Analytics are everywhere. Data analytics has been the primary focus for many industries to stay competitive in the ever-so-growing marketplace. And data-driven decision-making has been an essential part of organizations so that they are well-prepared to deal with uncertain times.
According to Statista, the data analytics market revenue is predicted to hit 68 billion dollars by 2025 with a compounded increase of up to 30% annually. So, what’s pushing businesses to adopt data analytics?
Data originates from all sources within an organization. And this raw, historical data is of no use if not analyzed or interpreted into meaningful insights.
How? Data analytics is further broken down into four categories – descriptive, diagnostic, predictive, and prescriptive. These four types of analytics answer the what, why, when, and where questions using your data.
And in this article, we’ll be looking at the “why” part of data analytics.
Diagnostic Analytics – What Is It and Why Do We Need It?
As the name suggests, diagnostic analytics help you uncover the reason behind any abnormality within your system using data. It is also known as root cause analysis because it helps you probe into what caused this issue.
For instance, when you come across a sudden spike in customer-related concerns, data analysts use the relevant data to drill down on the aspects that are causing this situation. Diagnostic analytics are super critical because simply understanding the issue is not sufficient to solve the problem in question, but you’d have to know the “why”, too.
And if timely diagnostic analytics aren’t performed in the example above and a lack of mitigation plan can ultimately lead to customer dissatisfaction and an increase in churn rate.
But how is diagnostic analytics actually performed? Analysts dig into large amounts of historical, and current data sets using techniques like data mining, data drilling, diagnostic analytics platforms, etc. This helps them derive super helpful and valuable insights that can drive key business decisions.
What Are Some Use Cases for Diagnostic Analytics?
Diagnostic analytics has been widely used across multiple industries and enterprises that adopted modern analytics. And here are the 4 most common ones:
#1. Marketing
With marketing, teams often face a “this vs that” situation where they need to identify why a particular campaign or strategy tanked over the other. And what better way to investigate this than by performing diagnostic analytics.
It can also be used to help marketing efforts by analyzing the declining click-through rate and customer engagement with certain posts, decreased or increased sales over a period, understanding customer behavior, and more.
Once root-caused, these insights further drive impactful decisions to perform the successive predictive analytics. Here, appropriate action is taken to address the lower performance and to see better growth in sales.
#2. Security
A weak cybersecurity system is a huge concern for many companies as any degree of failure could mean major losses of both critical data and revenue.
What’s the next step once threats or intrusions are detected? It’s finding and understanding the root cause to identify the extent of impact by collecting every possible digital information with the help of diagnostic analytics. And finally deriving risk areas so that they bring in security measures for future prevention using the next steps of analytics.
#3. Human Resources
HR is one such department where data is all around their processes. From hiring to firing, and training to managing, data is their primary driver to make key decisions.
For instance, if the company has unfortunately been experiencing a huge number of employee resignations, they need to look into the reasons for this without a second thought. Scraping through various data sets like exit interviews, feedback, company ratings, etc., and employing diagnostic analytics techniques can help them understand the exact reasons behind this trend.
#4. Healthcare
In the healthcare sector, diagnostic analytics could be of tremendous help as accurate and on-time detection plays a crucial role while dealing with patients’ data.
Some common areas to gain insights using diagnostic analytics are detecting the efficiency of treatments, infection rates, reasons for certain treatments not being effective enough, finding any existing bottlenecks within management, and so on.
Insights derived can aid in providing priority critical care for at-risk patients and expose any existing irregularities in the system.
Benefits of Diagnostic Analytics
Apart from the varied real-time use cases mentioned above, here are the overall benefits diagnostic analytics and its techniques provide every business:
- To make well-informed data-driven decisions and valuable insights that propel the company’s growth.
- To understand the company’s processes, employees, and performance while reducing complexity.
- Drill down and solve complex issues in the workforce.
- Analyze customer behavior to drive more revenue.
- Identify technology issues in production and mitigate them.
- Avoid repetitive mistakes as it helps correlate different aspects and understand the root cause.
The Bottom Line
Human decision-making can be full of misconceptions and assumptions leading to faulty business understanding. Therefore, powerful data analytics are the need of the hour to drive performance, reliability, and faster ROI.
Diagnostic analytics can play a crucial role in optimizing your business.
Transform your business today with the help of the Ascentt team of analytics experts. Contact us to know more!