What the heck is an Ontology?

Is Palantir onto something, or are they just blowing hot air?

You may have heard the term Ontology, and why leading businesses rely on them to build trustworthy data models. Recent advances in LLMs have made it possible for startups and enterprises alike to build flexible, reliable semantic layers faster than ever before.

What do we mean by “ontology”?

The ontology has its roots in philosophy, where it represents a formal catalog of objects, the properties that describe them, and the relationships that connect them. Here’s a simple example:

You might notice that this begins to resemble a data model. The Ontology has become popularized by Palantir as a semantic layer solution, which, to be reductive, is essentially a relational database with a semantic structure (different from the web 2.0 semantic ontology). This Ontology acts as an abstraction between raw data and the business concepts they represent. There are many implementations of semantic layers under a variety of names, but the bottom line is they’re working to make data represent our real world as we understand it.

Why do people want semantic layers anyway?

Every term you’ve heard — source of truth, golden tables, semantic layer — is chasing the same promise: let anyone ask and receive answers that faithfully reflect how the business works. An analyst should be able to ask, “How many premium users bought espresso last month?” and trust the answer. Well-implemented Ontologies deliver on that promise by encoding business logic and enforcing queries adhere to that model.

Ontologies in the Real World

As you might guess, this isn’t a new idea.

The star schema, introduced in the 1990s, puts a wide fact table at its center and fan‑outs to dimension tables on the edges. It celebrates speed and simplicity; query planners can predict access patterns, and BI tools understand the layout instinctively. This modeling approach forces data into either “dimensions” or “facts”. Facts generally have many, many rows with a few columns. The idea is to represent that which is large and highly repeatable row-over-row.

However, this hard divide between facts (immutable events) and dimensions (reference data) falls apart as the business evolves-say, a new pricing plan blurs the line between a transaction and a subscription, those early modeling choices become concrete shoes.

Snowflake schemas attempt to tame redundancy by further normalizing dimension tables. Storage usage drops, but the price is paid in join complexity. What once took a single hop now requires a small expedition through intermediary tables, and each extra join is another surface for error or performance regressions.

Both designs inherit the fact‑dimension mindset. It has served analytics well for decades, but it assumes the world stays still long enough for engineers to carve data into neat shapes. Modern businesses rarely stay that sessile.

Palantir’s Ontology: turning the model into a graph

Palantir threw out the fact‑dimension wall and treated every table as a node in a directed graph. Links between nodes come with explicit types, one‑to‑one, one‑to‑many, and so forth, so analysts can traverse the graph without guessing how joins should behave. The model is also iterative. If tomorrow’s reality demands a new object or a new relationship, you add a node or an edge and keep moving. Query optimizers still have plenty to chew on, but modelers are no longer forced to contort reality to fit a star pattern decided months earlier when the business looked dramatically different.

Palantir’s Ontology reflects current semantic logic while remaining fluid enough to reflect the changing reality of the business.

Why volatility hurts everyone-enterprises and startups alike

Unfortunately, it is incredibly challenging to build a solid, trustworthy source of truth that is also flexible enough to reflect a fast-moving environment.

Consider an enterprise that suddenly faces a new regulatory regime or an unexpected cybersecurity threat. Core schemas that once changed quarterly may now shift weekly as compliance teams add tables, columns, or entire data sources. Meanwhile, a fast‑growing startup is rewriting its own playbook-pivoting products, experimenting with pricing, or integrating a surge of user‑generated events. In both cases, pipelines that were handcrafted for yesterday break under the weight of today’s questions. The cost is measured in stale dashboards, duplicate metrics, and lost decisions.

Palantir gets around this problem by using the forward-deployed engineer model; embedded analysts who work to deeply understand how a business operates across industries, then task themselves with manually, and often painstakingly, maintaining an Ontology for a Fortune 500 company. Meanwhile, startups simply accept analytical debt as the price of speed.

LLMs have changed the game: automatic ontology generation

Large‑language models have changed the economics of generating and maintaining Ontologies. Point them at your data warehouses and they can take a massive snapshot of the current state of your data. They can then:

• Scan thousands of objects, notice common naming conventions, and summarize as a data model
• Spot tables that share primary keys or semantic similarities in values, and propose relationships
• Propose relationships between objects with cardinality

What previously required weeks of an expensive embedded analyst working to understand every nook and cranny of your data warehouse, an LLM can gain a version of that understanding in moments. The challenge is that LLMs, unlike a human analyst, don’t innately know that your “customer” excludes free‑trial users or the specific way your company calculates key metrics. Without guidance, they hallucinate, generalize, or miss edge cases. That’s where human context and Astrobee come in.

Astrobee, the collaboration layer

Astrobee is the collaboration layer between subject‑matter experts and the LLM runtime.

• Extract -We ingest your warehouse and scrape lineage to give the LLM its raw material.
• Draft — The model proposes objects, links, tests, plus the SQL and pipeline code to populate them.
• Review — Stakeholders approve, comment, or override in a Git‑style diff. Astrobee records every decision.
• Democratize Data — Anyone across your business can query your data, and rest assured they’re all referencing a single source of truth, Ontology

As more questions flow through Astrobee, it spots patterns and optimizes itself. Repeated joins become reusable generated pipelines. Expensive ad‑hoc queries trigger recommendations to use specific measures org wide.

The result is compound leverage: each inquiry refines the ontology, and consequently, business insights, for subsequent users. Enterprises manage schema drift without large data teams, and startups achieve enterprise-grade modeling agility at seed-stage budgets.

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