Idq summit2014 ronald damhof - it's all about the data

R.D.Damhof – October 2014 – IDQ Summit 2014
It’s all about the data
!
A managerial perspective
By Ronald Damhof

I am an opinionated kind a guy….
R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041
4
!

Who am I - My Data Manifesto
The X commandments of data management
!
I. Context is leading
!
II. Data is the ultimate proprietary asset, it is to be managed and
governed in line with morals & ethics, internal and external rules
and legislation
!
III. Stop center apps and process over data; data first, facts first
!
IV. It is all about the quality of our product; the data. Get clean,Stay
clean, Get access
!
V. Thou shall abstract
and separate concerns rigorously
!
4
!

!
!
VI. a) Thou shall make a fundamentalistic distinction between Fact
and Context
b) Thou shall not forsake ‘Time’
!
VII.Data architecture is not the same as technology architecture
!
VIII.The science and practice of Information & Data Modeling needs
to be uphold, improved and taught
!
IX. Specify, Standardise, Automate & Productise
!
X. Thou can not buy your way out of the data misery you are in
4
!

4
XI
There is a new saviour in town. Its name is Hadoop and
it calls to us from its mountain:
!
‘we got a lake and thou shall throw all your data in it. The
water will be clean so you can drink it, the water will flow so
it will irrigate your lands, grow your stock, feed your kids and
of course bring you world peace…..’
!
nah, kidding ;-)
!

R.D.Damhof – September 2014 – Data Modeling Zone

4

Logistics & Manufacturing

The Data Push Pull Point
Push/Supply/Source driven Pull/Demand/Product driven
▪ Mass deployment
▪ Control > Agility!
▪ Validation of “ingredients”
▪ Repeatable & predictable processes
▪ Standardized processes
▪ High level of automation
▪ Relatively high IT/Data expertise
▪ Piece deployment
▪ Agility > Control!
▪ Plausibility
▪ User-friendliness
▪ Relatively low IT expertise
▪ Domain expertise essential
All facts, fully temporal Truth, Interpretation, Context
Business Rules Downstream

The Development Style
Systematic
▪ User and developer are separated
▪ Defensive Governance; focus on control and compliance
▪ Strong focus on non-functionals; auditability, robustness, traceability, ….
▪ Centralised and organisation-wide information domain
▪ Configured and controlled deployment environment (dev/tst/acc/prod)
▪ User and developer are the same person or closely related
▪ Offensive governance; focus on adaptability & agility
▪ Decentralised,personal/workgroup/department/theme information domain
▪ All deployment is done in production
Opportunistic

A Data Deployment Quadrant
Systematic
Development
Style
Opportunistic
Push/Supply/Source driven Pull/Demand/Product driven
I II
III IV
Research,
Innovation &
Design
“Shadow IT,
Incubation,
Ad-hoc,
Once off”
Data
Push/Pull
Point
Facts Context

7 Applications of the Quadrant

(1) How we produce

How we produce, process variants

How we produce, automation
Rephrased - somewhat more nerdy:!
• Model-driven, metadata driven!
• Declarative instead of imperative !
!
Rephrased - somewhat more popular: !
“In Data, the developer is the data modeller”

How we produce, production lines
Production-line: Data orientation
Data Products Information
Products
Access to data
Analytical tools
Processing Power
Production-line: Forms orientation
Eg. XBRL

(2) How we organize

To centralize or to decentralize

(3) How we govern

How we govern, products

How we govern, accountability
Never, never, never ‘ownership’
In- en outbound
Data Delivery
Agreements
I II
III IV
Deliverant is
Accountable
Demandee is
Accountable
Data scientist/Analyst/Researcher responsible

(4) How do people excel

(5) How to use technology

(6) How about Technology
Storage: (R)DBMS
Processing: Automation Software
Data Quality: Validation, Profiling
Development: Data Modeling
Accessibility: Data Virtualization
Storage: Pattern based
Processing: Automation/limited ETL
Data Quality: DQ rules/dashboards
User tooling: Reporting, dashboards,
Data Visualization
Storage: Analytical
Processing: Preptools for Data Analyst
User tooling: Advanced Analytics,
Data Visualization

(7) Business-,Information- or
Data Modeling is key
The Logical Model drives the
technical data architecture,
design and implementation
Conceptual
Logical
e.g
Data Vault,
Anchor Model
Ontology
Facts
Relational
e.g.
Dimensional,
hierarchical,flat

Oh…data warehouse?
The classic distinction between ‘operational data
environment’ and ‘informational data environment’
is fading. "
!
Modern day data warehouses have been split up.
Where the ‘fact’-part (Q1) moved into the
operational side."
!
Although data warehouses have evolved,
operational applications have not, at least not in
terms of data architecture. They should though…..

Email:
ronald.damhof@prudenza.nl
Linkedin:
nl.linkedin.com/in/ronalddamhof/
Twitter:
RonaldDamhof
Blog:
prudenza.typepad.com
Website:
www.prudenza.nl

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