One of the four main types or categories of data analytics is Diagnostic Analytics. This type of analytics, along with the other three analytics categories, including descriptive, predictive, and prescriptive, can be a valuable asset to many businesses. Diagnostic Analytics, also known as root cause analysis, helps analysts determine why a particular change or event happened in the data. So, if your goal is to identify the root causes of specific problems in your business and find out how they came about, then Diagnostic Analytics is exactly what you need.
Essentially, Diagnostic Analytics uses data or content to answer the question “why” something happened in your business. As a form of advanced analytics, Diagnostic Analytics is typically considered the next logical step in data analysis after the given data has gone through the process of Descriptive Analysis.
Despite their common retrospective nature, Descriptive and Diagnostic Analysis differ in their complexity and the insights they provide. While Descriptive Analytics strives to give a clear picture of “what” happened in a business, Diagnostic Analytics aims to find the causality to show “why” and “how” something occurred.
Consequently, there is more complexity in performing diagnostic analytics because it requires more information and more varied techniques to get to the heart of a specific problem than simply stating the existence of a problem.
As stated above, the main purpose of Diagnostic Analytics is to determine the factors and events that led to the outcomes of past events and states. When conducting Diagnostic Analytics, data analysts dive deep into the data and look for patterns, trends, and hidden correlations between variables, often using both internal and external sources to get the information they need.
Interestingly, in the past, the entire process of Diagnostic Analytics was done manually. Nowadays, it would be almost impossible for a human to do all the work or business performance or financial analytics without the help of machines.
Typically, the process of Diagnostic Analytics employs a variety of techniques and tools to carry out its analyses, such as data mining, data discovery, drill-down, drill-through, statistical analysis, algorithms, principal component analysis, probability theory, filtering, sensitivity analysis, and time-series data analysis.
Aside from discovering hidden correlations and connections between variables, Diagnostic Analytics can also serve to detect anomalies, determine causal relationships, isolate patterns, and indicate potential problems as they arise.
Diagnostic Analytics has broad application across multiple industries, including retail, manufacturing, finance, and healthcare. This type of analytics allows company leaders to extract indispensable information from their data by transforming it into meaningful insights and visualizations that anyone can easily understand and use.
Below are a number of examples that illustrate how Diagnostic Analytics can be used in various industries:
Many businesses may reap great benefits from implementing Diagnostic Analytics solutions into their business models. By using it, companies can get valuable insights into their unique opportunities and challenges.
Diagnostic Analytics enables businesses to turn their complex data into manageable and easily understandable information, presented in the form of visualizations and insights that everyone can easily use. This way, company leaders, managers, and operational employees all have access to everything they need to know about the company’s operations, employees, and performance.
Knowing where they stand in the market and having a detailed picture of their business landscape helps businesses eliminate any uncertainty in decision-making. That ultimately leads to making well-informed decisions, getting better response rates, and achieving overall optimization of their business.
Regardless of its major benefits, Diagnostic Analytics has some drawbacks as well. One of the disadvantages of this type of analytics is its focus on past occurrences, which limits its ability to provide actionable insights about the future.
While understanding the causal relationships and sequences may be enough for a particular business, it may not provide all the necessary answers for others. This is where Diagnostic Analytics fails. Some businesses may require more advanced analytics solutions to manage big data. They have to find other tools when searching for meaningful insights, including predictive analytics and prescriptive analytics, which identify possible future happenings.
Any form of analytics, including Diagnostic Analytics, will surely benefit anyone’s business. Along with the other three main types of analytics, Diagnostic Analytics help businesses optimize their performance on many levels.
It performs a deep analysis of data to identify correlations, discover anomalies, and determine causality, creating a better perception of a company’s work and showcasing unique, unforeseen opportunities for growth and optimization.
To find out if your business would benefit from using diagnostic analytics, ask yourself what specifically you want from a given data analytics solution, what specific answers you want to get from it, and whether that specific analytics solution would be able to provide all the answers you need from your data.
Published on May 25, 2021
WRITTEN BYWhatagraph team
The Whatagraph blog team produces high-quality content on all things marketing: industry updates, how-to guides, and case studies.
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