Managing Quality Within an EA Repository

Managing Quality Within an EA
Repository
Version 01.00
Johan Schoeman

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Real IRM Solutions (Pty) Ltd
2008
Introduction
“Being good in business is the most
fascinating type of art”
Andy Warhol, 1975
Philosophy of Andy Warhol (From A to B and Back Again)
RI Managing the Quality of an EA Repository v 01.0006/06/2008

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Introduction
“Architecture is the art of how to waste
space”
Philip Johnson, 1964
New York Times, 27 December 1964

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Agenda
 Case Study background
 Challenges of repository quality management
 Quality management process
 Quality management reporting
 Conclusion

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Case Study Background
 Anglo Platinum is the world’s
largest primary producer of
platinum.
 Anglo Platinum started with
their Enterprise Architecture in
2005 and has since invested a
significant amount in building up
content within the architecture
repository (ARIS), with
particular focus in the Business
Domain.

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Case Study Background (Cont.)
 Real IRM provides outsource
EA services, including
repository management and
quality assurance.
 Support to a number of
internal users, external
consultants distributed across
the Anglo Platinum operations.

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Agenda
 Ongoing challenges

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 Controlling the content of the repository, becomes increasingly difficult
because of the amount of ARIS users and information in the repository.
 Models and Objects numbers are growing significantly over time, causing complexity
in the amount of information to manage
 Users are subject to turnover and get reassigned to working within new areas without
the knowledge of other users and administrators. When queries on content arise,
people struggle to find the person that is responsible for a particular area.
Challenges for Repository Quality (Cont.)
Models 18207
Objects 99461
Users 80

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Challenges for Repository Quality (Cont.)
 The variety of modeller backgrounds result in a variety of methodologies,
training and standards.
Employee
Required To
Attend To C...
Employee
Required To
Work Overtime
L4
Complete Pre-
Authorised
Overtime
Documentation
L4
Forward Pre-
Authorised
Overtime
Documentati...
L4
Determine If
Pre-Authorised
Overtime
Documentati...
L4 L3
Complete Pre-
Authorised Overtime
Documentation
L3
Forward Pre-
Authorised Overtime
Documentation For
Capturing
L3
Determine If Pre-
Authorised Overtime
Documentation
Complete
L3

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Challenges for Repository Quality
 EA modelling tools provide limited control of input
SHE...
Rustenburg
Region
R
General...
D
Analytical
Occupatio...
Mine, Health
& Safety A...
Occupational
Hygiene...
Area Noise
Survey Report
SHE.14.03
L3
Analyse Hygiene
Measurement
And Monitoring
Samples
Area
Illuminati...
Analyse
Hygiene...
Versi...
SIMRAC
Guideline
Mine
Manager...
A
Occupation
al Hygie...
E
Version 1.01
Mine Manager:
Lower Mine...
A
Manager
Engine...
A
Occupational
Hygiene...
Occupational
Hygiene Iss...
Occupational
Hygiene Ri...
Occupational
Hygiene...

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Agenda

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QM Process
 In order to publish a model to the
production repository, the modeller
has to go through three stages:
 Completing the EA model
 Completing the Logical Quality
Assurance of the model
 Completing the Technical Quality
Assurance of the model
Complete EA
Model
Request Logical
QA of EA Model
Perform Logical
QA of EA Model
Logical QA
Passed
Logical QA Not
Passed
Yes No
Request
Technicall QA of
EA Model
Perform
Technical QA of
EA Model
Technical QA
Not Passed
Technical QA
Passed
NoYes
Publish Model in
Production
Repository
Correct
Technical QA
Errors
Simplified QA Process

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QM Process: Completing the EA Model Content
Complete EA
Model
Request Logical
QA of EA Model
Perform Logical
QA of EA Model
Logical QA
Passed
Logical QA Not
Passed
Yes No
Request
Technicall QA of
EA Model
Perform
Technical QA of
EA Model
Technical QA
Not Passed
Technical QA
Passed
NoYes
Publish Model in
Production
Repository
Correct
Technical QA
Errors
 The modellers have various tools
available that help and guide them
in producing quality content:
 EA Meta-model
 EA Standards and Guidelines
 EA Training
 Capturing Interfaces
 Assigning responsible modellers

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 EA Meta-model

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 EA Standards and Guidelines

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 EA Training
 To be completed by every modeller before they can work within the repository
 Training not only teaches how to use the tool, but how to do modelling within Anglo
Platinum’s context, i.e. Meta-model, standards, guidelines, etc.
 Training course includes a case study and evaluation, which needs to be passed
before repository access is allowed

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 Capturing Interfaces
 Modellers use a form-based
interface with which to capture
model content, which ensures that
the right type of information is
captured at the appropriate levels.

