Yes.
CryspIQ® is both a methodology and a technology-enabled approach to modern data management. It rethinks how data is captured, prepared, and delivered for analytics—focusing on simplicity, speed, and accessibility for all users, not just technical specialists.

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How CryspIQ® Compares to Traditional Methods​

Aspect	Traditional Methods	CryspIQ® Methodology
Processing Layers	Multiple (e.g., staging, transformation, visualisation)	Single, unified layer
Data Quality Checks	Often post-ingestion or mid-pipeline	Embedded at point of entry
Metadata Handling	Added manually or in later stages	Automatically enriched in-flight
User Accessibility	Requires technical resources to curate and prepare data	Self-service for all users, regardless of technical ability
Speed to Insight	Slower due to multiple handoffs and dependencies	Faster due to simplified flow and fewer failure points
Support Requirements	Ongoing support from specialist technical teams	Minimal support; does not require deep technical expertise
Failure Points	More layers = more potential points of failure	Fewer layers = fewer failure points

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In Summary​

CryspIQ® simplifies the data value chain by collapsing complex processing into a single, intelligent layer. It replaces the need for multiple traditional tools and roles with an intuitive, governed, and scalable method that delivers faster insights—without compromising quality or control.

We are happy to announce🎤 the release of CryspIQ® v3.0❤️.

Text to SQL 😀​

Introduces:

• A simplified UX experience.
• The ability to use natural language to query data.
• The capability for everyone to use data for decisions.

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Data Quality ⭐️​

Which:

• Has a live view of data quality issues by business function.
• Keeps your teams accountable using a whole of enterprise approach.
• Has a live view of data quality issues by steward that need fixing.

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Parking Lot 🅿️​

Which:

• Implements the self-healing contextual data mechanism.
• Provides visibility of which contextual data is missing from their data.
• Identifies the relevant steward required to resolve these outstanding tasks.

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Data Catalogue 📋​

Allows users to see:

• What data they have access to.
• What data which has poor quality.
• What data is waiting for contextual metadata.

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We are happy to announce CryspIQ 2.0.

Move CryspIQ from on Premise to SAAS.​

Updated code base to integrate with Microsoft Entra ID and convert all code libraries to run on Cloud Infrastructure. Resolve Code dependencies and set up connectivity between cloud elements. Upgrade dependencies.

File Importer​

Ability for users to import csv files into CryspIQ.

Regression testing​

Lots of regression testing to ensure smooth transition to Cloud.

Source File Importer​

Developed ability to import CSV files into CryspIQ. This has helped the process of delivering Proof of Value to different clients.

"We're building something amazing"​

When I spoke to Vaughan a couple of years ago he told me that Dan and he was building something amazing. He spoke about a data model that can take in any type of data and report on it.

Having worked in large corporate and government data warehouse environments, I was naturally sceptical as you may be. I also fiddled with universal data models when I was studying at university, so the project and product had my attention.

I really wanted to see it in action and after spending some time on the East Coast in another BI role and trying my hand as an IT Manager, I joined the CryspIQ team.

A pragmatic business problem resolution perspective​

The hype cycle of big data has brought a number of single stream suggestions to bear in resolving the world’s insatiable appetite for information. There are 3 primary solution focus areas that show intent in solving the big data problem, each with their own specific benefit: -

• Technology – Database fragmentation, Multi thread parallelism, Logical and Physical Partitioning;
• Infrastructure – Processor Componentisation, Tiered Storage Provisioning, Hardwired Distributed Storage; and
• Application – Data De-Normalisation & Aggregation, Read-ahead logic, Pre Process aggregation, Performance re-engineering.

The truth be told, each specific solution in isolation provides of itself an improvement opportunity and each will bring varying degrees of success to an organisations ability to handle the big data problem if, and only if, the right questions are asked. Only through this rigorous analysis and functional de-composition can one select the right method(s) for resolution to provide long term resolution as opposed to short term ‘disguising’ of the issue.

We are happy to announce CryspIQ 1.0.

For two and a half decades or more, organisations have followed one of the two data warehousing principles of Inmon (CIF, GIF and DW2.0) and Kimball (Star Schema Fact models) for preparation of data. Both methods have intrinsic benefits but leave organisations with some challenges in accessing warehoused data. More recently Amazon S3 has provided data lake capability which similarly requires deep IT knowledge and a somewhat prescriptive data understanding to be of downstream value.

By taking the best of both data warehousing practices a new paradigm is possible however, one fundamental mindshift is required to bring the value of data closer to the surface for business self service, analytics and reporting. This mindshift is the breaking of the human paradigm of clustering data in a format that represents the data source (Transactions remain transactions, readings remain readings and functional records remain functional records) as this is the area that restricts data from being used in a more abstracted way.

For more than 25 years now, businesses have followed a pattern of mapping business transactions in their own context into a data warehouse. Realistically, this practice, driven by the ideals and methodologies of either Bill Inmon or Ralph Kimball, has to-date provided a great ability to re-format transactional data into a model that enables rapid retrieval and consistent enterprise reporting platforms. I believe that through this analytics evolution cycle, we have become a victim of our own design in that the value delivered from a data warehouse built in this manner is, by and large, not conducive to encouraging new questions to be asked of your business data. This stifled pattern of use leads us to, typically, answer questions from complex data warehouses for answers that we could just as well have built a static report from the operational system.

What’s the point you might ask, well, simplistically, if the business transaction from one area in your organisation looks like and apple and another looks like an orange, the reality is that it is difficult to compare or analyse the two together. There are a number of anomalies of which timeousness of information, granularity and the absence of common associative information are but a few that restrict us in doing this effectively. So, if we insist on making each warehouse entry look like the apple or orange from where it was sourced, the warehouse is, by design, failing to deliver much if any value other than migration of the reporting platform to a different system.