Sas insight sessie data management - Data Quadrant Model

© Ronald D. Damhof – May 19, 2015 – SAS Insight

It’s all about the data: A Managerial Perspective

By Ronald Damhof

Email: [email protected]

Linkedin:

nl.linkedin.com/in/ronalddam

hof/

Twitter: RonaldDamhof

Blog: prudenza.typepad.com

mailto:[email protected]

http://nl.linkedin.com/in/ronalddamhof/

http://prudenza.typepad.com

R.D.Damhof – Prudenza BV - Copyright - 22 mei 2014© Ronald D. Damhof – May 19, 2015 – SAS Insight

I am an opinionated kind a guy….


Who am I - My Data ManifestoThe X commandments of data management

I. Thou shall always respect

& consider the context.

Context is leading

Cynafin: D.Snowden (cognitive-edge)



II. Thou shall love your

(meta)data. Data is the

ultimate proprietary

asset:

- Manage it

- Govern it

- Utilise it

But do it ethically

“Most companies manage their

parking lot better than their data” —Gartner, Frank Buytendijk (paraphrased)



III.Thou shall stop centering

apps over data: data first



IV.Thou shall strive for

accurate, relevant, timely,

reliable and accessible data:

It is all about the quality of

the product

Deming’s point 3 of 14:

”Cease dependence on inspection to

achieve quality. Eliminate the need for

massive inspection by building quality

into the product in the first place."



V. Thou shall abstract



VI.Thou shall make a

‘fundamentalistic’ separation

between facts & context


VI. Thou shall not forsake ‘TIME’



VIII.Thou shall uphold, improve

and teach the science and

practice of Information- &

data modeling



IX.Thou shall 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…..’



A Framework for a Managerial

Perspective on Data


Logistics & Manufacturing



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

Mass deployment

Control > Agility

Validation of “ingredients”

Repeatable & predictable proces

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 & developer are separated

Defensive Governance

Focus on non-functionals

Centralised

Proper system development

User = developer

Offensive governance

Decentralised

“System development” 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

How 2 produce

How 2 automate

How 2 organize

How 2 govern

How about people

How about technology

How 2 model


(1) How we produce


How we produce, process variants


Production-line: Data orientation

Data Products Information

Products

Access to data

Analytical tools

Processing Power

Production-line: Forms orientation

Eg. XBRL/JSON

How we produce, production lines

Production-line: Poly Structured


(2) How we automate


Rephrased - somewhat more nerdy:

• Model-driven, metadata driven

Or

• Declarative instead of Imperative

Rephrased - somewhat more popular:

“In Data, the developer is the data modeller”

(2) How we automate


(3) How we organize


2nd law of Thermodynamics

Entropy of an isolated system

will always tend to stay the

same or increase – in other

words, the energy in the

universe is gradually moving

towards disorder.

In loose terms; a measure of the

amount of disorder within a

system


Entropy is ever increasing

Simple/

Order

ChaosComplex/

Un-order

Complicated/

”Order”


To centralize or to decentralize


(4) How we govern


Remember the entropy?

Simple/

Order

ChaosComplex/

Un-order

Complicated/

“Order”


How we govern, products


I II

III IV

Deliverant is

Accountable

Demandee is

Accountable

Data scientist/Analyst/Researcher accountable

How we govern, accountabilityNever, never, never ‘ownership’

In- en outbound

Data Delivery

Agreements

With great power comes great responsibility


(5) How do people excel

Data

Engineer

Application

Developer /

BI professional

Data

ScientistInfrastructure

Specialist


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

Infrastructure-as-service

Datalakes

Fileservers

…


(7) Business-,Information- or

Data Modeling is key

Conceptual

Logical

e.g

Fact based,

Graph,

Key-value

Models that fit

the needAt least the Logical Model

drives the technical data

architecture, design and

implementation

Ontology

Facts

Relational

Natural Language

ORM / FCO-IM


Please (!) have a holistic view &

strategy of data – it’s a supply

chain.

Data isn't ‘One size fits all’

If you have x Euro to invest, what

quadrant would you invest in?

If you want to be data-driven,

how would you organize

yourselves? What kind of competencies and skills would you need?

Think before you buy, think hard. Do not follow blindly the

latest tech fad, framework x, method y, best practice z.

Fight the entropy! It isn't free….


Thank

you

Date post:	20-Jul-2015
Category:	Data & Analytics
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Sas insight sessie data management - Data Quadrant Model

Data & Analytics