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

R.D.Damhof – Prudenza BV - Copyright - 22 mei 2014R.D.Damhof – October 2014 – IDQ Summit 2014

I am an opinionated kind a guy…. !


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 !

!



The X commandments of data management !!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

!

!


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 ;-) !


The X commandments of data management

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


Logistics & Manufacturing


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 Data Push Pull Point


Systematic

Opportunistic

▪ 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

The Development Style


Development Style

Systematic

Opportunistic

I II

III IV

Research, Innovation & Design

“Shadow IT, Incubation, Ad-hoc,

Once off”

Push/Supply/Source driven Pull/Demand/Product driven

Data Push/Pull

Point

ContextFacts

A Data Deployment Quadrant


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”


Production-line: Data orientation

Data Products Information Products

Access to data

Analytical tools

Processing Power

Production-line: Forms orientation

Eg. XBRL

How we produce, production lines


(2) How we organize


To centralize or to decentralize


(3) How we govern


How we govern, products


I II

III IV

Deliverant is Accountable

Demandee is Accountable

Data scientist/Analyst/Researcher responsible

How we govern, accountability Never, never, never ‘ownership’

In- en outboundData Delivery Agreements

http://appalachianconservative.files.wordpress.com/2009/08/milton_friedman.jpg


(4) How do people excel


(5) How to use 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

(6) How about Technology


(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

e.g. Dimensional,

hierarchical,flat

OntologyFacts

Relational


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…..


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

Data & Analytics

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