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.
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 other methodologies, please see table below:

Function	CryspIQ	Kimball	Inmon	Data Lake	Data Vault
Single source of truth across functions	✅	❌	✅	❌	❌
Data modelling skills	❌	✅	✅	✅	✅
Source system dependency	Flexible	Inflexible	Inflexible	Flexible	Flexible
Upfront effort	Low	Medium	High	Low	medium
Low technical dependency	✅	❌	❌	✅	❌
Speed to value	Fast	Medium	Slow	Fast	Medium
Training	✅	✅	✅	❌	✅
Scalability	✅	❌	❌	❌	✅
Change impacts	Low	Medium	High	Low	High
Lineage & traceability	Automatic	Manual	Manual	Manual	Manual
Data consistency	✅	❌	✅	❌	✅

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:

Function	CryspIQ	Databricks	Snowflake	AWS	Microsoft	Google
Embedded data governance for AI	✅	❌	❌	❌	❌	❌
Static data model	✅	❌	❌	❌	❌	❌
Master data unification	✅	❌	❌	❌	❌	❌
Store only relevant data	✅	❌	❌	❌	❌	❌
Small data footprint	✅	❌	❌	❌	❌	❌
In-built data quality	✅	❌	❌	❌	❌	❌
Low application change impacts*	✅	❌	❌	❌	❌	❌
IT / OT data modelled**	✅	❌	❌	❌	❌	❌
User self service (No modelling)	✅	❌	❌	❌	❌	❌
Text to SQL	✅	❌	✅	❌	❌	❌
Multi-cloud	✅	✅	✅	❌	❌	❌
Unstructured data	✅	✅	❌	❌	❌	❌

Please note:

*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 difference between the CryspIQ and traditional solutions is the number or layers involved in making the data ready for analytics. Traditional solutions normally have approximately three layers before the data is made available to the end user, whereas CryspIQ has one. These difference are shown below in the diagrams.

The CryspIQ Solution

The CryspIQ solution collapses the layers to measure quality at the point of entry and reduces the failure points in the processing and management of data. CryspIQ provides true business self service across your organisation which requires no technical intepretation or input. Ongoing support does not require specialist technical resources. This is demonstrated in the diagram below:

In the CryspIQ world, you have:

• a single technical toolset that manages a single layer for your data.
• the same technical toolset that conducts live data quality checks on your data.
• the same technical toolset that transforms or prepares your data for visualisation.
• the same technical toolset that catalogues your data.
• the same technical toolset that provides true self service (no technical knowledge required)

Result - You have 1 Licensing cost for the different functional requirements.

The Traditional Solution

Traditional data warehouse solutions are normally made up of a number layers. Each layer introduces a failure point in the processing and management of data. In most cases, there is a staging layer, a transform layer and visualisation layer. Data products are curated and presented in the visualisation layer to users for analytics. This creates a dependency on expensive technical resources to curate the data into a product. Ongoing support requires specialist technical resources. This is shown the diagram below:

In this traditional world, you have:

• a separate technical toolset to manage the layers for your data - Date Warehouse Toolset.
• a separate technical toolset to conduct adhoc data quality checks - Data Quality Toolset.
• a separate technical toolset to transform or prepare your data for the visualisation layer - Transformation Toolset.
• a separate technical toolset to catalogue your data - Data Catalogue Toolset.
• a separate technical toolset to provide self service of the data (you need technical knowledge of the data to use it)

Result - You have 5 licensing costs for the different functional requirements.