roi

The ROI multiplier argument works at three levels — cost avoidance, time compression, and compounding returns.

The core math

A typical enterprise Snowflake migration runs $400k–$800k in platform licensing, compute, and SI fees. The hidden follow-on cost that most teams don't budget for is the data catalog and governance retrofit — usually a separate 4–6 month project that costs another $150k–$300k and starts from scratch because nothing was instrumented during the migration itself.

ARCXA eliminates that second project entirely. When attached at roughly 10% of migration cost, it produces the catalog as a byproduct of the migration — every schema move, transformation, and lineage relationship is recorded in the NNX graph in real time. The catalog isn't a post-project deliverable. It's a side effect.

The three ROI levers:

1. Time to value — Snowflake's value is only realized when analysts and engineers can find, trust, and query the data. Without lineage documentation, that trust-building takes months of tribal knowledge reconstruction. With ARCXA, the NNX graph is queryable on day one — every field has a traceable origin, every transformation is documented, every deprecated source is flagged.

2. Scope control — as covered in the dark data discussion, ARCXA's pre-migration crawl typically removes 30% of assets from migration scope. On a $500k engagement, that's potentially $150k in Snowflake storage and compute you never provision. The tool pays for itself before migration even begins.

3. Audit cost avoidance — regulated industries (finance, healthcare, retail) face mandatory lineage audits post-migration. Without ARCXA, those audits require manual reconstruction of what moved and how. With the NNX graph, audit responses are SPARQL queries. What used to take weeks of consultant time becomes hours.

The compounding angle — where the multiplier really lives

The single most underrated aspect of the ROI argument is that the NNX graph doesn't reset between projects. Every Snowflake migration ARCXA touches adds to the knowledge graph. The second engagement starts with the intelligence from the first. By the third migration, your team has a proprietary mapping library — field-level transform patterns, common schema equivalences, known data quality issues in legacy systems — that competitors building fresh spreadsheets every time simply don't have.

For SIs and integrators, this is the product moat. You're not just delivering migrations faster — you're building an asset that makes every future migration cheaper to scope, faster to execute, and lower risk to guarantee. That's the multiplier: not just 10% attach on one deal, but compounding margin improvement across every deal in the portfolio.