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 Assigning responsible modellers
 ARIS users are assigned as
responsible modellers for the models
within the repository
 This helps other users and
administrators keep track of the people
responsible for models, when queries
arise.
 This gets logged within a read only
attribute in ARIS to ensure that it does
not get changed erroneously

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QM Process: Completing the Logical QA
Complete EA
Model
Request Logical
QA of EA Model
Perform Logical
QA of EA Model
Logical QA
Passed
Logical QA Not
Passed
Yes No
Request
Technicall QA of
EA Model
Perform
Technical QA of
EA Model
Technical QA
Not Passed
Technical QA
Passed
NoYes
Publish Model in
Production
Repository
Correct
Technical QA
Errors
 The Logical QA process ensures
that the model makes logical sense
and follows the logical guidelines. It
is essential for maintaining the
quality of the EA content.
 Needs to be performed by a person
 The Logical QA process consists out
of:
 Requesting Logical QA
 Performing Logical QA
 Providing feedback on the Logical QA

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 Modellers trigger a request for
Logical QA within ARIS

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 This request logged into a read only
attribute within ARIS

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 Performing Logical QA
 This request logged into a read only
attribute within ARIS
 This request is logged into an
external database that tracks the
QA history
QA DB

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 Performing the Logical QA
 The Quality Regulator evaluates the
model against a set of specified
criteria.
 The result of the logical QA is
logged within a read only attribute in
ARIS.
 The result is also logged into an
external database that tracks the
QA history.
QA DB

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 Performing the Logical QA
 Feedback is provided to the
responsible modeller, typicaly in
the form of a scanned model with
written comments.
 This process continues until the
model passes the Logical QA

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QM Process: Completing the Technical QA
Complete EA
Model
Request Logical
QA of EA Model
Perform Logical
QA of EA Model
Logical QA
Passed
Logical QA Not
Passed
Yes No
Request
Technicall QA of
EA Model
Perform
Technical QA of
EA Model
Technical QA
Not Passed
Technical QA
Passed
NoYes
Publish Model in
Production
Repository
Correct
Technical QA
Errors
 The Technical QA process ensures
that the model follows meta-model
rules and guidelines. This QA is
essential for repository integrity.
 Is automated
 The Technical QA process consists
out of:
 Requesting Technical QA
 Automatically performing Technical QA
 Providing feedback on the Technical
QA

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 Performing the Technical QA
 The content of the ARIS repository
is exported to an external database.
 Predefined SQL queries that check
for technical errors are run
ARIS DB
Export
DB

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 Providing Feedback on the Technical QA
 The result from the Technical QA is logged within ARIS and the QA DB.
 Spreadsheets that highlight the technical errors are sent back to the applicable
modeller to correct.

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QM Process: QA Completed
Complete EA
Model
Request Logical
QA of EA Model
Perform Logical
QA of EA Model
Logical QA
Passed
Logical QA Not
Passed
Yes No
Request
Technicall QA of
EA Model
Perform
Technical QA of
EA Model
Technical QA
Not Passed
Technical QA
Passed
NoYes
Publish Model in
Production
Repository
Correct
Technical QA
Errors


ARIS
Development DB
ARIS Production
DB

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Quality Management Reporting
QA DB
ARIS DB
Historical QA Data
Point-in-Time Data
REPORT A
REPORT B
REPORT C
REPORT D
Export
DB

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 QA Status
 Helps with tracking the Quality Assurance process over time
Row Labels <1 Month 1 Month
4-6
Months
6-12
Months
> 1
Year
Grand
Total
Logical Fail 1 5 144 112 262
Logical Pass, Technical Fail 48 62 3 98 3 214
QA Backlog 20 20
Logical QA in Progress 1 1
Logical QA Requested 11 11
Passed 23 136 159
Technical QA Requested 14 14
Grand Total 63 78 26 379 135 681

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 QA Status
 Provides QA statistics per modeller
Responsible Ave Logical Cycles
user1 1
user2 6
user3 2
user4 2
user5 1
Row Labels user1 user2 user3 user4 user5
Logical Fail 21 8 102 5
Logical Pass, Technical Fail 6 88 3 25
QA Backlog 14
Logical QA in Progress 1
Logical QA Requested
Passed 99 1
Technical QA Requested
Grand Total 27 210 105 30 1

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 Modelling Progress
 Provides management with a point in time view of the status of the EA modelling
projects.

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Conclusion
 Challenges are not individual to Anglo Platinum or ARIS alone, but are
very similar at a number of organisations and EA tools.
 Using similar processes across clients with different toolsets
 Quality Management is an ongoing and ever-increasing challenge and the
process and reporting has to mature over time

Managing Quality Within an EA Repository

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