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
Defined Enterprise Data Model	✅	❌	✅	❌	❌
Single Source of Truth across Functions	✅	❌	✅	❌	❌
Low Technical Dependency	✅	❌	❌	✅	❌
Source Flexibility	Flexible	Inflexible	Inflexible	Flexible	Flexible
Application Change Impact	Low	Medium	High	High	Low
Data Quality	Measured	Limited	Measured	Limited	Limited
Upfront Effort	Low	Low	High	Low	Medium
Speed to Availability	Fast	Fast	Slow	Fast	Fast
End User Training	✅	✅	✅	❌	✅
User Self Service	✅	❌	❌	✅	❌
Lineage & Traceability	Automatic	Manual	Manual	Manual	Manual
Data Consistency	✅	❌	✅	❌	✅
Method Scalability	✅	❌	❌	❌	✅
Enables Automation	✅	❌	❌	❌	✅

Cloud Enterprise Data Platforms

CryspIQ is integrated Cloud Enterprise Data Platform (EDP) with a unique Data collection method. We have provided a comparison against the most common Cloud EDPs used in the market. These are shown the table below:

Function	CryspIQ	Databricks	Snowflake	AWS	Microsoft	Google
Methodology	CryspIQ	Lake	Lake	Lake	Lake	Lake
Single EDP Toolset	✅	❌	❌	❌	❌	❌
In-Built Data Quality	✅	❌	❌	❌	❌	❌
Low On-going costs	✅	❌	❌	❌	❌	❌
Static Data Model	✅	❌	❌	❌	❌	❌
Skillset Dependencies	❌	✅	✅	✅	✅	✅
Only Factual Data*	✅	❌	❌	❌	❌	❌
Small Data Footprint	✅	❌	❌	❌	❌	❌
Low Change Impacts**	✅	❌	❌	❌	❌	❌
Multi-Cloud	✅	✅	✅	❌	❌	❌
Separate Storage and Compute	✅	✅	✅	❌	✅	✅
Industry Standard SQL	✅	✅	❌	❌	❌	✅
Structured Data	✅	✅	✅	✅	✅	✅
Unstructured Data	✅	✅	✅	❌	❌	❌
Time Series Data***	✅	❌	❌	❌	❌	❌
Text to SQL	✅	✅	❌	❌	❌	❌

Please note:

*This may feel like you are missing Data because you are only ingesting the buiness factual data. However any missing data is a "mapping" away from being landed in CryspIQ. Thus, you are not actually missing any Data, but rather selecting (sometimes known as business critical data) what's important to your business.

**This refers to costs when changing out source applications / technologies and the impacts of doing so. Its a high cost for end to end rebuilds.

***This is refers to both IT / OT Data being stored in the same database schema 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

CryspIQ Data Warehouse 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: