What is diagnostic analytics?

Diagnostic analytics describes the techniques you will use to ask your data: Why did this happen? It’s doing a deep-dive into your data to search for valuable insights. Descriptive analytics, the initial step in most companies’ data analysis, is a simpler process that chronicles the facts of what has already happened. Diagnostic analytics takes it a step further to uncover the reasoning behind certain results.

Diagnostic analytics is usually performed using such techniques as data discovery, drill-down, data mining, and correlations. In the discovery process, analysts identify the data sources that will help them interpret the results. Drilling down involves focusing on a certain facet of the data or particular widget. This drill-down is easily done using Sisense’s BI platform. Data mining is an automated process to get information from a massive set of raw data. And finding consistent correlations in your data can help you pinpoint the parameters of the investigation.

It’s the analysts’ job to identify the data sources that will be used. Often, this requires them to look for patterns outside the company’s internal datasets. It may necessitate pulling in data from external sources to identify correlations and determine causality.

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What are the benefits of diagnostic analytics?

Data plays an ever-increasing role in every company. Using diagnostic tools will allow you to get the most out of it by translating your complex data into visualizations and insights that everyone can take advantage of. Sisense creates tools that you can use to uncover answers to your data questions and easily share insights around the company. 

Diagnostic analytics helps you get value out of your data by asking the right questions and making deep dives for the answers. And this requires a BI and analytics platform that’s versatile, agile, and customizable. Then you can get answers that are specific to your business and your particular challenges and opportunities.

Diagnostic analytics examples

To understand the “why” behind what happened, here are some steps you can use to perform diagnostic analytics on your internal data, and it may be necessary to include outside information as well. First, set up your data investigation – what questions you will be answering. This might be an investigation into the reasoning behind a problem, like a decreased click-through rate, or a positive change, like a dramatic rise in sales during a particular period or season.

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Once you’ve identified the issue, you can set up your analysis. You may be able to find a single root cause, or you may need to look at multiple data sets to isolate a pattern and find a correlation. Linear regression can help you find relationships by fitting a set of variables into a linear equation. Remember that the more time you give your data model to collect data, the more accurate your outcomes will be. A data model ages like a fine wine. Next, filter your results to only include the most important factor, or two possible factors, in your report. Finally, draw your conclusions and make a clear case for them, using the correlated relationships that you’ve discovered.

Let’s look at the example of an HR department that wants to analyze its employees’ performance, based on quarterly performance levels, absenteeism, and overtime hours per week. You could set up your data models, use Python or R for deeper exploration, and look for correlations in your data. Or you could harness the power of the Sisense BI platform and plug in the HR Employee Performance dashboard for a high-level, customizable, real-time analysis of your employees’ time and performance.

Another example involves an issue that every company should be devoting resources to – cybersecurity. The Sisense Cyber Security Team Analytics Dashboard can help you discover the correlation between security rating and the number of incidents, or measure other objectives such as the response teamwork compared with the average time to resolution. Your organization can use these results to plan preventative actions for locations that are considered to be at risk.

In summary

Diagnostic analytics is one of the ways we uncover insights from our data and make it work for us. There are infinite ways to ask questions of data, so concentrate on which questions are the most critical for your organization. The goal of any analytics program should be more relevant information, which will lead to more valuable decisions and a more complete understanding of your business landscape.

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