8. Competitive Advantage- Over Ages
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How Fast
Co-Create
How Fast
Innovate
How much
Actionable
Connectedness
How much Knowledge
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and Quickly
Extent of
can be
How much Informed
How much of can be In the Connected Age –
Agricultural produced Co-Creators will thrive
land and
Natural
Resources
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9. Have You Seen Any Regular
Geometrical Shapes in Nature?
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11. Nature
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is
Fractal!
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12. Black Swan – The impact of
the highly improbable
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• “I cannot accept a pretense of
science. I much prefer a
sophisticated craft, focused on
tricks, to a failed science looking
for certainties” Nassim Nicholas
Taleb
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13. Innovation is not about
Automation Alone!
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14. Artful Making
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15. Get it Right First Time!
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17. Failures/Mistakes/Errors
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Burning your hand is a small
price to pay for a good
idea.
PLEASE LET YOUR
EMPLOYEES FAIL!
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18. 4 Qualities of Artful Making
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Accept wide
variations • Interactions
within known among
members of
parameters
the groups
Release Play
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Collaboration Ensemble
• Conversation. • Work of a group
• Released from Vanity, dedicated to
inhibition, preconception collaboration
• Treat others inputs as • Create a whole
material to make with greater than
• So that New Ideas sum of its parts
emerge
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19. Emergence can not be pre-
conceived crafting innovation together
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Re-conceiving everything in response
to what team sees as newly possible
with each passing movement of
rehearsal, as a result of each new
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thing tried
20. Albert Einstein Says
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Sometimes one pays the
most for the things one
gets for nothing
The whole of science is nothing more than the
refinement of everyday THINKING
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21. Artful Making - Again
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22. Discovery Vs Making
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“Discovery” suggests there is a
right choice waiting to be found
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23. HOW TO THINK
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When Einstein was
asked what was most
helpful to him in
developing the theory
of relativity, he
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replied,
“Figuring out how to think
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about the problem.”
24. What are the Assumptions?
• Care about the Premises
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more than the Theory
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Oh My
God
Really!
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26. crafting innovation together
• The brain is a complex adaptive system.
• The brain is a social brain.
• The search for meaning is innate.
• The search for meaning occurs through patterning.
• Emotions are critical to patterning.
• Every brain simultaneously perceives and creates
parts and wholes.
• Learning involves both focused attention and
peripheral attention.
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• Learning always involves conscious and unconscious
processes.
• We have at least two ways of organizing memory.
• Learning is developmental.
• Complex learning is enhanced by challenge and
inhibited by threat.
• Every brain is uniquely organized
http://www.brainconnection.com/topics/?main=fa/brain-based3
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27. 3 Conditions for Learning
Low Threat and
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High Challenge
Relaxed Alertness
Orchestrated
Immersion Active Processing
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Experiences
processed as
In Multiple, the basis of
Complex, Meaning
Authentic
Experiences
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28. 3 Conditions for Learning
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• "Optimizing the use of the human brain means using the
brain’s infinite capacity to make connections–and
understanding what conditions maximize this process."
• Three interactive and mutually supportive elements that
should be present in order for complex learning to occur:
– An optimal state of mind that we call relaxed alertness,
consisting of low threat and high challenge.
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– The orchestrated immersion of the learner in multiple,
complex, authentic experience.
– The regular, active processing of experience as the basis for
making meaning.
http://www.brainconnection.com/topics/?main=fa/brain-
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29. • Chaotic Dumbness (High Threat, Low Challenge)v a t i o n
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– Comply with this or else…
– Constant use of “Killer Phrases” to destroy new ideas
• Automated Disjointedness (Delegation, Hands Off)
– Follow the process, don’t worry about anything
– Processes above people – Right processes Mandated will create
results
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– “Skill Set” based work assignment
• Passive Analysis (Data Collection, Historical Data Analysis)
– Give me all the data that has been accumulated – I will analyze
and give you Insights
– Quantify – if you cant quantify you don’t really know?
