What Is Diagnostic Analytics? How It Works and Examples

Determining the root causes of trends and events is critical to business success. Diagnostic analytics is designed to achieve that goal. It helps businesses delve into data to understand what’s driving trends and anomalies, such as an unexpected drop in revenue, a shift in customer behavior or an increase in expenses. As a result, companies can make more-informed decisions about how to solve problems and achieve greater business success.

What Is Diagnostic Analytics?

Diagnostic analytics is a branch of analytics that aims to answer the question, “Why did this happen?” By using diagnostic analytics, companies can gain insights into the causes of patterns they’ve observed in their data. Diagnostic analytics can involve a variety of techniques, including data drilling and data mining. To investigate the root cause of trends, companies may need to examine additional data sources, potentially including external data.

Key Takeaways

• Diagnostic analytics is a type of analytics that helps companies answer the question, “Why did this happen?”
• Diagnostic analytics enables companies to make more-informed decisions about how to remediate problems and drive continued success.
• Diagnostic analytics can use a variety of techniques, including data drilling, data mining and correlation analysis.

Diagnostic Analytics Explained

Diagnostic analytics is one of the four primary types of business analytics, along with descriptive, predictive and prescriptive analytics. Together, these analytics approaches help companies better understand and forecast business performance.

• Descriptive analytics is usually the first step in analyzing data. By examining data often stored in a data warehouse, then summarizing and highlighting trends in historical trends on source systems beyond the data warehouse with multi-source analysis, you can try to answer the question, “What happened?” Financial and other business reports are examples of descriptive analytics.

• Diagnostic analytics then examines the causes of those trends, helping companies understand why they occurred. For example, if the latest sales report shows a better-than-average increase in sales, the company can drill down into internal sales data to see whether specific customers or new products were responsible for the increase. Diagnostic analytics can also look at external data about weather patterns or competitors’ activities.

• Predictive analytics looks at how trends might unfold in the future and their potential impact.

• Prescriptive analytics suggests actions to respond to those future trends and improve business outcomes.

Importance of Diagnostic Analytics

Every company can benefit from gaining a better understanding of its business performance in order to replicate its success and fix any problems. Diagnostic analytics helps companies better understand the internal and external factors that affect its outcomes. It paints a more comprehensive picture of each situation, which helps businesses make better decisions. For example, if the company determines that a specific online marketing campaign is responsible for higher sales of a key product, it can direct more resources to that campaign and create similar campaigns for other products.

How Does Diagnostic Analytics Work?

Diagnostic analytics uses a variety of techniques to provide insights into the causes of trends. These include:

• Data drilling: Drilling down into a dataset can reveal more detailed information about which aspects of the data are driving the observed trends. For example, analysts may drill down into national sales data to determine whether specific regions, customers or retail channels are responsible for increased sales growth.

• Data mining hunts through large volumes of data to find patterns and associations within the data. For example, data mining might reveal the most common factors associated with a rise in insurance claims. Data mining can be conducted manually or automatically with machine learning technology.

• Correlation analysis examines how strongly different variables are linked to each other. For example, sales of ice cream and refrigerated soda may soar on hot days.

Three Diagnostic Analytics Categories

The diagnostic analytics process of determining the root cause of a problem or trend typically comprises three primary stages.

1. Identify anomalies: Trends or anomalies highlighted by descriptive analysis may require diagnostic analytics if the cause isn’t immediately obvious. In addition, it can sometimes be difficult to determine whether the results of descriptive analysis really show a new trend, especially if there’s a lot of natural variability in the data. In those cases, statistical analysis can help to determine whether the results actually represent a departure from the norm.

2. Discovery: The next step is to look for data that explains the anomalies: data discovery. That may involve gathering external data as well as drilling into internal data. For example, searching external data might reveal changes in supply chains, new regulatory requirements, a shifting competitive landscape or weather patterns that are associated with the anomalous data.

3. Causal relationships: Further investigation can provide insights into whether the associations in the data point to the true cause of the anomaly. The fact that two events correlate doesn’t necessarily mean one causes the other. Deeper examination of the data associated with the sales increase can indicate which factor or factors were the most likely cause.

What Does Diagnostic Analytics Tell Us?

Diagnostic analytics goes beyond simply illustrating key trends and patterns and seeks to identify their causes. By highlighting the factors that drive trends, diagnostic analytics helps companies better understand variations in business performance and customer behavior. For example, diagnostic analytics can help companies understand why customers buy specific products. Companies can use this information to fine-tune marketing campaigns targeting those customers.

Benefits and Drawbacks of Diagnostic Analytics

Understanding the causes of business outcomes is critical to a company’s ability to grow and learn from mistakes. Diagnostic analytics lets companies zero in on the factors that drive success or cause failure, including contributing factors that may not be obvious at first glance. Diagnostic analytics can help to instill a data-driven analytical culture throughout the business. When business leaders understand that the company has the tools to investigate the cause of problems, they’re more likely to use diagnostic analytics in their decision-making. For example, if a problem in on-time deliveries is identified and further supply chain analysis reveals disruptions and unpredictable lead times, managers may choose to increase the inventory on hand to meet customer demand.

