Back in September, Snowflake announced a new open-source initiative focused on standardizing semantic metadata for AI. Salesforce, dbt Labs, Mistral, and other major players signed on. The goal is straightforward: create a shared framework so data definitions mean the same thing across tools, systems, and AI applications.
That announcement matters. But not for the reason most teams think.
Industry-wide metadata standards don’t emerge when systems are healthy. They wouldn't need to. In fact, the need for them only emerges when drift has already become impossible to ignore.
For years, orgs have operated under the assumption that their core systems are “close enough” to aligned. Salesforce feeds Snowflake. Snowflake feeds dashboards. Dashboards inform decisions. AI sits on top and is expected to make sense of it all.
On paper, the flow looks clean. In reality, meaning shifts underneath it.
An Example of Reality Shift
A field gets renamed in Salesforce, but the transformation logic in Snowflake still assumes the old definition. A formula is updated to reflect a new business rule, while downstream models continue to encode the previous logic. A permissions change subtly alters which records are visible, changing aggregate metrics without touching a single line of SQL.
Nothing breaks. No jobs fail. No alerts fire.
The system keeps running — just on quietly diverging assumptions.
That’s data drift.
Most teams don’t detect drift directly. They notice it indirectly, through symptoms that feel frustratingly hard to pin down. A revenue number no longer matches across dashboards. A forecast looks plausible but doesn’t track with reality. An AI agent answers confidently, but gives different explanations depending on which system it pulls context from.
By the time those symptoms surface, drift has already compounded.
At that point, teams react the only way they can. Analysts reconcile numbers manually. Engineers trace pipelines backward through layers of transformations. Leaders start to lose trust — not just in AI outputs, but in the systems that are supposed to ground decisions in truth.
This is why the push for metadata standardization exists in the first place.
Semantic metadata is meant to capture meaning: what a field represents, how it’s calculated, how it relates to other data, and how it should be interpreted. When that meaning fragments across Salesforce, Snowflake, and the tools in between, AI doesn’t fail loudly. It guesses. And guessing is indistinguishable from intelligence until the moment it matters.
These standards promise a future where tools speak the same language. But they don’t tell you where your language has already drifted.
They don’t explain which Salesforce change last quarter altered the logic Snowflake is still enforcing today. They don’t show how a small schema update cascaded into downstream models, dashboards, and agents. And they don’t restore the shared understanding teams need to move quickly without fear.
Detecting data drift requires visibility before alignment.
It requires knowing how objects, fields, permissions, and transformations actually connect across systems — not how they were documented, not how they were intended, but how they behave right now. It means seeing how changes propagate, where old assumptions still live, and which downstream processes depend on definitions that no longer hold.
Data Drift is a Governance Problem
This is why drift is a governance problem before it’s an AI problem.
AI doesn’t introduce inconsistency; it amplifies it. When models and agents consume data at scale, every ambiguity becomes probabilistic. Every undocumented dependency becomes risk. Every silent mismatch in meaning gets multiplied across decisions, automations, and actions.
Standardization will help over time. Shared schemas and semantic frameworks are a step in the right direction. But no standard can retroactively explain why a system stopped agreeing with itself.
Until teams can see how Salesforce and Snowflake are actually connected — where meaning is shared, where it has diverged, and where it’s drifting — standards alone won’t restore trust.
They’ll just make the gap more visible.
Where Sweep Fits In
This is the gap Sweep is explicitly built to close.
Sweep gives teams a living, continuously updated view of how their systems actually work — across Salesforce, downstream data platforms, and the logic that connects them. Instead of relying on static documentation or post-hoc debugging, Sweep makes metadata visible as it changes: fields, formulas, permissions, automations, and the dependencies that quietly carry meaning from one system to another.
When something shifts in Salesforce, Sweep shows what depends on it. When assumptions diverge between systems, Sweep surfaces the mismatch before it shows up in dashboards, forecasts, or AI outputs. Drift doesn’t get discovered months later as a trust problem — it gets caught early as a governance signal.
That’s the difference between aligning systems in theory and understanding them in practice.
Standards help systems agree on language going forward. Sweep helps teams see where meaning has already drifted — and fix it before speed turns into risk.
Because the real work here is knowing, at any moment, what your systems are actually saying.