– We will do a Quarterly Analysis to provide you results
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30. Killer Phrases Are Not Used During Idea Generation*
1. "Yes, but. . . " 21. "The boss fwill g i n n ogoa for n t o g e t h e r
c r a t i n never v t i o it."
2. "We tried that before." 22. "It's too far ahead of the times."
3. "That's irrelevant." 23. . . . laughter. . .
4. "We haven't got the manpower." 24. . . . suppressed laughter. . .
5. "Obviously, you misread my request." 25. . . . condescending grin. . .
6. "Don't rock the boat!" 26. . . . dirty looks. . .
7. "The boss (or competition) will eat you alive." 27. "Don't fight city hall!"
8. "Don't waste time thinking." 28. "I'm the one who gets paid to think."
9. "Great idea, but not for us." 29. "What will people say?"
10. "It'll never fly." 30. "Get a committee to look into that."
11. "Don't be ridiculous." 31. "If it ain't broke, don't fix it."
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12. "People don't want change." 32. "You have got to be kidding."
13. "It's not in the budget." 33. "No!"
14. "Put it in writing." 34. "We've always done it this way."
15. "It will be more trouble than it's worth." 35. "It's all right in theory. . . but. . ."
16. "It isn't your responsibility." 36. "Be practical!"
17. "That's not in your job description." 37. "Do you realize the paperwork it will create?"
18. "You can't teach an old dog new tricks." 38. "Because I said so." * Reference: “What A Great
39. "I'll get back to you." Idea” by Charles “Chick”
19. "Let's stick with what works."C Thompson, 1992,
20. "We've done all right so far." 40. . . . silence. . . HarperCollins Publishers
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31. crafting innovation together
From Chaotic Dumbness to Relaxed Alertness
From Automated Disjointedness to Orchestrated Immersion
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From Passive Analysis to Active Processing
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34. LIST – INVENTIVE THINKING
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HOW TO INVENT, UNDERSTAND and
SOLVE PROBLEMS – How to Innovate
TRIZ - Theory of
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Inventive Problem Solving
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35. A Brief History of TRIZ
TRIZ – Inventive Problem Solving by Altshuller
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• 1946 Patent Officer
in Russian Navy Teoriya Resheniya Izobreatatelskikh Zadatch
• Discovered patterns
in patents,
World’s best ideas
published paper. A situation like mine World
in this situation
Sent to Gulag (Abstraction) Knowledge
Access (Access)
• 1954 released, Base
analysed 2,500,000 Abstraction Specific
patents
My specific situation My specific solution
• Identified what Evaluate
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Me / my company
makes a successful
patent
• 1956-1985 TRIZ
formulated
• Same Problems and Solutions appear again and again but in different industries
• There are a series of recognizable Technological Evolution paths for all industries
• Innovative solutions used theories outside their own area/industry
• The most powerful solutions uncover and eliminate contradictions
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36. Problem Solving
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* Problem solving is like digging
for treasure in a field
* If a hole already exists, our
inclination is to dig it deeper
* The deeper the hole, the more
difficult it is to see what’s happening
in other parts of the field
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* If someone else comes along,
we encourage them to jump in
the hole with us
* The overall effect is called
PSYCHOLOGICAL INERTIA
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37. Questions that TRIZ asks you…
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1. What is my Ideal Final Result – How can
I achieve the functionality without A
spending any resources or cost B
2. How the problem/situation/system looks
in time and space coordinates C
3. Am I using all the existing resources or
potential resources to the fullest
4. What is the main useful function I need
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Plain Forest Coal
to deliver. What are various ways in
which I can deliver the function
5. How others have solved the same Tree
Seed Timber
problem in the past
DNA Fruit Pie
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38. Innovation Crafting
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Idea
Generation
Situation
Assessment TRIZ Six Hats
Idea Clustering –
Solutions
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Generation
DSM AHP
Solution
Implementation
Solution
Evaluation
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39. Convergence in Design…How Toyota does it…
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How to converge from an initial set of conceptual ideas to one idea
that will become the final Design?
