Predictive analytics has become increasingly prevalent in organizations, as it is a strategic tool that uses historical data to identify patterns and forecast market trends and behaviors.
It can (and should) be applied across multiple areas, making a significant contribution to risk management, process optimization, strategic planning, marketing, and other essential functions for business success.
Supported by its three main pillars—data, statistical models and algorithms, and the interpretation and application of results—it ensures the flexibility needed to meet the demands of different market segments.
The benefits of its application are numerous. In the industrial sector, for example, it enables the monitoring of machinery and equipment conditions in order to optimize maintenance routines and prevent downtime. In agribusiness, it generates insights into specific climate events to overcome challenges, increasing flexibility in the production chain and ensuring resilience in adverse scenarios.
In addition, it encourages data-driven decision-making, positively influences customer experience, increases operational efficiency, reduces risks, helps prevent fraud, promotes continuous process improvement, and supports a culture of innovation. All these factors also enhance credibility and, consequently, become a competitive advantage for the organization.
However, for predictive analytics to be applied accurately and effectively, the organization must be well structured and maintain a reliable historical database, among other essential factors. Otherwise, predictive analytics may produce the opposite effect and generate unrealistic scenarios.
Predictive Analytics Requires Process Management
Reliable data is a basic prerequisite for predictive analytics to function effectively, and collecting data without proper process management can be extremely difficult.
Process management is essential because consolidated data, as well as historical records, require consistency and standardization. It is the key factor that ensures the analyzed information can be compared on equal terms, preventing inconsistent scenarios.
As our teachers used to say, it is not possible to compare apples to bananas. When we compare information collected in different ways or without standard standards, we compromise predictive analytics and prevent it from achieving its primary objective.
Business Process Management (BPM) ensures a solid structure, traceability, organized data history, and the necessary context for analysis, including when using artificial intelligence algorithms, facilitating the processing of large volumes of data.
Another important aspect is continuous improvement, a fundamental pillar of process management, which feeds predictive analytics with high-quality data and standardized processes. As a result, the forecasts generated become more accurate and reliable.
Structured Processes with Interact Suite
Before focusing on the implementation of predictive analytics, it is essential to first structure your company’s process management. For this purpose, you can rely on the Process Management solution within the Interact Suite.
The solution promotes close alignment between organizational strategies, information technology, and other operational technologies. It also includes tools for process automation and artificial intelligence functionalities.
The proposed disciplinary approach enables organizations to plan, document, analyze, model, design, execute, measure, monitor, control, and improve their processes.
Its features allow for process management and enhancement based on historical data analysis, automatic collection of performance indicators, and the generation of predefined strategic data. In addition, it includes a heat map to assess critical points in each process and support the implementation of improvements.
The Process Assistant, an artificial intelligence tool, helps identify steps that can be removed or added to processes, explores indicators that can be linked to them, and provides a critical analysis of each process.
Furthermore, the Analytics Manager—integrated with the solution—provides analytical management that enables organizations to achieve maturity in data analysis. It consolidates and cross-references multiple data sources to ensure better decision-making and to build a reliable data history for predictive analytics.
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