Often I see companies define their data strategy by simply choosing the top tools from the latest quadrant. Is your approach more about technology selection, or about shaping a strategy that delivers measurable value?
At its core, an asset is something that creates value and drives growth—directly or indirectly. It should strengthen resilience, fuel innovation, and deliver competitive advantage.
Now consider your data: Is your Enterprise Data Platform positioned as an asset, or is it on track to become a liability?
As your business expands, the foundations you set for your data become critical. A true data asset reduces reliance on ever-changing applications and removes the need for specialist technical skills just to interpret the numbers.
"Lakehouses", "Lakebases", "Meshes", and "Medallion Architectures". Regardless of the "buzzword" being used, it's essential to understand the underlying methodology, as these ones all follow the same foundational Data Lake methodology — yet the core business question often remains unanswered or simply assumed. Before committing to a data journey that typically spans 3–5 years and costs in excess $25 million — an approach frequently promoted by industry quadrants, shaping strategic architecture decisions — maybe worth considering the following points:
Data Lakehouses becoming Super Data Swamps highlights the strategic risk organisations face when modern data platforms scale without proper governance or value discipline.
The application of data governance often lags well behind the enthusiasm for filling data lakes and lakehouses. With the rapid adoption of AI, that gap is no longer theoretical—it’s becoming a costly and unavoidable reality for many organisations.
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.
We are happy to announce🎤 the release of CryspIQ® v3.0❤️.
Introduces:
We are happy to announce CryspIQ 2.0.
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.
Ability for users to import csv files into CryspIQ.
Lots of regression testing to ensure smooth transition to Cloud.
Developed ability to import CSV files into CryspIQ. This has helped the process of delivering Proof of Value to different clients.
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.
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: -
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.