Diagnostic Analytics: Beyond the What, Towards the Why

This post is part of our Analytics of Things series.

Diagnostic analytics is a branch of analytics that aims to answer the question, "Why did this happen?" by understanding the reasons behind a particular event or issue.

It goes beyond descriptive analytics, as it helps uncover correlations and dependencies, and explains why specific patterns occurred.

Purpose of Diagnostic Analytics

The main idea behind using this analytical technique is to focus on the cause and effect, thereby discovering the root cause of the 'why'.

To illustrate, let us look at three key areas to help us better understand the purpose of diagnostic analytics.

• Uncovers Causes for Informed Insights: These analytics uncover root causes behind past events, answering "why" questions. It examines data to reveal factors that contribute to success or failure.
• Facilitates Data-Driven Decision-Making: It forms a practical basis for strategic decisions by enabling a comprehensive understanding of past events and employs a systematic, data-driven choice.
• Enables Continuous Improvement and Optimization: Diagnostic analytics doesn't stop at "why." It guides the ongoing refinement of processes and strategies. As business adapts, so does the analytical framework.

With a careful plan, a business can effectively implement diagnostic analytics. Let's look at some of the best practices surrounding this.

Five Best Practices for Implementing Diagnostic Analytics

1. Define Clear Objectives: Identify the problems you want to address with diagnostic analytics. Then, clarify the objectives, such as understanding past performance issues, problems' root causes, or historical data trends.
2. Data Quality and Data Preparation: Maintain the accuracy of your historical data. All gathered data must be clean and ready for integration to ensure no missing values, as this can skew the results.
3. Select Appropriate Data Sources: Carefully choose relevant data sources. Ensure that data is trustworthy, clean, and ready for integration, and prevent issues that could stem from missing or unreliable values.
4. Select the Right Tools: Choose the most suitable analytics tools and technologies, whether statistical software, business intelligence tools, or data visualization platforms. Of course, the tools must be practical to use for data analysis.
5. Data Visualization: Leverage data visualization methods to effectively explore and share findings. Employ visual aids like charts, graphs, and dashboards to convey insights to non-technical stakeholders.

The above can help you understand past events better and make data-driven decisions in your operations, products, and services.

Use Cases of Diagnostic Analytics

• Identifying Performance Issues: You can apply diagnostic analytics to uncover underlying reasons for performance declines in specific business operations, such as sales, marketing campaigns, or customer service. Doing so can assist in preventing future setbacks.
• Customer Churn Analysis: Historical customer data can pinpoint the factors driving customer attrition. Once you know your root causes, you can tailor strategies to enhance customer experiences and reduce churn rates.
• Quality Control in Manufacturing: You can improve quality control and reduce costs by analyzing historical data to enhance quality control.
• Financial Analysis: By analyzing historical financial data, organizations can identify the drivers of financial performance, make informed decisions, and optimize their financial strategies.

Supply Chain Optimization: Analyzing past data and pinpointing bottlenecks in the supply chain process allows businesses to streamline operations, reduce costs, and optimize performance.

The most important thing to know is that diagnostic analytics is part of a broader arsenal of techniques contributing to the wider field of predictive analytics.

In conclusion, exploring root causes, using data-driven choices, and working on continuous improvement allows businesses to adapt and thrive in ever-changing landscapes. The potential of diagnostic analytics lies in enhanced operations, products, and services. With a proper plan, any organization can achieve these goals.