10 things to help prepare your data before your build your Palantir ontology

Building an ontology is at it's core a forensics job. You are often walking into a crime scene of neglected databases, conflicting spreadsheets, and siloed servers. Map this mess "as-is", and you just get a highly structured (and costly) disaster in your future. You have to process the crime scene first.

Here is how you prepare your data before the modeling even begins.

1. Interrogate the source systems

Most teams start with the pristine data warehouse. Big mistake. You need to find the shadow systems. I've seen projects where the client prodly points to the ERP as their primary source of truth only to then spend three weeks tracking down a critical customer metric that only lived on a retired manager's local hard drive. Map the physical locations of your raw data. If you miss the origin point, your ontology is blind from day one.

2. Appoint a single dictator of truth

You will find "Client_ID" in five different systems. They will all have different values, field names, validation and so on. You cannot democratize this. You must pick one system to rule them all and map the others back to it. Fail to do this, and your users will ask a simple question and get five conflicting answers. Bad data is bad data.

3. Force a common language

Marketing calls them "subscribers." Sales calls them "leads." Support calls them "tickets." They are talking about the exact same human being. Get these department heads in a room and lock the door until they agree on a single term. An ontology requires a shared vocabulary. Without it, you are just building separate, conflicting dictionaries.

4. Crack open the text blobs

People love stuffing vital information into free-text "Notes" fields. This is an absolute nightmare for structuring knowledge. You have to extract the dates, names, and locations out of those paragraphs and put them into discrete columns. An ontology connects specific entities, not unstructured rants.

5. Map the literal connections

Don't wait for the graph database to figure out how things relate. Sketch it out now. Does an "Invoice" belong to a "Customer," or does it belong to an "Account"? Grab a piece of paper. Draw the lines. If the relationship looks like a bowl of spaghetti, your data model is too complicated.

6. Purge the ghosts

Some data sources are beyond saving. I frequently evaluate legacy CRMs where 60% of the fields are completely blank. We often have to cut it loose if it causes more problems than the value of the partial record sets solve. You need to set a ruthless quality threshold. If a dataset is mostly nulls or errors, drop it. Mapping garbage just creates a more expensive pile of garbage.

7. Lock down the timelines

Information expires. A shipping address from 2018 is useless today unless you are doing historical analysis. You need to attach "start" and "end" dates to your records, or, you need to go through a complete data hygiene exercise and re-verification of records. Your ontology must understand the dimension of time, or it will present outdated facts as current reality.

8. Enforce brutal uniformity

This is the grunt work. Dates, currencies, and metrics must follow one standard. If one system uses pounds and another uses kilograms, your automated reasoning will break. Pick a standard (like ISO 8601 for dates) and run the conversion scripts. It is tedious. Do it anyway.

9. Bypass the security red tape early

You will inevitably need access to a locked-down server to verify a data feed. Do not wait until week six to ask for the keys. Identify the database admins and security officers now. Get your credentials sorted before you write a single line of code. It is also important to understand the cadence and timing on when/how/where you will get your data from these systems.

10. Prove it in a fishbowl

Do not try to boil the ocean. Pick a tiny, highly specific slice of the business—like "returns processing"—and map it completely. This gives you a working, rapid (we hope) prototype. You learn the eccentricities of your data in a controlled environment. Once that fishbowl works, you can expand to the whole aquarium.

Preparation is ugly. It is political, manual, and exhausting. Do not skip it. A beautiful ontology built on rotten data is just a very expensive hallucination. Fix the foundation, and the structure will hold. Absent that you are in for a world of hurt.