Data modeling, data science, and data analytics all go hand-in-hand—you need a quality data model to get the most impactful data analytics for effectual business intelligence that'll inform your ...

Model drift is the degradation of data analytics model performance due to changes in data and relationships between data variables. Model drift occurs when the accuracy of insights, especially ...

Brands can save 10-15% just by building a CSV data model. API Integrations. API data insights pick up where Excel feed data leaves off. The API is designed with the end-user in mind. The data can be ...

Predictive modeling is a statistical analysis of data used to generate future scenarios for organizations and companies. It can be used in any industry, enterprise, or endeavor in which data is ...

Most of us feel like we’re drowning in data. And yet, in the world of generative AI, a looming data shortage is keeping some ...

Data modeling is an important part of business intelligence that requires the support of skilled professionals. Learn more about what they do.

The data column of the Zachman Framework comprises multiple layers, including architectural standards important to the business, a semantic model or conceptual/enterprise data model, an enterprise ...

Discussed in 2023, but popularised more recently, “model collapse” refers to a hypothetical scenario where future AI systems get progressively dumber due to the increase of AI-generated data ...

As such, it involves tracking data from place to place, monitoring the transition of data from one form to another, and ensuring nothing important is left out of a business analytics model. Data ...

Chances are, unless you're already deep into AI programming, you've never heard of Model Context Protocol (MCP). But, trust me, you will. MCP is rapidly emerging as a foundational standard for the ...