What is a Decision Making Model?

The Evolution and Role of Models in Enterprise Operations

Models are software artifacts that drive decisions based on data. In some cases models learn from data directly (machine learning) in other cases a human expresses the patterns in the data, such as in rules-based models.

Valuable Intellectual Property

They encode critical intellectual property and as such are highly valuable enterprise assets. As models have evolved and grown in power they have begun to take on an outsized role in how enterprises function and compete.

How Models Function in Decision-Making

Models ingest data and produce inferences that are used in making decisions. Increasingly, the inferences produced by models are consumed by software applications and used to partially or fully automate business decisions.

The Long History of Model Usage

Models are not new. Many enterprises have employed models for decades, largely rule-based models or algorithmic models that perform mathematical optimization.

The Continuing Relevance of Traditional Models

In financial services for example, these types of models have traditionally been used to assist with myriad tasks such as processing loan applications, handling insurance claims, pricing products, or executing trades.

These types of models are still in wide use and will likely remain so for many years to come.

Understanding the Nature of ML Models

In several key respects, ML models are not like conventional software or even like other types of models. ML models do not execute deterministic rules—they are statistical artifacts created by “training” with data.

With appropriate design and training, ML models can make highly effective inferences from complex data that would be impractical or impossible to code using explicit rules.

Model Accuracy Depends on Data Consistency

Because ML models are trained using data, their accuracy holds only as long as the data they see in production resembles the data they were trained on. As real-world conditions change, this alignment breaks down, causing prediction quality to degrade.

Every model has a natural retraining cadence that may range from months to days—or even less in dynamic environments.

Impact of Rapid Change on Model Performance

In cases of significant environmental disruption, such as the COVID-19 pandemic, ML models can lose predictive effectiveness very quickly. This highlights the need for constant monitoring and timely retraining to maintain accuracy and reliability in real-world conditions.

Technical Sensitivities in Model Deployment

ML models have strict technical requirements. Small differences between development and production environments can cause models to perform poorly or fail entirely, underscoring the need for controlled and consistent deployment processes.

Stakeholders in the Model Lifecycle

Successful model lifecycle management requires collaboration among multiple teams.

These include:

• the business unit sponsoring the model
• data scientists who develop it
• DevOps for integration
• DataOps for managing data pipelines
• ITOps for infrastructure
• governance teams ensuring compliance

Model Volume and Lifecycle Complexity

In large enterprises, it is common to manage hundreds or thousands of models. Each model may have different business KPIs, platforms, training cycles, environments, compliance rules, and monitoring needs, making lifecycle coordination essential.

Business Accountability for Models

Because models automate high-impact decisions, line-of-business managers need real-time oversight and control of models in production—often to a greater degree than is required for conventional software applications.

Governance and Risk Mitigation

The outcomes produced by models can affect people’s lives directly—such as loan approvals, hiring decisions, or promotions—so governance is critical. Rigorous oversight helps minimize legal, ethical, and reputational risks.

Models as Strategic Assets

Models represent some of an enterprise’s most valuable intellectual property, encoding behaviors of customers, employees, and markets. Unlike much commoditized software, high-value models remain unique and proprietary, making them essential corporate assets that require careful management and protection.

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Related: ModelOps vs MLOps