Data analysis is an essential tool for many businesses today. However, one of the most valuable aspects of data analysis is the second branch, known as diagnostic analytics.
This process is highly used by many companies and organizations because analyzing data answers a very important question, which is why something has happened. This is a crucial resource for a lot of companies, as it helps diagnose problems in business operations.
Table of Contents
- The Definition of Diagnostic Analytics
- How Diagnostic Analytics Works
- The Benefits of Diagnostic Analytics
- Examples of Diagnostic Analytics
By looking at data, diagnostic analytics determines cause and effect. This valuable insight identifies why a particular issue has occurred by making a hypothesis and detecting patterns.
By looking into variables, diagnostic analytics identifies specific trends and correlations and hypothesizes about the possible causes behind them. The techniques often used in diagnostic analytics are data mining, data discovery, correlations, and drill-down.
Diagnostic analytics can show why a company’s performance is rising or declining, which can then inform the formation of new approaches.
By studying why a company is successful or encounters issues, it can identify the actual cause and then attempt to correct its course or replicate it. Therefore, businesses can better plan to achieve their desired outcomes and experience more informed decision making with business analytics.
As mentioned earlier, there are a lot of techniques involved in diagnostic business analytics. Let’s take a look at how some of them actually work.
The first part of the diagnostic process detects anomalies within the data. After doing that, a business analyst would proceed to look deeper into the data involved within the occurring anomaly.
Data drill-down (or data drilling) is the process of looking deeper into specific data to uncover more detailed information about it. This process aids the data discovery process, which analysts use to detect trends between different data sets.
This is different from data mining, which looks more into finding associations between different data sets. This is how a data analyst identifies patterns in the data.
After finding relationships, a data analyst looks into trying to identify what types they are, with the ultimate goal being to be able to determine cause and effect.
For example, what is the cause of a particular anomaly or the causal relationship? Through correlation analysis, the data analyst investigates how different variables are connected to each other and with the data, they find the causal relationship.
If an analyst determines that two variables are correlated, it doesn’t mean that he or she can immediately determine the cause. As a matter of fact, finding that two variables are correlated does not indicate that one might have caused the other to occur. However, a correlation might provide critical information that can lead a business analyst toward finding the real cause of the anomaly.
An analyst uses different hypotheses to better understand what might have caused an anomaly. Hypothesis testing is a method of statistics that uses data to prove or disprove a hypothesis. It is basically the process of elimination by testing what different hypothesized scenarios do not apply to this case.
Hypothesis testing helps analysts maintain their focus on their ultimate goal and guide them toward finding the cause.
Diagnostic analytics can help businesses grow by learning from their mistakes. It allows a company to identify the reasons behind some of its past issues and failures and correct its course. It also gives helpful insight into where the company might lack.
For example, suppose a business conducts a data analysis about the performance of each of its departments and sees that a specific department does not perform exceptionally well. It might be possible to determine that the department lacks funding or requires more human resources through a diagnostic analysis.
The only drawback of diagnostic analytics is that it is based on historical data, meaning that it cannot predict issues that might occur in the future but only what has already happened.
Examples of Diagnostic Analytics
In marketing, diagnostic analytics are used to show the performance of electronic ads and can identify why they either perform well or below a company’s expectations. For this reason, diagnostic analytics is often used for social media.
In retail, diagnostic analytics can be used to identify the reason why the purchases of a particular product might have significantly risen during a certain period of time.
For example, through diagnostic analytics, the declined sales of a frozen pizza brand at a grocery store might be attributed to opening of a new pizzeria in the same area if the data shows that the formerly frequent customers who stopped buying the product all reside in the same area.
In manufacturing, diagnostic analytics is used to determine the cause of failure in production machines.
Most frequently, in finance, diagnostic analytics are used to investigate the sales performance of businesses and identify the reasons behind revenue growth or decline.
In human resources, diagnostic analytics is used, especially in large corporations, to identify staffing shortages and the need for extra funding when the performances of specific departments are studied.
Similarly, in healthcare, diagnostic analytics are used to determine the quality of medical diagnosing. For example, diagnostic analytics could look into the number of patients admitted during a specific period in a hospital, and the number of patients eventually readmitted.
Through that, the results of this analytical approach could show that the patients all had the same condition, which could mean a number of different things such as misdiagnosis, insufficient treatment plan, and lack of medical staff that have experience with that specific condition, etc.
Diagnostic analytics is a significant brand of data analysis that enables businesses to engage in problem-solving practices.
This is because this branch of data analytics specializes in determining the cause behind a problem. By diagnosing an issue, businesses are better equipped to address them as they know what they deal with.
Diagnostic analytics is perhaps one of the most commonly used types of data analysis, as it is prevalent in several industries such as retail, manufacturing, human resources, marketing, healthcare, finance, and much more.
Moreover, diagnostic analytics is a statistical method used to help companies improve their performance by detecting the cause behind the issues that surface in their operations and allowing them to form better operational strategies and actionable insights based on the provided data.