How to Apply Descriptive Analytics, Step by Step

Descriptive Analytics: the First Step to Transforming Data into Strategic Decisions

Increasingly, organizations are seeking to transform data into useful knowledge to make much more strategic and informed decisions. This approach, in addition to improving current processes, allows for the establishment of much more effective future actions. However, before looking at where we are going, it is essential to understand where we come from. This is where descriptive analytics comes into play.

Descriptive analytics is the first level of analytical tools that allows data to be transformed into valuable information. Its main objective is to understand “what has happened” by analyzing historical and current data. Unlike other types of analysis such as predictive or prescriptive, which focus on the future or on making concrete decisions, it offers a clear and objective view of the past, laying the foundation for more advanced analysis.

How can you apply it in your organization? Very simple

Implementing descriptive analytics in the industrial environment is not very complicated as long as you follow a key structure. At Becolve, we have defined five key steps for this:

1. Define your objective

To begin, it is essential that you establish what you want to analyze and why. Defining specific objectives will help you focus your analysis on data that is truly relevant to optimizing your operations. Some examples are:

• How has the defect rate in production evolved?
• What are the patterns in machine downtime?
• What trends do inventory levels or energy consumption show?

2. Collect and prepare industrial data

Collection:

• Data from SCADA sensors (temperature, vibrations, pressure, etc.).
• Machinery records (operation logs, downtime).
• Information from MES, ERP, and other systems (production and resource management).

Cleaning:

• Correction of inconsistent or anomalous data (for example, impossible temperatures).
• Synchronization of information from multiple sources.

Transformation:

• Consolidation of temporal data, such as converting records per second to hourly or daily averages.
• Categorization of variables (product type, shift, production line).

3. Explore industrial data

Perform an exploratory analysis to identify trends and deviations, such as:

• Cycle times: identifies deviations in standard production.
• Machine performance: compares operating and downtime.

Use specific graphs:

• Control charts for quality (SPC).
• Histograms to analyze the distribution of defects.
• Time series to monitor critical variables, such as temperature or energy consumption.

4. Generate key industrial metrics

Transform data into actionable indicators that reflect the actual performance of your operations:

Performance indicators (KPIs):

• OEE (Overall Equipment Efficiency): availability, performance, and quality.
• MTBF (Mean Time Between Failures): helps to understand the reliability of machines.
• MTTR (Mean Time to Repair): indicates the efficiency of repairs.

Quality trends:

• Defect rates per batch, shift, or production line.
• Percentage of non-conforming products.

Operational efficiency:

• Energy consumption per unit produced.
• Costs per hour of operation.

5. Interpret and communicate the results

Finally, beyond delivering the data by themselves, you must interpret and present them clearly so that they support future rapid and effective decision-making.

Analysis focused on decision-making:

• Identify critical areas that need adjustments, such as lines with high defect rates.
• Detect seasonal patterns or peaks in the use of machinery.

Specific visualizations:

• Dashboards for real-time monitoring of sensors and KPIs.
• Pareto charts to prioritize problems.
• Heat maps to identify bottlenecks in the process.

Finally, summarize findings in a clear report and translate them into concrete actions, such as preventive maintenance or line adjustments.

By following these steps, you can implement one of the most important tools for defining strategic actions based on data. By providing a clear view of the past and present, advanced analytics becomes the starting point for more advanced analysis that optimizes processes and decision-making.

With the rise of emerging data management technologies, the impact of this discipline continues to grow, progressively becoming an indispensable resource for any organization that aspires to remain competitive.