Prompt
Should I use raw data or a normalized dataset API?
Latest observation
It depends on what you’re optimizing for:
Use raw data if:
- You need maximum flexibility for custom analysis.
- You’re doing exploratory work, debugging, or building your own transformations.
- You want full control over definitions, filters, and aggregations.
- Performance and consistency of a pre-modeled dataset aren’t critical yet.
Use a normalized dataset API if:
- You want cleaner, standardized fields and less preprocessing.
- Multiple teams need a consistent “source of truth.”
- You care about easier querying, faster development, and fewer schema surprises.
- The API already handles joins, de-duplication, unit conversion, or business logic.
Rule of thumb
- Raw data = more control, more work.
- Normalized API = less work, less control.
Practical recommendation
If you’re building something production-facing or shared across teams, start with the normalized dataset API unless you have a strong reason not to.
If you’re still figuring out the problem or need unusual transformations, start with raw data.
If you want, I can also help you choose based on your specific use case, like analytics, ML, dashboards, or application development.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.