A drawback of diagnostic analytics is that it focuses on historical data; it can only help businesses understand why events happened in the past. In addition, further investigation may be needed to determine whether the correlations revealed by diagnostic analytics really show cause and effect. To look into the future, businesses need to use other analytic techniques, such as predictive analytics, which examines the potential future impact of trends and events, and prescriptive analytics, which suggests actions businesses can take to influence the outcome of those future trends.

Use Cases of Diagnostic Analytics

Every department within an organization can benefit from analyzing root causes of events in order to improve its processes and outcomes. For example, companies can use diagnostic analytics to investigate the cause of:

• A sudden decline in revenue or increase in expenses
• The popularity of a product or service
• An increase in employee turnover
• Bottlenecks in production or distribution

Diagnostic Analytics Examples

Diagnostic analytics can be helpful in any industry, from manufacturing and retail to health care. After applying diagnostic analytics to discover why an event occurred, companies can use that knowledge to create solutions and develop predictive models for the future.

Health care:

Diagnostic analytics can support many areas of health care, including the core function of diagnosing medical problems. For example, descriptive analytics can answer questions like, how many patients were admitted to the hospital last month? And how many returned within 30 days? After all, in some cases reimbursement may be dependent in part on readmittance rates. Descriptive analytics can quantify events and highlight things like what hospital resources are being used and even model the rate of disease diagnosis. By comparing that data with historical trends, anomalies can be detected, and then the discovery work trying to find causal relationships can begin. For example, did high readmittance rates coincide with a change in rounding policy, or what time of day patients are sent home? Regardless, the first step to prescriptive analytics to solve issues is to uncover the anomalies.

Retail:

A store that sells eco-friendly products noticed a recent surge in revenue from one state. During discovery, the company learned that the surge was driven by a leap in sales of a single product — a canvas tote bag. Research revealed the causal relationship: the state’s governor had signed a law making plastic shopping bags illegal, causing sales of reusable bags to soar.

Manufacturing:

A contract manufacturer found that a valuable type of machine started experiencing intermittent failures. By using diagnostic analytics to examine the machines’ logs, the company discovered that routine software updates had been installed the previous day. It identified the update as a likely cause of failure. It verified the cause by uninstalling the software, which eliminated the problem.

Human resources:

A company’s annual hiring report showed that one department hired more people than any other department — but there was no net increase in the department’s staff because it was losing people as fast as it hired them. Drilling down into the data revealed that many of the positions were for a specific team, which paid its staff less than the industry average. The company used the information to examine pay scales, interview employees and take other measures to improve retention.

Conclusion

Diagnostic analytics is an important element in any company’s analytics portfolio. It takes analysis beyond simply highlighting trends and anomalies, helping companies understand why they occurred. By providing vital insights into root causes, diagnostic analytics and a data warehouse enable companies to make more-informed decisions to fix problems and improve business performance.

Diagnostic Analytics FAQs

Q: What are diagnostic analytics?

A: Diagnostic analytics is a type of advanced analytics that attempts to determine the cause of trends and anomalies observed in the company’s data. Techniques used in diagnostic analytics include drilling down into specific parts of the data, data discovery to find other internal and external data that may reveal the cause of the problem, and data mining to find relationships within the data.

Q: What are the purposes of diagnostic analytics?

A: The purpose of diagnostic analytics is to determine the root cause of an occurrence or trend. Often, a trend is identified using an earlier descriptive analysis step. The company can then apply diagnostic analytics to understand why the trend occurred. This equips the company with a more complete picture of the situation, so it can make more-informed decisions about how to respond to the trend.

Q: What are the 4 types of analytics?

A: Businesses use four main types of analytics: descriptive, diagnostic, predictive and prescriptive. Descriptive analytics is the most common and basic type: it highlights trends and other occurrences but doesn’t examine their causes. Diagnostic analytics seeks to help the company understand why those trends occurred. Predictive analytics examines potential future trends and their impact. Prescriptive analytics suggests actions the company can take to influence the outcome of those trends.

Q: How do you perform a diagnostic analysis?

A: Diagnostic analysis can use a variety of techniques to provide insights into the cause of events or trends. Drilling down into data sets can provide a more detailed view of exactly which aspect of the data was responsible for the trend. Drilling across other data sets can help the company discover other information that may explain the trend. Data mining can manually or automatically reveal patterns and associations within the data.

Q: What is meant by diagnostic?

A: The term diagnostic is in reference to the process of discovering the type or cause of an illness or problem. In business it refers to the process of uncovering why an event occurred, such as a sudden drop in sales or high employee turnover.

Q: What industries use diagnostic analytics?

A: Diagnostic analytics is useful in most any industry. From retail to health care, and manufacturing to service-based, diagnostic analytics helps companies create solutions and develop predictive models for the future.