MinusOneDB for Data Providers & Brokers — Identity-Centric Data Monetisation

A Perfect Storm Is Attacking Margins

Current cloud providers extract a massive compute tax from query-heavy workloads. At a typical floor price of $2–10 per TB/query, a single query on a petabyte costs $10,000. The questions you need to ask yourself:

Signal loss everywhere. Cookies and MAIDs are increasingly unreliable while every walled garden invents a new ID.
Exploding channel count. CTV, retail media, in-app, DOOH, audio—each with its own event fire-hose.
Cloud-cost chaos. Pay-per-query warehouse bills are wildly out of control.
Stale identity graphs & update costs. Overnight Spark jobs can’t keep pace with real-time auctions and site events—and each batch update is expensive.
Product velocity gridlock. New data products only launch quarterly because data scientists wait on ETL queues.
Ops complexity. Seven systems (stream, lake, warehouse, feature store, model DB, queue, BI) means significant resources dedicated to maintenance.
Margin squeeze from giants. Walled gardens push their proprietary audiences, eroding broker relevance.

Where the Storm Hits Revenue

And how MinusOneDB fixes each pain point.

Business Problem	Status-Quo Reality	MinusOneDB Capability	Immediate Win
Signal loss — cookies & MAIDs crumble	Fragmented identity stitched in nightly Spark jobs	IdentityForge — deterministic + probabilistic match on every event in seconds	Live graph updates ~2 s → significant match-rate improvements, fresher segments
Exploding channel count	Seven-system stack strains pipelines	MinusOne Core — single distributed-search store; true streaming ingest visible in ~2 s	Fewer failure points, simpler ops, much faster development
Cloud-cost chaos	Columnar warehouses charge $2–10/TB/query	Capacity-based pricing + constant-time queries	80–95% lower spend; cost now forecastable
Stale identity graphs	Micro-batch ETL latency; build jobs 6–24 h	IdentityForge + streaming ingest	Audiences refresh continuously → fewer mismatches
Product velocity gridlock	Data scientists wait on ETL queues & separate model DB	ModelForge + constant-time scoring on the primary index	Idea-to-launch in weeks, not quarters; more SKUs per year
Ops complexity & audit risk	Copies of PII proliferate across lake, warehouse, queue	CleanForge — hardware-isolated rooms spun up quickly	Fewer data copies, one ACL surface → easier compliance sign-off
Margin squeeze from giants	Walled garden proprietary data	Core price/performance + live identity keeps match-rates high	Recover pricing power; compete on freshness & economics

A Fundamentally Different Architecture

MinusOneDB collapses the warehouse, lake, stream processor, feature store, and queue into one rebuilt distributed-search datastore that is 100–1000x more efficient per query on a price/performance basis. Storage, not compute, bears the bulk of the query workload.

Constant-Time

Distributed Search

Rebuilt distributed search architecture traverses petabytes in seconds through optimised index structure.

~2s

Write Visibility

True streaming ingest—each write is index-visible in ~2 seconds. No micro-batch lag or complicated ETL pipelines.

~3 hrs

Deterministic Rebuild

Any dataset at any scale rebuilt from object store in ~3 hours—essential for disaster recovery, DevOps, and data sovereignty.

80–95%

Lower Spend at Scale

Capacity-based pricing—you lease infrastructure, not queries. ~5M queries/mo on base capacity.

Our architecture delivers eventually consistent semantics (typically under 2 seconds), sufficient for most data broker workloads. For true ACID requirements, we offer integration with transactional stores while maintaining performance advantages for query-driven workloads.

Seven Systems → One Platform

Stream processor, data lake, warehouse, feature store, model database, queue, BI layer—all replaced by a single foundation with purpose-built modules on top.

Module	Purpose	Timeline
MinusOne Core	Distributed-search primary datastore; constant-time operations	2–4 weeks
IdentityForge	Deterministic + probabilistic matching on every event (seconds latency)	3–4 months
ModelForge	Parallel look-alike / propensity / fraud modelling at scale	3–4 months
CleanForge	Hardware-isolated workspaces with lineage & governance controls	1–2 months

IdentityForge & ModelForge

IdentityForge: Real-Time Identity Resolution

Continuous identity resolution: process tens of billions of identity signals as they arrive
Complete identity lineage: preserve full history of how identities evolve, merge, and split
Confidence-based modelling: evaluate trustworthiness of each data point across multiple dimensions
Hybrid identity approach: combine deterministic and probabilistic techniques with machine learning
Self-correcting architecture: automatically reprocess data when rules change

ModelForge: Hyper-Scale Data Modelling

Continuous experimentation: test hundreds of data models and attribute definitions simultaneously
Real-time adaptation: update data products instantly as new signals arrive
Scale-independent modelling: score billions of profiles against thousands of models daily
Distributed architecture: process large-scale behavioural signals, extract actionable intelligence
Predictable economics: fixed-cost platform that eliminates query pricing concerns

CleanForge: Hardware-Isolated Clean Rooms

Spin up isolated workspaces for partner collaboration, compliance, and governed analytics—without moving raw data.

Federated Queries

One logical query spans every partner while raw rows stay home. No data movement, no copies proliferating across systems.

Flat-Cost Analytics

Capacity pricing kills the per-query tax and rewards exploration. Run as many analyses as needed without budget gates.

Hardware Isolation

Each workspace runs in its own isolated environment with full lineage tracking and governance controls. Fewer data copies, one ACL surface.

Plug-In Privacy Controls

Built-in masking plus IdentityForge/ModelForge extensions. No bespoke privacy engineering required.

The MinusOneDB Data Provider Advantage

Data Science Teams

Superior identity resolution through continuous processing and fine-grained matching. Broad attribute testing without incremental compute costs. Real-time data enrichment for higher-value products.

Product Teams

Accelerated time-to-market for new data products and segments. Higher-value attributes through continuous experimentation. Partner-ready APIs for immediate activation.

Technology Teams

Simplified data architecture that reduces maintenance burden. Predictable costs through capacity-based pricing. Scalable performance that grows with your data.

Revenue Teams

Higher CPMs through differentiated attribute packages. New monetisation models enabled by real-time data delivery. Expanded use cases including clean room and activation opportunities.

Pricing & Specs

Capacity-Based Pricing

$1,575/mo base capacity
$1,200/TB/mo storage
~5M queries/mo on base capacity
80–95% lower spend at scale vs pay-per-query warehouses

Technical Details

REST API + JS SDK
SOC 2 certified
Write visibility: ~2 seconds
Dataset rebuild: ~3 hours from object store
Eventually consistent (sufficient for data broker workloads)

Run Your Numbers

Implementation Approach

Phase	Timeline	What Happens
Discovery	2–3 weeks	Technical assessment of current data architecture and monetisation processes. Identity resolution evaluation and opportunity mapping.
Proof of Value	6–8 weeks	Implementation with 10–20% of your data. Identity graph migration and enhancement. Side-by-side performance comparison.
Full Deployment	8–12 weeks	Complete transition of identity graph and data processing. Partner integration and activation. Knowledge transfer to your teams.
Ongoing Optimisation	Continuous	Improvement of identity resolution and data models. Regular business reviews focused on monetisation impact. New use case development.

First meaningful business impact typically arrives in 6–8 weeks with the proof of value phase.

Book a Discovery Session

MinusOneDB forData Providers & Brokers