Early Convergence Strategy Toyota’s Slow Convergence
- Point-Based CE – Set-Based CE
Large Design Spaces –
Design Critical Integration of Sets
Chose
Space n Analysis
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Design
Modificati “ELIMINATE
on “DESIGN
WEAKEST
CHURNING”
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ALTERNATIVES”
40. crafting innovation together
LEAN INVENTIVE SYSTEMS THINKING
Case Studies
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41. Creating a Common
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Operating Picture
using Dependency
Structure Matrix
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(DSM)
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42. Dependency Analysis – Design Structure Matrix (DSM)
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• DSM – a methodology to sequence information flows
DSM is a square binary matrix whose rows and columns represent the individual
activities/tasks and element values represent the relationships between these activities.
Relationship Parallel Series Coupled (Loop)
A A
Graphic
A B
Representation B
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B
(CPM) (CPM)
(not allowed in CPM)
A B A B A B
DSM
A A A X
Representation
B B X B X
(indicates iteration)
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44. Cardiac Rhythm Management
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Components Suppliers US Non US
Leads 545 68 78% (53) 22% (15)
PG ~2500 194 87% (169) 13% (25)
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45. Enterprise Wide Initiative – To Strengthen,
Streamline and make Processes more Robust
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46. crafting innovation together
Dependency Structure Workshop
Key Participants
• Design Engineer
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• Supplier Engineer
• Production Engineer
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47. Understanding Process Complexity
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Dependency Structure Matrix
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48. Understand Process Complexity
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Rework
Iterations
Multiple Handoffs
Ambiguities
Uncertainty
-possible chaos
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Identifying Complexity Blocks
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49. Organizational Complexity
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High Interactions between various departments
– High Organizational Complexity
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50. crafting innovation together
10
15
20
25
0
5
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WindChill
WinSPC
MATT
CATSWEB -
Applications
TWO (Tool
ATHENA
SMART
Product/Desig
Supplier
Manufacturing
Reliability
Regulatory
Medical
Actors
Quality
Test
Suppliers
Test
Process Dependencies
Compliance
Documentation
Tool
Operators
Project
R&D
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Operations
Regulatory
Supplier
Departments
Model Shop
Process Dependencies on Applications, Actors and Departments
51. crafting innovation together
SYSTEM COMPLEXITY ESTIMATOR
Applications in Software Architecture,
Design & Project Planning
3rd International Conference on Quality Reliability
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and Infocom Technologies, Delhi, 2-4 December
2006
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52. Software Design Guideline –
Coupling & Cohesion crafting innovation together
• A software system is the decomposition of conceptual construct into
conceptually independent modules that cooperate to achieve a desired
result.
• Coupling refers to the degree of interdependence among the components of
a software system. Good software system should obey the principle of Low
coupling. Strong coupling makes the system more Complex.
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• The cohesion of a module is defined as a quality attribute that seeks to
measure the singleness of purpose of a module. Cohesion seeks to maximize
the connections within a module.
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53. System Complexity Estimator
Ideal System with Minimumt iComplexityt o g e t h e r
craf ng innovation
A system with completely independent elements (modules)
where each module performs a single function is the least
complex– this is the Ideal Architecture for a system. If each of
the activity performs a single function only and it does not
depend on the other modules for its functioning except on itself
then the complexity is minimum which is proportional to
number of elements in the system.
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Cohesion
Dependency Measures (Coupling)
A Measure of how much cohesive the
Module Dependency on the System Index (MDSI) module is on its own – If it has less
System Dependency on the Module Index (SDMI) cohesion it leads to more complexity
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54. System Complexity Estimator – Dependency Measures
Dependency Measures (Coupling) t i n g
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Module Dependency on the System Index (MDSI)
System Dependency on the Module Index (SDMI)
Simple Summation of row elements of a DSM can give a measure of
MDSI; Simple summation of column elements of a DSM can give a
measure of SDMI
A B C D sum MDSI
A 1 1 1 3 0.33
Indirect Dependencies are not
B 1 1 2 0.22 taken care of in this measure
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A depends on B and B depends on
C 1 1 2 0.22 D hence A depends on D
D 1 1 2 0.22
sum 2 2 3 2 9 MDSI
0.33
SDMI 0.22 0.22 0.33 0.22
0.25
0.21
To take care of indirect dependencies 0.21
Normalized eigen vector
SDMI 0.21 0.21 0.33 0.25
corresponding to Principal eigen
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55. System Complexity Estimator (SCE)
• Enables the Designer/Project Managerc rtot iIdentifyi okeyg eareas in the
af ng innovat n to ther
system being designed that contribute more to the complexity
• Fundamental Shift in the way software systems are designed and
software projects are planned.
