Methodology
To provide some further background on the CryspIQ® Methodology, more details on the approach, patent and inventor are provided below. Refer to following items for more detail on the background to the methodology:
Compare to Other Industry Methods
Approach
The CryspIQ® approach consists of applying the method to store the Data in the Universal Context.
The Method
The CryspIQ® method involves the deconstruction of source records to allow the storage of the incoming data in its most granular form. It clusters data of like type from all inputs into a single data schema meaning that the underlying data structures used to store the data remains static by nature. With an element of training to understand the static structure, all data for an organisation becomes available for your entire organisation to consume.
The Universal Context
The CryspIQ® universal context is functionally agnostic fine-grained Operational data store of factual detail (past, present and potentially future) that represents any data source’s specific elements in a single organisation context, irrespective of organisation type, source system or desired downstream use.
Is a fixed model a limitation?
It is the first objection most people raise, and a fair one. Rigid schemas are exactly why a generation of data warehouse projects disappointed, so "the model is already defined" sounds like the same mistake with better marketing.
It is not, and the reason turns on a distinction worth being precise about.
Fixed structure, open content
What is fixed is the structure: the seven typed fact stores, the dimensions, and the relationships between them.
What is entirely open is the content: every fact type, every entity, product, service and location, and every business definition attached to them.
That split is not arbitrary. The model is fixed at the level where businesses are genuinely the same, and open at the level where they actually differ.
No organisation is unique in needing to record money, quantities, references, events, positions, rates and documents. That is a complete account of what a transaction record can contain. Organisations differ in what those measurements mean — and that is precisely the layer CryspIQ® leaves to you.
Your specificity lives in the vocabulary, not in the table layout.
Star schemas failed for the opposite reason
This is where the objection misfires.
Kimball and Inmon models are rigid at the content level. You model a sales fact, with those measures, against those dimensions, for one business process. A new process means new tables, new pipelines, new everything. That specificity is what made them brittle.
CryspIQ® is fixed at a level above that. It does not model your sales process at all — it models a monetary value, of a defined type, about a party, at a place, at a time. A new business process is new rows, not new DDL.
So it is not "rigid like a star schema, only more so". It is fixed in the one place a star schema is flexible, and flexible in the place a star schema is fixed. Generic structure is what makes it stable; specific structure is what made the others brittle.
The precedent
Double-entry bookkeeping has a fixed structure — every transaction has two sides, debits equal credits — and it has been unchanged for five hundred years across every business on earth. No organisation has ever concluded it was too unique for double-entry.
The structure is fixed. The chart of accounts is entirely yours. That is the same trade, and it is the reason accounts from any two companies can be compared at all.
What flexibility actually costs
It is worth asking what the alternative buys.
Freedom to model however you like is also freedom for every team to model differently. That is definition multiplicity — the problem the exercise was meant to solve. An organisation cannot have both one enforced definition of customer and anyone may shape the model however they wish; the constraint is what makes the single instance possible.
It is also where the cost sits. Every new source becomes a modelling decision, every change becomes a project, and none of it is reusable. The flexibility is the bill.
Where it genuinely constrains
Three honest limits, because a method that claims no trade-offs is not being described accurately.
You cannot invent a new kind of fact. The seven types are a product decision, not configuration.
A purpose-built star schema, tuned for one known report, will outperform a generic model on that one report. You are trading per-workload optimisation for enterprise-wide consistency.
Some questions are harder to express against a generic model than against a schema shaped around them, and answering them well depends on the query layer rather than the model.
The trade is deliberate: give up local optimisation and per-project freedom, gain a model that does not need redesigning every time the business changes.
Inventor
Vaughan Nothnagel is an African data architect and the registered inventor of the CryspIQ® data modelling method for data warehouses and business intelligence. First envisioned around 2010, the Crysp Team developed the model in 2016 and formally published the first version in the early 2018. In 2023, CryspIQ® 2.0 was announced and it was released in 2024.
Compare CryspIQ® to other Industry Methodologies and other Cloud Data Warehousing products in the market.
Patent
This gives you an understanding of what the CryspIQ® patent contains.
Description
The CryspIQ® Patent utilises a combined practice of: –
- A ‘single organisational context’ to overcome nomenclature differences across different areas of the organisation (normalisation and standardisation of context);
- A ‘transactional de-composition’ philosophy forcing the deconstruction of individual elements from source systems and thereby disassociating them from their original structural format constraints and their source of origination (information decomposition);
- Recording only specific elements of the source data (as opposed to the whole record) within your organisation (One-off build) as one or more of the finite types of data resulting in storage savings of up to 90%;
- Retention of time-based sensitivity for event driven recording; and
- Multi-Dimensional representation of the data types allowing cross business domain analysis and reporting.
Issued
This the current list of the patents that have been issued around the world:
| Date | Country | Patent # |
|---|---|---|
| 02-Oct-2019 | Singapore | 11201806825R |
| 01-Apr-2022 | Japan | 7051108 |
| 03-May-2022 | China | ZL 2017 8 0013415.4 |
| 03-Jun-2022 | United States | US11372880B2 |
| 22-Jul-2022 | Hong Kong | HK1255050 |
| 20-Apr-2023 | Australia | 2017224831 |
There are further patents pending in other countries around the world and will be added to this list when they are issued and finalised.