Why Model Management is Essential for Modeling Success

Gary Robinson Data Management, Predictive Analytics

Modeling is a powerful way to predict behaviors and business outcomes. But, to maintain relevant and accurate models, they must be regularly monitored and adjusted to address changes that occur as part of the natural model evolution. 

Managing the lifecycle of a model requires many factors that need to be considered to maintain its quality. The cost of storage, understanding the model’s approach, the documentation of metadata, monitoring the evolution of the model, validating and meeting the standards of the model compliance framework are all essential.

Once you understand the requirements of model management, you can more easily maintain your model and reach the optimal balance of performance, cost, efficiency, and quality.

The Cost of Storage

Good data models can reduce unnecessary data redundancy, reuse computing results, and reduce the storage and computing costs for the system.

Striking a balance between the cost of storage with the demand for capacity and performance requires in-depth knowledge of how your organization is generating, using, and storing data from creation to deletion.

To perform this practice effectively your company should implement consistent policies around the handling and use of data. All information should be classified and controls set regarding access, distribution, retention, and deletion.

Understanding the Model’s Scope

Once you’ve created a data model, you need to document it to deliver the model’s content to others—for business colleagues, developers, and future reference.

When the model is built, documentation of who created the model is imperative in the event you need to understand the approach behind the model in the future. Each developer has a unique approach and techniques that translate to the development of the model. And the more models you have, the higher the risk of variation in approach. Documenting ownership provides a history of the development that will ultimately extend the life of the model.

Documentation of Metadata

Metadata creates a context for your data, helping you understand the ‘who,’ ‘what,’ ‘where,’ ‘why,’ ‘when,’ and ‘how’ of data. It provides essential information such as when your data was acquired, created, or revised.

Managing your metadata and creating a consistent information infrastructure is vital when it comes to referencing, accessing, and consuming business data. Many organizations are missing basic facts like who owns specific information, how important that information is, and how relevant it is to the business.

It’s essential to set consistent policies for information that can be enforced wherever the data resides, whether in physical, virtual, or cloud environments.

Organizations working with fragmented systems and inconsistent approaches have to deal with gaps in the usability of their data. They risk non-compliance with new regulations like the General Data Protection Regulation (GDPR) because they can’t show how their data was acquired and how and where it’s stored.

Monitoring and Validating Your Model

Catching errors and oversights early, when they are easy to fix is better than developing a model and then fixing it again, but a model also needs to be updated over time. It’s important to watch and document the evolution of your models and how they shift to ensure they continue to serve their original purpose.

When validating your model, the Data Model Scorecard is an objective way to measure positive and negative attributes. In the review of a model, you need to ask a few questions and check the following boxes. Ask yourself if the model:

  • Meets requirements and standards
  • Is complete and structurally sound
  • Is defined and readable
  • Accounts for the realities of the data it is processing
  • Is consistent with the needs of your organization
  • Has metadata definitions that align with the data you’re processing

To watch for degradation in your model, monitor its performance to determine when the model needs to be rebuilt. Learn how to validate models with basic training and test error concepts. If your predictive model is validated incorrectly, it can have dire business consequences in real-life scenarios.

The Model Compliance Framework

A model compliance framework defines your organization’s compliance standards, relevant to business processes.  The set of required standards are compiled to communicate procedures, risk controls, and governance practices for maintaining compliance. The framework should specify which compliance processes overlap to reduce redundancy.

For example, highly regulated industries such as financial services have strict data processing and compliance requirements. Along with reviews conducted by an internal compliance team, regulated companies must abide by external regulatory protocols from the SEC and FINRA. So, it’s especially important that if your organization falls in this category, you establish and manage best practices for maintaining compliance.

Lityx helps businesses manage the lifecycle of models. Check out our training materials and see how our software works to understand our approach to predictive modeling and optimization.

 


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