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 methodologies describe the architectural approaches used to organise, store, and manage data across an enterprise. These methodologies define how data is structured, accessed, governed, and ultimately consumed for analytics and AI use cases.

Methodology Definitions

The most commonly adopted data modelling methodologies are outlined below:

1. Bill Inmon
   A top-down, data-driven approach that begins with the creation of a centralised enterprise data warehouse. Data marts are derived from this warehouse to support reporting and analytics for individual business functions.

2. Ralph Kimball
   A bottom-up approach where dimensional data marts are built first to support specific analytical and reporting needs. These marts are later integrated to form a broader enterprise view.

3. Data Lake
   An approach that stores data in its native, raw format within object storage or file-based repositories. This category includes related patterns such as Lakehouse, Data Mesh, and Medallion architectures, which aim to improve structure, governance, and consumption over time.

4. Data Vault 2.0
   A modelling methodology designed for long-term historical data storage, focusing on auditability, traceability, and the integration of data from multiple operational source systems.

5. CryspIQ®
   An approach based on decomposing source records into granular, standardised data structures. Data is stored once at the lowest meaningful level and clustered by type, enabling consistent governance, reuse, and AI-ready analytics without repeated re-modelling.

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.

Compare Cloud Data Toolsets

CryspIQ® provides an end to end data fabric solution in a single toolset. We have provided a comparison against the most common cloud data solutions used in the market.

Please note that CryspIQ® can co-exist with your existing Enterprise Data Platform (EDP).

These are shown the table below:

Capability	CryspIQ®	Databricks	Snowflake	AWS Redshift	Azure Synapse	Google BigQuery
AI-ready data governance (by design)	✅	⚠️	⚠️	⚠️	⚠️	⚠️
Reduced AI retraining through stable models	✅	⚠️	⚠️	⚠️	⚠️	⚠️
Enterprise data model (pre-defined)	✅	❌	❌	❌	❌	❌
Master data unification	✅	❌	❌	❌	❌	❌
Stores only relevant, curated data	✅	❌	❌	❌	❌	❌
Small analytical data footprint	✅	❌	❌	❌	❌	❌
Built-in data quality rules	✅	⚠️	⚠️	⚠️	⚠️	⚠️
Low application change impact*	✅	❌	❌	❌	❌	❌
IT / OT data modelled together**	✅	❌	❌	❌	❌	❌
Self-service analytics (no modelling required)	✅	⚠️	⚠️	⚠️	⚠️	⚠️
Multi-cloud support	✅	✅	✅	❌	❌	❌
Native support for unstructured data	✅	✅	❌	❌	❌	❌

Key principles:

• ✅ = native / built-in / by design
• ⚠️ = requires additional tools, configuration, or engineering effort
• ❌ = Not provided as a native capability

*This refers to impacts when changing out source applications / technologies. There is a high cost for end to end rebuilds or model re-training.

**This refers to both IT / OT data (Sensor or time series data) being stored in the same database schema and modelled ready for analytical purposes.

Compare End-to-End Solutions

The key distinction between CryspIQ® and traditional data platforms is the number of processing layers required to deliver analytics-ready data.

Traditional architectures typically rely on multiple intermediary layers—often three or more—to ingest, transform, govern, and prepare data before it reaches end users. Each additional layer increases complexity, cost, and risk.

CryspIQ® simplifies this approach by consolidating these capabilities into a single, governed data layer. This reduces architectural overhead, accelerates time to insight, and minimises operational failure points.

The diagrams below illustrate the difference in architecture clearly.

The CryspIQ® solution

CryspIQ® streamlines enterprise data processing by measuring data quality at the point of entry, enriching data with contextual metadata in real time, and reducing downstream failure points.

It enables everyone across the organisation—regardless of technical expertise—to access, trust, and use data independently. Once adopted, data preparation and management processes become significantly simpler, with ongoing operation requiring minimal technical oversight and no reliance on specialist resources.

In the CryspIQ® model

• A single technical toolset manages one governed data layer across the enterprise
• The same platform performs real-time data quality validation at ingestion
• The same platform prepares and transforms data for analytics and visualisation
• The same platform automatically catalogues data and applies business context
• The same platform enables secure, self-service access for all users

The CryspIQ® result

A simplified data landscape delivered through one integrated platform and a single licence, covering ingestion, quality, governance, transformation, and self-service—without the complexity of multiple tools or overlapping contracts.

This is demonstrated in the diagram below:

The Traditional solution

Traditional data warehouse architectures are typically built from multiple processing layers—such as staging, transformation, and visualisation. Each additional layer increases architectural complexity, introduces more points of failure, and slows the delivery of analytics-ready data.

In these environments, data products are curated and exposed primarily within the visualisation layer. This creates a strong dependency on specialist technical resources to prepare, manage, and maintain data pipelines, often turning data teams into ongoing bottlenecks for the business.

In the Traditional model

• One technical toolset manages the underlying data layers
• Separate tools are used to perform ad hoc data quality checks
• Additional tools are required to transform and prepare data for visualisation
• Separate cataloguing tools are needed to manually define context and metadata
• Self-service access is limited to pre-interpreted data products

The Traditional result

A complex, fragmented data landscape supported by multiple platforms and licences—driving higher cost, greater operational risk, and continued reliance on scarce technical expertise.

This is shown the diagram below: