Comparisons

To compare CryspIQ® to other industry methodologies and market products, it is necessary look at the method and solution separately. These comparisons are provided below.

Industry Methodologies

Data Warehousing methods refer to architectural designs and structures used to organise and manage Data across an enterprise. These models determine how data is stored, accessed and used for analytical and AI purposes.

Methodology Definitions

The data warehousing methodologies are described below:

1. Bill Inmon - Method is the top-down or data-driven strategy, in which we start with the data warehouse and break it down into data marts.
2. Ralph Kimball - Method is the bottom-up approach where data marts are first created to provide reporting and analytical capabilities for a function.
3. Data Lake - Method is storing data within a system or repository, in its natural format, that facilitates the collation of data in object blobs or files. Includes Lakehouse, Meshes and Medallion Architectures.
4. Data Vault 2.0 - Method is designed to provide long-term historical storage of data coming in from multiple operational systems.
5. CryspIQ® - Method is the decomposition of source records to allow storage of the incoming data at a granular level clustered with data of like type.

Compare Methodologies

To compare CryspIQ® against established data modelling methodologies, see the table below:

Capability	CryspIQ®	Kimball	Inmon	Data Lake	Data Vault
Single source of truth across functions	✅	❌	✅	❌	❌
Data model provided out-of-the-box	✅	❌	❌	❌	❌
Dependency on source systems	Flexible	Inflexible	Inflexible	Flexible	Flexible
Upfront implementation effort	Low	Medium	High	Low	Medium
Low reliance on specialist resources	✅	❌	❌	⚠️	❌
Speed to initial value	Fast	Medium	Slow	Fast	Medium
Specialist training required	Low	Medium	Medium	High	Medium
Scalability by design	✅	❌	❌	❌	✅
Impact of change over time	Low	Medium	High	Low	High
Lineage and traceability	Automatic	Manual	Manual	Manual	Manual
Consistent enterprise definitions	✅	❌	✅	❌	✅

Key principles:
✅ = Native capability by design.
⚠️ = Achievable with additional engineering effort.
❌ = Not provided as a native capability.

What a data platform does not provide

CryspIQ® is not an alternative to Databricks, Snowflake, Redshift, Synapse or BigQuery. It runs on top of whichever of them you already have — see co-existence for how.

Those platforms solve storage, compute and scale, and they solve it well. CryspIQ® depends on that work rather than repeating it.

What a data platform does not supply is a model: the enterprise definitions, the master data structure and the business meaning that turn stored data into trusted answers. Every one of them expects you to build that yourself, and correctly so — a general platform cannot ship your organisation's definition of a customer.

That is a difference in purpose, not in quality. The two are measured on different things:

• A data platform is measured on capacity and capability — how much you can store, how fast you can process it, how much you can do with it.
• CryspIQ® is measured on Enterprise Data Efficiency — how much trusted business value you get per unit of data effort. See what that means.

Those axes move independently. Adding capacity does not make an organisation more efficient at turning data into answers, which is why data spend and reporting confidence so often move in opposite directions.

So the table below is not a scorecard. It sets out what remains your responsibility on any data platform, and what CryspIQ® provides as part of the model.

Capability	On a cloud data platform	With CryspIQ®
Enterprise data model	You design and build it	Pre-defined, patented, ready on day one
Master data	You choose an approach and maintain it	A single instance per party, enforced by the structure
Business definitions	Held in a separate semantic layer you maintain	Carried on the fact type, alongside the data
Data quality	Applied downstream, per pipeline	Assessed as data loads, scored organisation-wide
What gets stored	Whatever you land, in source shape	Only the elements that matter, decomposed by type
Replacing a source application	Rebuild the pipelines and models that depended on it	Map the new source; history is unaffected
IT and OT data	Modelled separately, joined later	Modelled together in one schema
Self-service analytics	Requires modelling before business users can serve themselves	No modelling step between question and answer
AI readiness	An initiative you run	A property of the model

The middle column is not a criticism. It is what a general-purpose platform is for — flexibility, and the freedom to model your business however you choose. The cost of that freedom is that somebody has to exercise it, repeatedly, for every source and every change.

CryspIQ® trades that flexibility for a model that is already made. Where the flexibility is worth more than the time, a platform alone is the right answer. Where the modelling work has become the constraint, it is not.

Where the layers go

The reframe above is about what a platform gives you. This one is about what sits between the platform and an answer.

A traditional stack reaches analytics-ready data through several processing layers, and each layer exists for the same underlying reason: to add a bit more meaning. Staging holds the data as it arrived. Transformation reshapes it. A semantic layer names the measures. The visualisation layer interprets what those measures mean for a particular audience.

Meaning accumulates gradually, and it accumulates in tools rather than in the data. That has three consequences worth naming:

• Every layer is a place the meaning can be applied differently, which is how two reports disagree
• Every layer needs building and maintaining, which is where the engineering time goes
• Meaning defined in the visualisation layer is only available to that tool, so the next consumer starts again

CryspIQ® attaches meaning at entry — the fact type carries the business definition, and quality is measured as data loads. Once meaning is already attached, the layers whose job was to add it later have nothing left to do.

What is actually removed

Not your data platform. You keep it, and CryspIQ® reads from the raw or staging layer you already populate — see co-existence.

What goes is the tooling that accumulated around it to compensate for meaning arriving late: the separate quality tool, the separate catalogue, the transformation layer built per consumer, the semantic layer maintained alongside the warehouse. Those are the licences, the pipelines and the specialist time that a shorter path makes unnecessary.

So the cost argument is not "one licence instead of many" — you are adding CryspIQ® to a platform you keep paying for. It is that a large amount of the tooling and engineering that normally surrounds a warehouse exists to solve a problem CryspIQ® solves earlier.

The two shapes, side by side

The bar on each layer shows how much business meaning the data carries at that point.

Sources, ingestion and consumption are the same in both. The only thing that changes is how much sits between them — and how long the data goes without carrying its business meaning.

What that changes in practice

• One place where meaning lives, so reports built by different teams agree because they are reading the same definitions rather than reimplementing them
• Quality measured at entry, so a defect is caught on the way in rather than found by whoever consumed it
• Less to maintain between source and answer, which is where most of the recurring engineering cost sits
• Self-service without a modelling step, because the model a business user needs is already there

The honest limit: this shortens the path, it does not remove the work. Sources still have to be connected and mappings still have to be confirmed by someone who understands the business. What changes is that the work happens once, at entry, rather than repeatedly in every layer downstream.