Useful data is often spread across platform exports, APIs, subtitle files, chat logs, spreadsheets, and public websites. Before that information can support a report or analysis, it needs to be collected, standardized, checked, and organized.
Sun Data Analytics, LLC builds repeatable workflows that move information from its original source into cleaner, analysis-ready tables.
These workflows can support a one-time project or become part of a reporting system that is refreshed on a recurring schedule.
A collection workflow can stand on its own or connect directly to an automated report, dashboard, classification system, or mixed-methods analysis.
Different sources require different collection methods, but the goal is consistent: create dependable data that can be refreshed, inspected, and connected to later analysis. These are examples of repeatable import and preparation workflows, not separate one-off services.
Creator analytics
YouTube data can describe both the content a creator publishes and how that content performs over time. Sun Data Analytics can organize data from platform exports, authenticated analytics sources, public APIs, and existing creator records.
These sources can be combined into stable video-level, channel-level, or time-based tables for reporting and comparison.
Titles, dates, tags, categories, durations.
Views, watch time, impressions, click-through rate.
Age, geography, devices, subscriber status.
Estimated revenue, revenue sources, subscriber changes.
Combined by video, channel, and reporting period.
Stream text
Performance metrics can show how a video or stream performed, but they do not describe everything that happened inside the content.
This makes it possible to connect platform performance with the subjects, interactions, and audience responses found inside the content itself.
Host Main segment
Analysis segmentChat marker Audience question aligned to transcript time.
Media monitoring
Source-specific public collection can support research, trend monitoring, and media analysis where collection is technically and ethically appropriate.
Examples include: articles, publication dates, authors, publishers, topics, named entities.
Research files
Not every project begins with an API. Many begin with spreadsheets, survey exports, transcripts, manually maintained records, or files created by several different people.
Examples include: survey cleanup, transcripts, field definitions, duplicate checks, date cleanup, reshaping.
Bring source materials together.
Align fields, dates, and identifiers.
Check duplicates and gaps.
Record sources and assumptions.
Prepare for future updates.
Clean tables organized around the useful unit of analysis.
Documented R, Python, API, or file-processing workflows.
Definitions, source notes, transformation rules, and limitations.
Stable structures and update processes for recurring work.
Related content
See how recurring reports can turn refreshed source data into repeatable analysis outputs.
See how performance data, text classification, and qualitative interpretation can work together.
Return to the main services page to compare reporting, data collection, and analysis support.