The document explores the Data Vault 2.0 methodology, highlighting its advantages over traditional data warehousing approaches like Kimball and Inmon. It addresses current challenges in data management, including regulatory compliance, data quality issues, and the need for automation. The Data Vault framework is presented as an adaptable, scalable solution for modern data environments, emphasizing its vendor-agnostic nature and ability to handle various data types.

1. DEMYSTIFYING DATA VAULT
2.0

2. What is the current landscape for
Information Managers?

3. Security
Open Data
» Lockdown vs. Democratisation
» Redaction
» De-personalisation
Regulatory compliance
» GDPR
» SOX
» FMA/APRA
» Cloud / ISO27001
External factors

4. GDPR
› This is likely to be replicated in
New Zealand
› Security Data Breach Notification Law in
effect as at 25 May 2018
› Lack of compliance will result in
heavy penalties

5. › More data more often
› New data sets all the time
› Data Quality challenges
› Corporate memory
› Reliance on individuals
› Reconciliation and audit
Internal factors

6. What else?

7. Data Vault vs.
Kimball/Inmon/L
ake

8. What’s wrong with traditional Data Warehousing methods?
‘Shadow IT’
Big Data
E
T
L
M
A
R
T
S
3rd party
data
Source
System
Source
System
JSON/XML
Semi-
structured
Unstruct-
ured data
BI
Analytics
Data Science

9. Automation Framework
Data Acquisition
Real-Time
CDC
Messaging
Batch
ETL/ELT
Files
PDF, Docs,
Video, etc.
Staging
Raw
Data
Vault
Data Provisioning
Information Governance - Metadata Management, Lineage, Data Quality
Big Data/No SQL
Information
Marts
(Virtual/Physical)
Operational Data
Store
Business
Data
Vault
Enterprise Data
Vault
Data Vault: Scalable, Extensible, Agnostic
3rd party
data
Source
System
JSON/XML
Semi-
structured
Unstruct-
ured data
BI
Analytics
Data Science
"It is not the strongest of the species that survives, nor the most intelligent
that survives. It is the one that is the most adaptable to change." Charles
Darwin

10. Data Lake
› Data flows in ‘naturally’
› Some boundaries
› Content flows out with little constraint
Data Swamp
› Uncontrolled flow
› No borders and potentially bottomless
› Filled with flotsam and jetsam
Data Harbour
› Controlled flow
› Trust in the delivery and content of data
› Built to use and extract value from the data

11. What is Data Vault?
› Invented by Dan Linstedt in the late 1990s, Data Vault is
a System of Information Delivery containing the
necessary components needed to accomplish
enterprise vision in Data Warehousing and Business
Intelligence
› Data Vault includes a data modelling technique for data
repositories that has significant advantages over
traditional methodologies: auditable, extensible,
automated
› With the release of Data Vault 2.0 in 2013, it extended
from just the data model to a full methodology
› Is effectively vendor agnostic: works with multiple data
processing tools, relational databases, and file stores

12. DV2.0 System: Foundational Pillars
Methodology
Architecture
Model
• Consistent & Repeatable
• Pattern Based
• Automation
• Agile
• Multi-Tier
• Scalable
• Supports NoSQL
• Insert only architecture
• Flexible
• Scalable (Big Data)
• Hub & Spoke

13. Demystifying Data Vault
Automati
on
Integrati
on
Governa
nce