FAQ: What Is Diagnostic Analytics? (With Examples)
By Indeed Editorial Team
Published 23 October 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data analytics examine information to identify trends, answer questions and extract insights. There are four main types, which are descriptive, diagnostic, predictive and prescriptive. Learning about diagnostic analytics can help you understand how to apply it and how to use data to determine why an event happened. In this article, we define diagnostics analytics, discuss how it works, provide its benefits and disadvantages, outline its process and highlight examples.
What is diagnostic analytics?
Diagnostic analytics is a type of data analytics that helps professionals determine why something happened. It examines trends and correlations between variables to determine the root cause. Diagnostics analysis is a logical next step after descriptive analytics establishes what happens. There are multiple ways to conduct these diagnostics, including performing it manually, using an algorithm or applying statistical software, such as a spreadsheet application.
How does diagnostic analysis work?
Diagnostic analysis works through various techniques that provide insight into the causes of specific events. These methods can consist of the following:
This is the practice of searching through large data volumes to find patterns and relationships in them. It converts raw data into useful information. For example, an insurance company uses data mining to understand factors that cause an increase in insurance claims. Data analysts can conduct this technique manually or automatically using machine-learning technology.
This method involves conducting a deeper analysis of the specific data layers or information you're analysing. It reveals more details on the aspects of data that influence the trends. This technique analyses relational, tabular and multi-dimensional data. For example, a national company can conduct data drilling on its sales information to determine if specific customers, retail channels or regions influence its growth.
This analyses the relationship between the directional movement of two or more variables, which can have either a positive or negative correlation. A positive correlation is when an increase in one item's value causes the same effect on another item. Variables have a negative correlation when the value of one increases as the other decreases. Correlation differs from causation because if two variables have a relationship, it doesn't always mean one instigated the other.
What are the advantages of diagnostic analysis?
There are many benefits of using this analysis technique. Some of these are as follows:
provides distinct insights on minor aspects of the data
compares input and output data to determine if they have a correlation or represent a cause-and-effect relationship
provides hard evidence on specific events, which helps form and test hypothesis
identifies anomalies and outliers to help determine if they represent significant findings or inaccurate data
helps understand the causes of past events, which enables the company to avoid repeating mistakes and replicate actions that brought positive outcomes
helps create a culture that relies on data to make accurate decisions and refines how the organisation collects, analyses and diagnoses data
What are the disadvantages of diagnostics analysis?
Understanding the disadvantages of diagnostics analysis can help you prepare mitigation methods for them. Some of these include the following:
limits the ability to predict future events
requires additional sources to supplement your analysis, including third-party historical and real-time data
takes more time and requires higher-level skills than descriptive analytics, but there are new platforms that mitigate this challenge
leads analysts to mistake correlation for causation, which can easily create costly consequences
What is the process of performing diagnostic data analytics?
There are three main stages of analysing using the diagnostic technique. They include:
1. Identify anomalies
Trends and anomalies you discover during the descriptive analysis may not present the cause, so this requires diagnostics analysis to identify it. The data may have many natural variabilities, making it hard to determine if the descriptive analysis shows a trend. Analysts use statistical analysis in these cases to determine if the results represent an anomaly.
2. Conduct further analysis for data discovery
Only some data is valuable for diagnostic analysis. Professionals investigate all relevant internal and external sets that explain a similar anomaly and determine how they can help diagnose the phenomenon. For example, analysing external data may reveal supply chain changes, new regulatory requirements or changes in competition. It may also be a switch data that can help identify an anomaly's causes.
3. Establish causal connections between variables
Established connections within the data define relationships and causes of the anomaly. You can apply various techniques to create these connections, such as filtering, regression analysis, time series, data analytics and probability theory. The method you employ depends on the circumstances. For example, data mining helps to establish patterns between variables in a data set using administration statistics and machine learning.
Examples of diagnostic analytic data
A company that sells organic products experienced an increase in revenue from its all-natural cooking oil brand. They conducted a diagnostic analysis and discovered that the government had revoked the licences of three competitive brands due to health risks. The law and information caused people to opt for healthier culinary options, which increased the sales of natural cooking oil.
This analysis method helps execute various health care functions, including diagnosing medical issues, charging and billing medical expenses, analysing the efficiency of surgical techniques and examining reinfection rates in specific types of treatments. It can also help determine which patients are using their prescriptions and those who aren't, identify populations that are more likely to contract specific illnesses and ascertain why some patients continue with home therapy and others don't. The health care industry uses diagnostic analytics for AI diagnostic platforms. For example, they may develop chatbots on their website that analyse health care data to interact with patients effectively.
This department also applies diagnostic analysis to examine various employee variables. For example, the hiring department can analyse the company's annual hiring report to determine why a department has high turnover and costs. For instance, the report can reveal that the department's compensation for positions with high turnover is below the industry average.
This industry also uses diagnostic analysis to evaluate the cause of various issues. A minor hindrance can affect the entire manufacturing process. The analysis helps determine the causes and relationships between the variables to ensure operations continue with minimal interruptions. For example, if a machine malfunctions, the manufacturer uses diagnostics analysis to determine the cause.
What are the other types of data analytics?
To help you understand the role of diagnostic analysis better, review the other types of analytics in this field:
This is a big data analytics approach that uses current and historical data to interpret trends and relationships. It helps managers and executives evaluate business performance and understand the changes in the company. It may also influence the organisation's decisions. Some examples of descriptive analysis include the number of users, total revenue per subscriber, sales growth per month and annual price changes. Businesses use descriptive analytics in various aspects of the business, including:
generating business reports, such as revenue, expenses, accounts receivable and accounts payable, cash flow production and inventory
identifying and monitoring financial metrics and other business key performance indicators, such as return on investment or price to earnings ratio
evaluating social media use metrics, such as revenue from their platforms, engagement rates and the percentage of growth in followers
producing summaries of internal and external server results, such as the net promoter score
This refers to data analytics that uses historical data and data mining techniques, statistical modelling and machine learning to make predictions. You use this model to find patterns that identify opportunities and risks. Various industries and disciplines use this technique to influence decisions, such as insurance companies and marketing departments. Predictive analysis helps accomplish various activities, such as making weather forecasts, translating voice to text messages, making customer service decisions, developing video games and creating investment portfolios.
This is a data analysis approach that focuses on what the organisation can do to influence a specific outcome. It uses neural networks, complex event processing, graph analysis, recommendation engine simulations and machine learning. Predictive and prescriptive analytics turn descriptive metrics into insights and decisions.
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