothesis testing).

Imagine you want to test if changing the color of your website’s “Buy Now” button will increase sales. Your hypothesis could be: “Changing the button color will lead to a higher click-through rate.”

Through hypothesis testing, you collect data on the click-through rates before and after you change the button’s color and statistically analyze if it had a significant impact.

Correlation vs causation

Correlation is a statistical relationship between two variables, while causation implies that changes in one variable directly cause changes in another.

Let’s say there’s a correlation between ice cream consumption and bicycle accidents during summer. This correlation doesn’t mean eating ice cream causes the accidents. Rather, the common factor is the hot weather—more people eat ice cream and ride their bikes on hot days, leading to the correlation.

Diagnostic regression analysis

This technique helps you understand the relationship between variables, identifying influential data points that might affect the regression model’s accuracy.

Let’s say you work for a retail company that wants to understand how advertising spending affects sales. You collect data on advertising expenses and corresponding sales for several months. The diagnostic regression analysis can identify outliers or influential data points that might distort the relationship between advertising spending and sales to refine your understanding of the relationship.

By using diagnostic analytics, you can better understand your operations, outcomes, and processes.

You can move beyond surface-level observation and address problems at their root, reducing the guesswork in decision-making and enhancing the quality of your choices.

With insight into causal relationships, you can create more accurate and adaptable strategic plans and focus your resources on the areas that drive the most impact.

Diagnostic analytics is an essential component of data analytics, but like all things, there are pros and cons to consider.

By examining the positive and negative, you can better understand the impact of diagnostic analytics.

Benefits

With diagnostic analytics, you can determine why certain events happened and fix the main problems causing them. When you know why things happened, you can make better choices later and adjust your plans.

These helpful insights can improve processes, help solve problems, and mitigate risks. Instead of just dealing with the signs of a problem, diagnostic analytics can help you address the real causes behind it.

You can learn from what happened in the past and use that knowledge to make things better in the future.

Disadvantages

Working with large data sets with complex relationships and many variables can be challenging.

It can be tricky to determine why something happened, especially when you’re trying to distinguish it from related events. Just because two events seem connected doesn’t mean one caused the other—this is the causality vs. correlation issue.

Additionally, unaccounted biases or overlooked variables can invalidate conclusions about why something happened, making helpful insights less accurate.

You also need accurate and clean data to get the best diagnostic insights. If the data isn’t reliable, your conclusions might be wrong.

It can also be challenging to find the skills and tools required to perform in-depth research like diagnostic analytics. You might need advanced statistical and analytical tools, which cost money and require training.

Lastly, diagnostic analysis focuses on historical data, which doesn’t necessarily predict future events. For a more complete picture, you should complement diagnostic analysis with other types of analytics.

Diagnostic analytics can be used across industries to gain insights into data to drive informed decision-making.

Let’s explore some of the sectors benefiting from diagnostic analytics.

Human Resources

HR professionals can use diagnostic analytics to understand employee turnover rates, identify factors leading to low employee engagement, and pinpoint the causes of workplace conflicts.

The resulting insights can help you develop strategies to improve employee satisfaction, retention, and overall organizational performance.

Healthcare

Diagnostic analytics can help healthcare practitioners spot the patterns and identify the causes of patient readmissions, analyze the effectiveness of treatments, and detect anomalies in patient data that might indicate health risks.

It contributes to personalized patient care and process optimization in healthcare facilities.

Manufacturing

Manufacturing companies can use diagnostic analysis to identify production inefficiencies, analyze equipment failure patterns, and optimize supply chain operations.

You can enhance productivity and reduce costs by understanding the root causes of defects or delays.

IT

IT departments use diagnostic analysis to troubleshoot and resolve technical issues.

By analyzing system logs, network data, and performance metrics, you can find the causes of system failures, latency, or security breaches, leading to fast problem resolution.

Retail

Diagnostic analysis enables retail leaders to understand customer behaviors, like purchasing trends and factors influencing buying decisions.

These insights can help you optimize product offerings, marketing strategies, and store layouts to better cater to customer preferences.

Descriptive and diagnostic analytics are part of the broader data analytics discipline, each with distinct purposes.

Descriptive analytics answers, “What happened?” while diagnostic analytics goes beyond to address, “Why did it happen?”

