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Google AIBefore we jump into how DQLabs uses both, lets clarify and clearly breakdown with examples grounded in the data world:
Semantics — What does this data mean?
Semantics is about the inherent meaning and definition of a data element — what it represents in business terms, independent of how it’s being used.
Example: A column called cust_id in a database table.
- Semantics tells you: “This is a Customer Identifier — a unique reference to a person or organization that has a business relationship with the company”
- It gets tagged with business terms like: Customer, PII, Primary Key, CRM Entity
- This meaning is stable — cust_id means the same thing whether it appears in a sales table, a support ticket table, or a billing table
DQLabs context: Prizm’s semantic layer auto-discovers that cust_id = a customer identifier across all your data sources, without you manually mapping it.
Context — How is this data being used, and does it matter?
Context is about the circumstances surrounding data — who uses it, where it flows, what depends on it, and what impact it has on the business.
Example: That same cust_id column — now let’s add context:
- It feeds into the daily revenue dashboard used by the CFO
- It’s joined to a pipeline that triggers customer invoices
- It was flagged with 3% null values last Tuesday
- It’s downstream of a Salesforce sync that ran late
Context tells you:
- This particular instance of cust_id is business-critical
- A data issue here affects revenue reporting and invoicing
- This needs to be prioritized over a cust_id sitting in an archive table nobody uses
Side-by-Side Comparison
| Semantics | Context | |
| Question | What does this data mean? | Why does this data matter right now? |
| Nature | Static definition | Dynamic and situational |
| Example | cust_id = Customer Identifier | cust_id feeds the CFO dashboard and invoice pipeline |
| Set by | Business glossary, classification | Lineage, usage patterns, downstream dependencies |
| Changes over time? | Rarely | Constantly |
How DQLabs Uses Both Together
This is where Prizm’s power comes in — semantics without context is just a label; context without semantics is just noise.
- Semantics tells Prizm: “This is customer data, it’s PII, it’s a key business entity”
- Context tells Prizm: “This specific instance flows into 12 downstream reports, was touched by 3 pipelines today, and is used by the finance team daily”
- Together, Prizm’s agents can say: “There’s a data quality issue here — and it’s high priority because of what this data means AND how critical it is to the business right now”
That combined intelligence is what makes it AI native — it’s not just flagging errors; it’s understanding meaning + impact to act intelligently.