Input
• Coupling between modules
• A measure of lack of cohesion in
each module
Output
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• Estimates total complexity of the
system
• Estimates how much each module
contributes to the complexity
Design Fundamental: Maximize
Cohesion Minimize Coupling
“Robust Inventive Software Design” presented
at the 7th International DSM Conference in Seattle, Wa, Oct 4-6, 2005
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56. Application of System Complexity Estimator
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Team became fully aware of inter-
• Identify dependencies through group brain storming dependencies and hand-offs
• Determine complexities using System Complexity Estimator
Understood meaningful delivery blocks
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58. Software System Evolves!
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Functionality of Software System (Requirements)
Requirements constructed into structure
Deployment and Usage creates new requirements (new
functionality)
Software system changes structure through
– Addition of new structural elements (e.g. new functionality)
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– Changes (e.g. bug-fixing) in the existing system
Evolutionary nature of software systems keeps on increasing
system complexity
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59. Software System Evolution – Complexity Increases
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The
Final
Softwa
Comple
re
xity
System
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New –
Functionality Highly
Major Design – Redesign Compl
Change and New ex
Initial Software patch
Design
Time
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60. TRIZ - IDEALITY
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• IDEALITY = Sum of All Useful Functions/ Sum of All
Harmful Functions
Focus on Function/Functionality
Proposition: Design is about structure – The ideal
system structure should be included in the definition
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of Ideality.
Achieved by Ideal Structure
Ideal Structure = Least Complex Structure
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61. TRIZ – IDEALITY for Software Systems
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Achieved by the Least Complex Software Structure
How to define the Least Complex Software
System?
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A note: Complexity is not the function delivered by a
system but it is the attribute of the system
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62. Software Complexity Measures (Existing Measures…)
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Modularization Criteria – Coupling & Cohesion
McCabe Complexity and Size of the Software
Software Entropy (Bhushan N and Kaushik K, 2002)
SEI’s Maintainability Index (www.sei.cmu.edu)
Limited Work on Structural Complexity of the system
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63. System Complexity Estimator (SCE)
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• Software Complexity Measures and approach for managing complexity
• System Complexity – Concepts of Coupling and Cohesion
• Social Network Analysis – A Framework for Software Design
• System Complexity Estimator (SCE) – Quantitative measure of complexity
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Ref: Bhushan N,, SYSTEM COMPLEXITY ESTIMATOR - Applications in Software
Architecture, Design & Project Planning, 3rd International Conference on Quality Reliability and
Infocom Technologies, India, 2-4 December 2006
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64. Software Design Guideline – Coupling &
Cohesion
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• A software system is the decomposition
of conceptual construct into
M M
conceptually independent modules that
cooperate to achieve a desired result. 1 2
M
3
• Coupling: the degree of interdependence
among the components of a software system.
M
Good software system should obey the 4
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principle of Low coupling.
• Cohesion of a module is defined as a quality
attribute that seeks to measure the singleness M4
of purpose of a module. Cohesion seeks to m41 m42
maximize the connections within a module. Cohesi m43 m44
on m45
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65. Problem:
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• How to measure Coupling and Cohesion of Software Systems as a single
metric?
• A metric from Social Network Analysis…
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Solution:
A Software System is like a Social Network
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66. Social Networks Analysisr a f t i n g i n n o v a t i o n t o g e t h e r
c
• A primary use of graph theory in social network analysis is to identify the
“important” actors. Centrality and prestige concepts seek to quantify graph
theoretic ideas about an individual actor’s prominence within a network by
summarizing structural relations among the g nodes.
• An actor’s prominence reflects its greater visibility to the other network
actors (an audience). A prominent location takes account of sociometric
choices made and choices received, as well as indirect ties. The two
prominence classes discussed in this literature are
– Centrality: Actor has high involvement in many relations, regardless of
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send/receive directionality (volume of activity)
– Prestige: Actor is recipient of many directed ties, but initiates few
relations (popularity > extensivity)
Bonacich, P.B., Power and Centrality: A Family of Measures, American
Journal of Sociology 92, 1170-1182, 1987
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67. Eigenvector-like Measures of Centrality for
Asymmetric Relations
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• The eigenvector captures a certain
aspect of centrality or status that is
not captured by other measures.
The eigenvector is an appropriate
measure when one believes that
actors' status is determined by
those with whom they are in
contact. This conception of
importance or centrality makes
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sense in a variety of circumstances.
Social status rubs off on one's
associates. Receiving information
from knowledgeable sources adds
more to one's own knowledge.
A Software System is like a Social Network
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68. Existing Design (Capturing Couplinga fandi nCohesion tinputs)
cr ting novation toge her
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69. Existing Design – System Complexity Map
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Existing Design IDEAL SYSTEM –
Scramble COMPLEXITY =
Create New Rule 16.00 Name Shuffle
Preview Masking 12.00 Address Shuffle
Number of Functions
Profile Management 8.00 Generic Shuffle Delivered = 54 with
Installer
4.00
Blank Out
Cohesion closer to
0.00 1.0 COHESION
LOW
Scheduler Replacement
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Masking SSN Generator
Preload Script Luhn Generator
Pattern Generator Sequence Number Generator
Random Number Generator
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70. TEAM BRAINSTORMED TO COME UP WITH THREE ALTERNATIVE DESIGNS
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– with a Trigger to increase module Cohesion
Design Option1 Design Option 2
Scramble
Custom Rule Manager 20.00 Name Shuffle Scramble
18.00 Execute Preload Script 20.00 Name Shuffle
DB Masker Address Shuffle
16.00 Authenticate Db Credentials 18.00 Address Shuffle
14.00 16.00
Profile Manager Generic Shuffle
Log file generation 14.00 Generic Shuffle
12.00
12.00
10.00
Preview Ethnicity Shuffle 10.00
8.00 Update Transaction Table Ethnicity Shuffle
8.00
6.00 6.00
4.00 4.00
Scheduler Selective Name Shuffle Load Staging Table Selective Name Shuffle
2.00 2.00
0.00 0.00
uthenticator (Authenticate,Pre-Load &
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Look Up Names Shuffle Common_Functions Look Up Names Shuffle
compile)
Pattern Generator Look Up Address Shuffle
Pattern Generator Look Up Address Shuffle
Random Number Generator Data Encryption/Decryption
Random Number Generator Data Encryption/Decryption
Sequence Number Generator Blank Out
Sequence Number Generator Blank Out
Luhn Generator Replacement
Luhn Generator Replacement SSN Generator
SSN Generator
Note: The Psychological Inertia of the existing design continued –
structurally similar to existing design
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71. TEAM BRAINSTORMED TO COME UP WITH THREE ALTERNATIVE DESIGNS
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(Contd…)
Design Option 3
Scramble
12.00 Name ShuffleShuffle
Address
Generic Shuffle
Modify Rule 10.00 Ethnicity Shuffle
Create New Rule Selective Name Shuffle
Monitor Mask Status 8.00 Look Up Names Shuffle
Load Profile 6.00 Look Up Address Shuffle
Start Masking 4.00 Data Encryption/Decryption
Preview Masking Blank Out
2.00
Save Profile Replacement
0.00
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Generate Mask Information SSN Generator
Search Mask Fields / Master Tables / Limit
Luhn Generator
Tables
Get All Mask Field List Sequence Number Generator
New Profile Random Number Generator
Restart Masking Pattern Generator
Preview Profile Common_Functions
Re-Schedule Masking Load Staging Table
Stop Scheduled Masking Update Transaction Table
Schedule Masking
Get Restart / Scheduled Job Status Log file generation
Authenticate Db Credentials
Execute Preload Script
Note: A shift in the thinking achieved through a focus on
maximizing cohesion
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72. System Complexity Analysis of Alternatives with
the existing Design Existing Design
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Scramble
Create New Rule 16.00 Name Shuffle
Preview Masking 12.00 Address Shuffle
Profile Management 8.00 Generic Shuffle
4.00
Installer Blank Out
0.00
Scheduler Replacement
# of Size (# of Avg Functions/ Masking
Preload Script
SSN Generator
Luhn Generator
Pattern Generator Sequence Number Generator
Design functions Modules) Module Complexity Random Number Generator
Existing Design Option1
Scramble
Custom Rule Manager 20.00 Name Shuffle
Design 54 18 3 88.7
18.00
DB Masker Address Shuffle
16.00
Profile Manager 14.00 Generic Shuffle
12.00
10.00
Preview Ethnicity Shuffle
8.00
6.00
Design Scheduler
DB Authenticator (Authenticate,Pre-Load &
compile)
4.00
2.00
0.00
Selective Name Shuffle
Look Up Names Shuffle
Option 1 66 22 3 102.3 Pattern Generator
Random Number Generator
Look Up Address Shuffle
Data Encryption/Decryption
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Sequence Number Generator Blank Out
Design Luhn Generator
SSN Generator
Design Option 2
Replacement
Option 2 51 39 1.3 174 Authenticate Db Credentials
Log file generation
Scramble
Execute Preload Script 20.00
18.00
16.00
14.00
Name Shuffle
Address Shuffle
Generic Shuffle
12.00
Design
10.00
Update Transaction Table Ethnicity Shuffle
8.00
6.00
4.00
Load Staging Table Selective Name Shuffle
2.00
0.00
Option 3 51 42 1.2 154.6
Common_Functions Look Up Names Shuffle
Pattern Generator Look Up Address Shuffle
Random Number Generator Data Encryption/Decryption
Sequence Number Generator Blank Out
Luhn Generator Replacement
SSN Generator
Design Option 3
Scramble
12.00 Name ShuffleShuffle
Address
Generic Shuffle
Modify Rule 10.00 Ethnicity Shuffle
Create New Rule Selective Name Shuffle
Monitor Mask Status 8.00 Look Up Names Shuffle
Load Profile 6.00 Look Up Address Shuffle
Start Masking 4.00 Data Encryption/Decryption
Preview Masking Blank Out
2.00
Save Profile Replacement
0.00
Generate Mask Information SSN Generator
Note: Cohesion Reduced but Complexity Increased Search Mask Fields / Master Tables / Limit
Tables
Get All Mask Field List
New Profile
Restart Masking
Luhn Generator
Sequence Number Generator
Random Number Generator
Pattern Generator
Preview Profile Common_Functions
due to increased coupling
Re-Schedule Masking Load Staging Table
Stop Scheduled Masking Update Transaction Table
Get Restart / Schedule Masking
Scheduled Job Status Log file generation
Authenticate Db Credentials
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73. Team Once Again Brainstormed … to Balance
Cohesion and Coupling
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• The team brainstormed further to look at ways and means of
reducing the coupling. The team hit upon the idea of a Router – A
New Module, which was suggested earlier during the discussions but
somehow was not pursued
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Final Evolved Design
Blank Out
Modify Rule
8.00 Replacement
Create New Rule Scramble
Monitor Mask Status Generic Shuffle
Load Profile 6.00 SSN Generator
Start Masking Luhn Generator
Preview Masking 4.00 Sequence Number Generator
Save Profile Random Number Generator
2.00
Generate Mask Information Pattern Generator
Search Mask Fields / Master Tables / Limit
0.00 Single Field Router
Tables
Get All Mask Field List Ethnicity Shuffle
New Profile Data Encryption/Decryption
Restart Masking Name Shuffle
Preview Profile Address Shuffle
Re-Schedule Masking Selective Name Shuffle
Stop Scheduled Masking Look Up Names Shuffle
Schedule Masking Look Up Address Shuffle
Get Restart / Scheduled Job Status Authenticate Db Credentials
Execute Preload Script
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