Data & Content

Data Sourcing

Identifying and acquiring data from various sources including APIs, databases, scraping, and partnerships.

Data Enrichment

Enhancing raw data with additional information to increase content depth and value.

Data Cleaning

Removing errors, inconsistencies, and duplicates from datasets before use in content generation.

Structured Data Sources

Organized data repositories like databases, spreadsheets, and APIs that power programmatic content.

Content Templates

Pre-designed content structures with variable placeholders for consistent, scalable page creation.

Variable Interpolation

The process of inserting dynamic values into template placeholders during page generation.

Data Normalization

Organizing data to reduce redundancy and improve consistency across programmatic pages.

Data Validation

Checking data accuracy and completeness before using it to generate pages.

Content Personalization

Tailoring page content based on user characteristics, location, or behavior patterns.

Dynamic Content Blocks

Page sections that change based on data, user context, or algorithmic decisions.

Data Freshness

How current and up-to-date the data powering programmatic pages remains over time.

Data Pipelines

Automated workflows that collect, transform, and load data for programmatic content generation.

ETL Processes

Extract, Transform, Load operations that prepare raw data for use in content systems.

Content Variations

Different versions of content created from the same data to avoid duplication and add uniqueness.

Natural Language Generation (NLG)

Using AI to automatically create human-readable text from structured data.

Data Deduplication

Identifying and removing duplicate records to prevent creating redundant pages.

Data Schemas

Formal definitions of data structure, types, and relationships used in programmatic content.