Introduction

Analytics has come forth as an enveloping term for a wide range of business intelligence and application-related activities. In most companies’ data analysis, descriptive analytics is the initial step of analysis that provides the answer to the question, “What did happen?” Diagnostic analytics goes a step further to disclose the reason for certain results. When we compare diagnostic vs descriptive analytics, the difference is about the questions to which these analytics cater to. Descriptive analytics answers the question, “What is happening in your business?” and diagnostic analytics answers the question, “Why is it happening in your business?”

In this article let us look at:

  1. What is Diagnostic Analytics?
  2. What are the benefits of Diagnostic Analytics?
  3. Diagnostic Analytics Examples

1. What is Diagnostic Analytics?

Diagnostic analytics is a type of advanced investigation which analyses content or data to respond to the inquiry “Why did it happen?” and is described by procedures, for example, data mining, drill-down, data discovery and correlations. Diagnostic analytics delves down deep into analysing data to comprehend the reasons for behaviours and events. 

Diagnostic analytics techniques can be categorized as:

Identifying anomalies: Analysts must identify areas that require further investigation based on the findings of descriptive analysis because there are questions that could not be answered merely by viewing the data. These may include questions such as why there is a rise in sales in an area where no marketing changes have been made or why there has been a sudden change in traffic to a website with no apparent cause. Diagnostic analytics will provide all the requisite answers for the above-mentioned questions.

Drilling down into the analytics: By locating the hidden relationships in data sources and events, analysts can explain any anomalies. This step often requires analysts to search for patterns outside of the data sets that already exist. It may also necessitate obtaining data from external sources to detect correlations and determine whether any of them are causal.

Determining causal relationships: Looking at events that may have led to the identified anomalies may reveal hidden relationships. Probability theory, filtering, regression analysis and time-series data analytics are all tools that can be used to find hidden stories in data.

2. What are the benefits of Diagnostic Analytics?

Diagnostic analytics enables you to better understand your data and respond more quickly to critical workforce questions. Diagnostic analytics is the quickest and easiest way for businesses to understand their employees and solve complex workforce problems. Managers can easily search, filter, and compare individuals using interactive data visualisation tools that centralise information from various sources.

Every business is increasingly reliant on data. By translating your complex data into visualisations and insights that everyone can understand, diagnostic tools can help you get the most out of it. Diagnostic analytics enables you to extract value from your data by posing the right questions and conducting in-depth investigations into the answers. This necessitates a flexible, agile, and customizable BI and analytics platform. Then you’ll be able to get answers that are tailored to your company’s unique challenges and opportunities.

3. Diagnostic Analytics Examples

Here are some steps you can take to perform diagnostic analytics on your internal data. It may be appropriate to include outside data to understand the “why” behind what happened. Set up your data investigation first, deciding what questions you’ll be answering. This may be an investigation into the cause of a problem, such as a lower click-through rate, or a positive change, such as a significant increase in sales during a specific time or season.

You can begin your analysis once you’ve identified the problem. You may be able to isolate a pattern and find a correlation by looking at different data sets, or you may need to look at multiple data sets to isolate a pattern and find a correlation. By fitting a set of variables into a linear equation, linear regression can aid in the discovery of relationships. Keep in mind that the more time you give your data model to gather information, the more accurate your results will be. 

A data model develops over time, much like a fine wine. Use diagnostic analytics techniques and diagnostic analytics tools to filter your results so that only the most important factor, or two possible factors, is included. Finally, using the correlated relationships you’ve found, draw your conclusions and make a strong case for them. 

Conclusion

Diagnostic analytics enabled by machine learning plays an essential role in minimising unintentional bias and misinterpreting correlation as causation. However, people would still be in charge of today’s diagnostic analytics. People should be used to contextualise the outputs of machine decision making, just as computers can be used to help eliminate bias in human decision making.

Diagnostic analytics based on a combination of AI-infused software and human domain knowledge appears to be the most effective way to answer the question, “Why did it happen?” After you’ve figured out why you can move on to the next question: What will happen next?

If you are interested in making it big in the world of data and evolve as a Future Leader, you may consider our Business Analytics Course Online, a 10-month online program, in collaboration with IIM Indore!

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