Analytics

Diagnostic analytics examples and use cases

By Rachel Lee - September 20, 2021

While it’s easy for companies to collect and store data today, pulling out valuable insights quickly is a common challenge. To help make sense of large-scale analyses, companies often use diagnostic analytics to determine what happened to produce a specific outcome.

The first step in the diagnostic analytics process is known as descriptive analytics, which helps analysts outline results. To uncover the reasoning behind those results, data analysts use a few different techniques: Drill-down, data mining, and data discovery can help analysis get to the root cause behind data findings.

Drilling down is the process of focusing on a particular facet of data, while data mining is an automated process that gleans information from raw data. Data discovery is the process of honing in on the data sources to help analysts interpret results.

The purpose of diagnostic analytics is to get the most value out of your data. Using the techniques mentioned above, analysts get to the root of a pattern or trend and pull out meaningful business insights. The important thing is knowing which questions to ask of your data to help you better understand your overall business landscape.

Examples of diagnostic analytics applications

There are a multitude of ways diagnostic analytics can be useful for a company. Let’s look at a few examples of how diagnostic analytics helps businesses gain valuable insights.

Marketing

For teams that manage social media campaigns, diagnostic analytics can look at specific aspects of ads that are high-performing versus those that are flops. The analytics process will help marketing teams identify reasons why one was successful and the other was not.

Finance

Financial teams can use diagnostic analytics to compare revenue growth or decline to help find patterns across initiatives.

Cybersecurity

Cybersecurity analysts could use diagnostic analytics to find the connection between security ratings and the number of data breaches, for example.

Why should I use diagnostic analytics?

Knowing how to make the most of your data should be a primary focus for today’s leading companies. The sheer amount of data available might be intimidating for some business leaders, but there’s a wealth of information and actionable insights that can be gained from diagnostic analytics.

Using diagnostic analytics is all about taking big data and turning it into useful information. Rather than simply looking at the results, diagnostic analytics takes a deep dive into the “how” and “why” of your outcomes, a key step in business intelligence.

To determine the “why” behind outcomes, it’s important to come up with a list of questions to answer. For example, this could be an investigation into what caused a decreased click-through rate. When you determine your key issue (positive or negative), you can start your analysis.

There are many potential outcomes: You might discover a root cause that answers your question, or you may need to take a deeper look into other data sets or historical data to find a pattern.

What are the other types of analytics?

Analysts use four main types of data analytics: Descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Let’s briefly look at the other three types and how they apply to the overall data analysis process.

Descriptive analytics

As described above, descriptive analytics is usually the first part of the process. In this step, analysts will look at historical data and produce visualizations to help paint a clear picture of what’s already happened.

Predictive analytics

As the name would suggest, predictive analytics is about predicting what might happen in the future based on data findings and knowledge about what led to those results. Predictive analytics typically incorporates computer modeling and machine learning to determine future outcomes.

Prescriptive analytics

The final step in the analytics process is prescriptive analytics, in which teams learn what they should do based on what forecasts came from the predictive analysis. Artificial intelligence is evolving to help with this phase of the advanced analytics process.

Improve your decision making with Sisu

Data analytics has the power to transform your business, but many companies are simply too overwhelmed with massive amounts of data to take action.

This is where Sisu comes in: Our analytics engine helps you comb through large datasets to glean valuable information and come up with actionable insights. We know that advanced data diagnostics are hard to perform on your own; luckily, you don’t have to.

Schedule a demo with Sisu today to find out how we can help you advance your business intelligence and make better decisions for success.


Read more

What is a decision support system in artificial intelligence (AI)?

Learn about decision support systems in artificial intelligence and how they help businesses make better decisions.

Read more

Dear CIO: Here’s why you’re still struggling to utilize data

Despite more data available to businesses than ever, many teams still struggle to utilize their data to make decisions. In this blog, we take a look at what’s making data unusable and how CIOs can address it.

Read more