You might use descriptive analytics to look at last year’s sales figures for each quarter. You could then apply diagnostic analytics to understand why sales figures decreased in a particular region during a specific quarter.

Descriptive analytics gives you a high-level summary of historical data and trends, while diagnostic analytics delves deeper to understand the reasoning.

Both types are essential for gaining data insights: Descriptive analytics provides context and an initial understanding, and diagnostic analytics offers more comprehensive explanations.

Diagnostic analytics techniques

Diagnostic analytics uses several techniques to uncover underlying causes.

Here are some of the most common:

• Hypothesis testing: Involves formulating a hypothesis about the relationship between variables and using statistical tests to determine if the data supports or rejects the hypothesis.
• Regression analysis: Helps you understand the relationship between a dependent variable and one or more independent variables. It can reveal how changes in the independent variables affect the dependent variable.
• Anomaly detection: Finds unusual or abnormal patterns in the data that might indicate underlying issues. These anomalies can help you uncover outliers or deviations from expected behavior.
• Root cause analysis: Aims to find the fundamental reasons behind an event or outcome. It involves investigating contributing factors to identify the primary cause.
• Correlation analysis: Assesses the strengths and direction of relationships between variables. Although it doesn’t imply causation, it can indicate potential areas for further investigation.
• Cohort analysis: Groups data into segments, also known as cohorts, based on shared characteristics and then compares their behaviors or outcomes over time. It can reveal insights into how different groups respond to changes.
• Factor analysis: Identifies underlying factors that contribute to interrelationships among variables, helping reduce data complexity and find common themes.
• Case-control studies: Compares individuals associated with a particular outcome or case to individuals in a control group who are not to identify factors associated with the outcome. Healthcare companies typically use this technique.
• Time series analysis: Examines data collected at different intervals to find trends, seasonality, and relationships between variables over time.
• Simulation modeling: Uses mathematical or computational methods to simulate real-world scenarios. These simulations can help you understand how variable changes impact outcomes and provide insights into different scenarios.
• Data visualization: Helps data analysts and decision-makers understand complex relationships and patterns by presenting data visually and clearly.

Businesses use tools and software to perform diagnostic analytics. They vary in complexity, capabilities, and cost, so you should select one that best fits your needs and expertise.

Some widely used diagnostic analytics tools are:

• R: A programming language and software environment specifically designed for statistical analysis and data visualization.
• Python: Provides several libraries for data manipulation, analysis, and statistical modeling.
• Tableau: A data visualization tool that helps create interactive and insightful visualizations for diagnostic analysis.
• Microsoft Excel: Used for basic diagnostic analysis tasks such as creating charts, performing correlation analysis, and running regression models.
• SAS: Provides a suite of analytics tools for data analysis, including advanced statistical methods and machine learning capabilities.
• Alteryx: A platform for data blending, preparation, and advanced analytics.
• QlikView: A business intelligence (BI) tool that offers interactive data visualization and analysis features.

You’ll most likely use a combination of tools to perform comprehensive diagnostic analytics, especially if your analysis is complex or has specific, unique goals.

Diagnostic analytics can guide you toward the root causes of your outcomes. Unraveling the “why” behind events and behaviors can enable you to make decisions with clarity and conviction.

However, the right tools are required to reap the benefits of diagnostic analytics. Tools that translate data into actionable insights, illuminate user behavior, and empower organizations to optimize their offerings.

This is where Amplitude Analytics shines. Amplitude Analytics isn’t just another analytics software—it’s a platform that unlocks the full spectrum of diagnostic capabilities.

Amplitude's Root Cause Analysis (RCA) feature is just one example of its diagnostic capabilities. It analyzes the properties of anomalous events for you, while also pulling in external context to potentially explain the anomaly or to help rule out the obvious.

As an all-in-one data analytics platform, it applies diagnostic, predictive, prescriptive, and descriptive analytics to help you garner insights across all areas of your business.

Let Amplitude help you transform your data into a strategic advantage. Explore the power of Amplitude today and embark on a new journey of insightful decision-making.

Get started for free with Amplitude today.

Other Analytics Guides

• What is Predictive Analytics?
• What is Descriptive Analytics?
• What is Prescriptive Analytics?