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 AI and analytical 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 one to store the incoming data at the granular level clustered with data of like type.

Compare Methodologies

To compare CryspIQ against other methodologies, please see table below:

Function	CryspIQ	Kimball	Inmom	Data Lake	Data Vault
Single Source of Truth across Functions	✅	❌	✅	❌	❌
Data Modelling skills	❌	✅	✅	✅	✅
Source System Dependency	Flexible	Inflexible	Inflexible	Flexible	Flexible
Upfront Effort	Low	Low	High	Low	High
Low Technical Dependency	✅	❌	❌	✅	❌
Speed to Availability	Fast	Fast	Slow	Fast	Fast
Training	✅	✅	✅	❌	✅
Scalability	✅	❌	❌	❌	✅
Change Impacts	Low	Medium	High	Low	High
Lineage & Traceability	Automatic	Manual	Manual	Manual	Manual
Data Consistency	✅	❌	✅	❌	✅

Compare Cloud Data Solutions

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
Static Data Model	✅	❌	❌	❌	❌	❌
Master Data Unification	✅	❌	❌	❌	❌	❌
Business Relevant Data	✅	❌	❌	❌	❌	❌
Small Data Footprint	✅	❌	❌	❌	❌	❌
In-Built Data Quality	✅	❌	❌	❌	❌	❌
Embedded Data Governance for AI	✅	❌	❌	❌	❌	❌
Low Application Change Impacts*	✅	❌	❌	❌	❌	❌
IT / OT Data Modelled**	✅	❌	❌	❌	❌	❌
User Self Service (No Modelling)	✅	❌	❌	❌	❌	❌
Text to SQL	✅	❌	✅	❌	❌	❌
Multi-Cloud	✅	✅	✅	❌	❌	❌
Unstructured Data	✅	✅	❌	❌	❌	❌
Real-time 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. These difference are shown below in the diagrams.

CryspIQ Solution

The CryspIQ Solution collapses the layers to measure quality at the point of entry and reduce failure points in the processing and management of Data. This is demonstrated in the diagram below:

Traditional Solution

Standard Data Warehouse Solutions usually consist of a number of different layers usually built with different technology toolsets. This is shown the diagram below: