Systems thinking, corporates fit, skills development, complexity management, org fit, system "functional" network processing

1. CBIR Consulting
Anil Sharma
sharma_anil@yahoo.com
https://www.linkedin.com/in/anil-sharma-592754/

2. Contents
1. Systems & architecture thinking...................................................................................................4
2. Complexity handling: Job complexity, careerevents & mind changes.............................................6
3. Detailed “functional network” processing.....................................................................................9
1. Inter-personal communication: ...............................................................................................10
2. Information analysis: .............................................................................................................10
a) Reading between the lines:.....................................................................................................10
b) “A picture is worth a thousand words.”: ..................................................................................11
c) PIP or picture in picture feature:.............................................................................................11
d) “Frame of reference” context is important: ..............................................................................11
4. Skills development focus............................................................................................................12
5. Corporate fit testing..................................................................................................................14
6. Organization fit.........................................................................................................................16
7. Personal development tracks.....................................................................................................19

3. Personality aspect of a software architect
“Y” in front of “ours” is “Yours”. Anonymous
There are multiple career tracks available for individuals interested in software and computing
systems based industry and businesses (our main focus). Just to name a few at very high level:
 Technology development
 Technology management
 Engineering management
 Project/Program management
 Marketing & sales
 Customer and other support services etc
Some of these career tracks would need people with more hands on experience while others
would need people who are either very good in subject matter understanding or are better at things like
personal interaction, inter-personal communication, use of management procedures and tools etc. Import
aspects for some careers are macro-level personal development and keeping global views such as for
architecture and management work streams, while for some others micro view and deep focus on nitty-
gritty along with desire for deep dive into minutia and details would be more import. This is especially
true for things like hands-on technology development and question and answer type customer & sales
support type of roles.
Work relationships also come in many flavors these days. Some important ones are “permanent
employment”, consulting relationship through brand name consulting companies, boutique consulting
relationships or even personal consulting practices along other very popular arrangements such as
outsourcing, specialty tasks partnerships and channels. Based on business requirements, some functions
would become essentially internal only and some functions could easily become external outsource-able.
Since we would mainly focus on software architect role in this write-up, let’s focus on
identification of core set of attributes which would set apart a good architect from a bad one very
analytically to start with. At very high level, we can identify most important personality attributes most
needed of a successful architect kind of person and then we would venture into the detailed analysis of
some core development which happen in a person through his academics and career development which
would make him a good fit for such role.
To begin with some of the personality attributes one can easily identify for a role that would need
macro and global view, detailed micro-processing as well as high level focus needed almost at the same
time, long-term career track enabling development of technology understanding along with good
exposure to management tools and standard procedures, complexity management, knowledge gathering,
working with others, product life cycles, business requirements understanding and corporate level
overview on software, systems & solution prepositions. Let’s go in details of some of these items step by
step and understand key sub-requirements and needed results out of these expectations.

4. 1. Systems & architecture thinking
System and architecture work is not single dimensional work as are many other jobs are such as
coding, scripting, taking sales orders and voice support on phone calls.
You have to be a multi-dimensional person at the very least for good deliveries at work place if you
want to be a good software architect. For example, let’s look at a telecom standards and protocol design
architect’s role. Someone who would not only understand and verify almost legally qualify-able message
exchanges through multi-node systems but also would design systems and its components in such as way
that system is “finite” even for 30 to 40 to 50 stages of laddered processing (something that even a normal
computers can’t achieve independently over days of processing, needs processing of almost 1000 graphs).
Reading thru Text (1 Dimension work) skill wouldn’t be sufficient for development of even basic
understanding of requirements of this type of work that has structured, defined and pre-established work-
flows. Skill that enables the ability for understanding “Text with Picture & Data (2 Dimensions)” would
be minimum requirement for even proper reading and imbibing the standards and protocols specification.
For engineering design work around it though, one would essentially need skill for working with “Text
with two dimensional pictures & data (3 Dimensions)” as you would have to break the specification into
finite sub-parts and would have to get to 1 more level down before properly solving it and define needed
data structures and algorithms for working through further stages of processing. And you would have to
do this in ways that the whole system still converges and works almost perfectly with 100% accuracy for
infinite life. Some of the careers where workings in 3 dimensions like the one mentioned above is norm
are space research, electronic circuit improvement, data science or serial processing software or for that
matter econometrics would need this type of visualizing skill for heavy lifting phase of work deliveries..
One example of architecture thinking is “Continuous operations improvement” in development and
software delivery process operations. This would entail continuous data analysis and feedback control
systems’ understanding for making operations better. This is sort of data science approach to solving
problems or Devops improvements on Devops’ed processes. You could define it as Devops Squared. Any
name out of following list of names would fit the bill for further automation on already optimized tasks:
{ DevOps 2
}, Pipeline of DevOps projects, DevOps on DevOps, Data Science
Process improvement or engineering processes is a skill that you develop during you education
career at college. For example, during a four year undergrad program new skills or tools a student gets
introduced to 1 year at a time are:
Eg. Engineering processing – for engineering career
1. Engineering tasks introduction (Welding, Painting, Chisel, Filing, Cutting & Humanities etc) –
Division of engineering tasks –Job creation, steps identification (both hands together same plane)
– Mental fit testing
2. Multi-plane hands movement coordination (drawings of various types: engineering, mechanical,
electrical, electronics and computer science (flow charts))- both hand taking decisions at same
time i.e. scale and pencil – Career fit testing

5. 3. 1,2,3 -> Linear structured simple processing for normal Math Civil, Mechanical and electrical
engineering projects – Technology fit testing
4. Brain tuning for different types of processing (multi) for information, historical knowledge and
data at same time: linear, structured, decimal & binary both (Eg. Start of computer science,
electronics, communication engineering)
Bits processing in electronics and computer science:
0 or 20
= 1 value
1 or 21
= 2 values
3 or 23
= 8 values
4 or 24
= 16 values
6 or 26
= 64 values
8 or 28
= 256 values
These are very general purpose tools if you analysis and focus on fundamental requirements of
what you are suppose to learn out of these new learnings. They would mostly apply to any field of work
where such improvement and efficiencies would make sense. For example, there is a lot of focus these
days on Artificial Intelligence technologies and innovation drive that is on in this new area of computer
science development. However, unless you further break it down in its fundamental components and
make it “applicable” to task at hand, you wouldn’t get to any benefits out of new research work in this
area of computer science. For example, to make it further applicable for corporate business use, you
would have to sub-divide it and then would have to apply components of it to improvement of routine
corporate tasks. Whether it is using data science part of it or using devops part of it. Here is an example
derivation and then further subdivision:
Stage 0: Artificial intelligence
Stage 1: Applied intelligence
Substage 1.1: Business intelligence
Substage 1.2: Operations intelligence or DevOps
Substage 1.3: Data intelligence i.e. Data Science
Substage 1.4: Innovation intelligence (better computers or better networks to better
computing power and better and secure access) i.e. things like CI/CD or Orchestration
Automation
Substage 1.5: Sales and marketing intelligence, Analytics planning and reporting
With better automation capabilities and we have now become more and more dependent on
machines. These days it is easy to get to up to 80% level on automated tasks coverage if not 100%. The
80% level is better than no machine use when it has become so cheap to use them. As we know nothing is
fundamentally perfect. Perfection itself is not perfect i.e. it is not singularity in its entirety. In some cases,
you can never make better enemy of good This is more true in growing fields and there is sometime no
choice but to work with good enough and improve things little by little one step at a time until you
somehow know what is the best possible scenario and have some known way of getting to it.

6. Machines weren’t fast enough and good enough in past and lots and lots of people worked on
making machines even more faster, better looking and more cleaner until there was this “good-enough” fit
for 90%.of the users. Cars weren’t fast enough and good enough and lot and lots of people worked on
making cars even more faster and finer looking until there was this “good-enough” fit car that satisfied
the need for 90%.of the folks. Flying machines weren’t fast enough and good enough and lots and lots of
people worked on making flying machines even more faster and more reliable until there was this “good-
enough” fit plane that was suitable flight experience for 90% of the folks.
Similarly computers and networks weren’t fast enough and good enough and lot and lots of
people worked on making computers and networks even more faster, better designed until this
“goodenough” fit perfect connected computer for the only bought comport for 90% of the folks and same
things are now happening to cellular phones. Similarly businesses weren’t transparent enough and
dependable enough and lot and lots and lots of people worked on making business results and outcomes
more transparent and visible to all stakeholders and dependable for owners up to the levels of 90%.
Technological singularity has now new frontiers. There are many new areas of focus. Just to name the
few: human brain, robots & automation, applied intelligence.
Systems and architecture thinking itself has grown to new levels now and it is now good tool for
even some of these newer technologies at their very inception: certainly a better start over some of the
older innovations stories we know about falter at the very beginning because of lack of better tools and
technological understandings. Our knowledge around software and electronics use for problem solving
has improved up to the level of almost human intelligence since.There are also many types of software
systems. Some examples are as follows:
1. Software applications: Left brain or parallel system
2. Communication software over multiple nodes: Right brain or n-stage serial software
3. Data Management, Analytics & Science: Front brain, huge information memory and timeline nodal
point software
4. Social, platform and business solution context in system architecture: Back brain for adjacency
sensitivities and valuation system for software
2. Complexity handling: Job complexity, career events & mind changes
Software programming it-self is a complex function for human brain. On top of it, software systems
with lots and lots of intelligence in them, procedure automation and need for complex multi-disciplinary
and multi-language software platforms create even more challenge for decision makers and architects
working on such systems/platforms. Human brain goes through changes when it is being trained for such
work.
With slowdown in progress of advancements in technologies such as computing, electronics
(Moore’s law doesn’t work anymore!), software systems (open-source has taken out “IP” novelty out of
lots of software systems), storage and networking etc; process understanding, improvements in

7. performance, multidisciplinary knowledge and multi-dimensional experience has already started
becoming forefront technological competition. It’s no wonder that people with such skill set would be
sought after resources in years to come.
About 10 years ago processer clock speeds stopped doubling every 18 month (density of transitors) or
so and miniaturization stopped decreasing (already at atoms level) sizes. Almost 20 years of global focus
and competition has brought computing technologies’ innovation to a point where it has now become
highly dependent on almost revolution in other technology areas for further improvement in this space.
Few new choices or dimensions of growth are
1. Social computing
2. Quantum computing
3. Genetic computing
Intellectual Property creation is again back to “Human” from machines and systems (sort of IP
manufacturing that was on for years!!). With these high level changes in technology landscape, human
brain neuron would become new set of electronic transistors.
Development of human brains’ computing capability i.e. activation of more and more neurons in
human brain (Moore’s law’ on human brain neurons!) would also become a competitive parameter and
would add to total computing power of states, countries and the world. Capable, well trained and fresh
talented brain source would be sought after destination for innovative work.
With education, experience and knowledge gathering human brain goes through multiple changes as
was mentioned before. Here is a sequence of changes that is sort of a normal progression of brain
development for someone who is involved in complex, multidisciplinary, decision making job:
1. Neuron firing: Simple steps
2. Neuron path in brain: Organized but simple and straight forward control procedures
3. Neuroplasticity: Complex, Challenging and Risky decision making
4. Neural networks: Multidimensional, multi-field, multidisciplinary work (switch)
5. Hippocampus activation for neural network management: More than 3 -4 thinking areas active at
the same time for decision support
Graphical representation of above mentioned process can be shown as follows:
(Hippocampus management of procedural memory, language switching and decision making for conflict
resolution)
Stage 1: Neuroplasticity because of longer term complex work -> Hippocampus activation
(Brain capability achieved: Multi-disciplinary decision making, memory ready for multi-dimensional
knowledge gathering, operations and procedures simplification capability, connectivity for multi-field
decisions)

8. Stage 2: Brain ready for multi-disciplinary, multi-dimensional work
Following diagram shows effects on human brain of multiple active areas working at the same for
years. Because of work experiences which involved working with different sections of brains at the same
time and needed active switching between brains parts, brain would have developed higher level neuron
plasticity between different parts of brain in this second case thereby activating hippocampus.
A person who is involved in operations understanding and simplification work (procedures and
programs both), works on systems which need activities in multiple areas of brain i.e. front brain (data
science) left side brain (business/economic) and right side brain (physics, programming) at same time and
is also responsible for multi-field work (active switching of technologies and programming languages)
Multidisciplinaryactivitytraining
Three differentswitchesactive atthe same-
time (complex programmingformulti-
disciplinary,multi-languageplatform)

9. because of involvement in complete system and platform development would have almost developed such
brain.
A good example person would be a devops system/platform designer and architect who is
involved in multi-field/multi-disciplinary work from below technology area:
{ DevOps 2
}, Pipeline of DevOps projects, DevOps on DevOps, Data Science
In the realm of “organization behavior studies”, above developments in human brain and its
manifestation in day to day work life could be outlined and compared with average development of
normal resource as follows:
3. Detailed “functional network” processing
For roles in which subject matter expertise in multiple areas is almost must such as a system
architect’s or software platform architect’s role, person has to not only know and be handy at a lot of
decision parameters and facts about the whole work-stream area but also should have capability to dive
deeper in the context when needed. For deeper understanding of any concept, content and background
material you would have to properly process, analyze and synthesize it.
Connectivity between parts of subject matter and it’s connectivity with other known facts and figures
has to be established and stored. On top of it, it has to be easily accessible again in memory for future
reference. Consistent exercise of these faculties of human brain would develop certain capabilities in

10. person’s body and brains. Here are some memory, brain use and decision processing impacts which
would become easily noticeable in such a person’s work related information processing:
1. Inter-personal communication:
Communication skills development happens over growth period in early and mid career life of any
individual when the person inside gains his attitude, creates his persona and establishes his relationship
circle. Here are few stages a person goes through when he goes out and work in the bigger world outside
of his personal social circle. He deals with different types of folks in different stages and walks of life and
learns to share information, feelings and thoughts with external world. Language skills development
happen in following stages:
Stage 1: Speak (mind, heart, feelings, thinking etc)
Stage 2: Speak – Listen (empathetically, sympathetically, honestly)
Stage 3: Speak – Listen – Connect (in full light, proper understanding and context)
Stage 4: Speak – Listen – Connect – Process (intellectually, intelligently and timely)
Stage 5: Speak – Listen – Connect – Process – Analyze (using proper tools, learnings, education)
Stage 6: Speak – Listen – Connect – Process – Analyze – Weigh (emotionally, fairly and equitably)
Stage 7: Speak – Listen – Connect – Process – Analyze – Weigh – Tell (to people, audience, learners)
2. Information analysis:
a) Reading between the lines:
Reading through text would not be just simple reading through text for multi-subject matter
expert as it is for a normal “single” subject matter area person. Reading would generate a mental picture
of this new “information” to multiple memory storage areas in such a person’s brain as his memory will
read this new “text” information in the light of a lot of existing knowledge from multiple subject matter
areas stored in many different brain and memory parts. Brain would try to associate this new processing
to multiple areas simultaneously and it would trigger “decision” making about each and every opinion
statement which would be generated by different subject’s understanding. Hence new information would
be stored as a branch with multiple leaves pertaining to multiple different subject matter areas.
Dimensional or disciplinary view of text as it gets stored in human brain in summarized way
would stay there for longer period of retention:
“Text summary” as it gets stored within a node in multi-disciplinary human brain
Textprocessing
Pointersubjectmatter1
Pointersubjectmatter2
Pointersubjectmatter3

11. b) “A picture is worth a thousand words.”:
This is more true in some subject matter areas over others i.e. statistics would have more
information summarized and presented to you in the form of 3 to 4 dimensional charts while history
would mostly have simple dimension timeline in text. Otherwise, in simple words, some people would
generate that thousand word’s information from a picture more effectively over others.
Some subject matter areas expertise would trigger multi-dimensional or multi-disciplinary
analysis for picture formation in human brain and some others would not enable that feature in human
brain “picture” would just stay as picture element. Multi-field multi-dimensional information would
generate an image or understanding in brains of and information analyst as follows:
This image would clarify difference around text processing and image processing both by some
brains and just single timeline text processing by others.
c) PIP or picture in picture feature:
“PIP” or picture in picture sort of processing for multi-disciplinary and multi-dimensional
elements helps deeper understanding of complex subject matters. This is sort of saying that not only text
and pictures are properly processed but also sub-content in them is processed at the same level of
technical expertise. In terms of “programming lingo” you would define it as processing structures of
multiple programming languages and multiple varied sources of code origin to the level of elements to
elements mapping for proper assignments and associations.
d) “Frame ofreference” context is important:
Especially in diverse work, multi-disciplinary and multi-dimensional view is highly important. A
person who would work in such work-area would develop some faculties and abilities inside of him.
Single view) Textprocessingwith1D imagesin-line with text
Multi-view) Addl.storage of X,Yand Z elementsandsummaryfromchartinfo
Some timelinepointeratsame level
Textprocessingwith3-D
imagesin-line withtext
One level
deep
Textprocessingwith3-D
imagesin-line withtext
One level
deep
Subjectmatter
linkages

12. Some of these would even be physically visible in the form of anatomical changes. Someone who solves
complex problems would developer highly efficient categorization or data management skills to the
extent of properly framing even the unimportant and minor things in very efficient and “beveled” frames
of content in brain. They come handy many times in decision making.
In other words notion of nodes or hierarchical connectivity (sort of well outlined frames of reference)
between work elements would be visibly strong and mind-mapped in such brains and brain would have
proper frame based categorical processing for these element as shown above.
4. Skills development focus
For multi-disciplinary, multi-field and complex work, constant focus and deeper idea development
skills are needed. In such jobs, perfect knowledge along with very strong background and constantly
progressive experience is must. One such example would be mathematics knowledge development along
with development of computer science knowledge in computing careers. Usual development track for
such careers would certainly have basic requirements of constant learning, knowledge accumulation and
skill-set development. Someone whose job is highly analytical would have developed mathematics
knowledge with career progression as complexity of the jobs would have required deeper understanding
of core and adjacent concepts both from science and mathematics field.
Along with that, person would have to have risk taking and challenge seeking ability developed by
developing penchant for attempting “unsolved”, “unique”, “8x8 multiple subject areas” and “multi-field
complex” problems. One example mathematic skills development ladder that such a person would have
followed while solving computer science problems in last 30 to 40 years of constant computer science
development is as follows (“10” levels of mathematics knowledge development in computer science in
this period):
Skills development track: Eg. Mathematic
(Last 30 years of computer science & computer engineering development):
Contextframe
Textprocessingwith
3-D imagesin-line
withtextandits own
inertial frame
One level
deep
Contextframe
Textprocessingwith
3-D imagesin-line
withtextandits own
inertial frame
One level
deep
Beveledmulti-
depthassociations
inbrains

13. 1. Arithmetic
2. Algebra
3. Geometry
4. Trigonometry/Time
5. Calculus/Metrics
6. Probability, Statistics, Number systems, Abstract and dimensional math, Signals, circuits &
control systems, Defense science math
7. Stochastic, Randomness, Space, Computational and informational theory
8. Spatial/geo, n-discreet, n-order, n-dimensional
9. Hypothesis testing, Game theory, Decision science, Optimization, Econometrics, Business
Ratios, Modeling. Valuations
10. Deductive & Predictive Analysis, Derivatives, Multiple regressions and data science
algorithms
As one would have already noticed, complexity increases as you go down the “learnings” list for
mathematics shown above. In case someone uses this knowledge for solving problems in some other area
“such as computer science”, complexity for understanding of that area increases as well as one would
have to map newer learnings to newer requirement in that adjacent area. Multi-field knowledge “use”
based highly differentiated jobs would needed academic calibration type of calibration for job
performance measurements. One such test scale is shown below:
Academic: Day to day work performance level based on academic ability calibration:
Performance in knowledge industry jobs highly co-relates with person’s academic ability as most of
the work requirements are similar to requirements for a good student. High memory retention, good
problem solving skills, good reasoning ability and highly disciplined behavior are some of such
Case 1
Case 2
Case 3

14. requirements. One such academic ability calibration method that can easily be used in performance
measurement for such jobs is shown above. There is example of 3 different types of performers in the
above diagram:
Case 1: Day-to-day performance level: 40%, Exam prep drive: 25%: Very low stickiness for future
reference; “Longer” time for exam prep. Highs and lows around exam time. No time for value add work,
over cramming even over “mental” and “genetic” abilities
Case 2: Day-to-day performance level: 60%, Exam prep drive: 25%: Very low stickiness for future
reference; “Longer” time for exam prep. Highs and lows around exam time. No time for value add work,
too much cramming for storing pre-solved problems
Case 3: Day-to-day performance level: 80%, Exam prep drive: 5%: Very high stickiness for future
reference; don’t have to exert two much for exam prep (5%) because of constant disciplined learnings;
Can devote time to useful value additions such as creativity, extra projects and new challenges, attempt at
problems in exam over writing “bookish” pre-prepared, pre-canned answers
The difference between 3 categories of individuals would be very clear and would show up in their
job as lack in risk taking, no creativity and highly undisciplined behavior.
Other such tool for similar kind of benchmarking is comparison between examination performance of
two test candidates. For example, here is a comparison between two students on their exam attempt
philosophy. Similar data analysis would give you your right choice:
Name Total
Score
Attempted
Exam.
Scored
Marks
% Scoring Accuracy Rate
(confidence interval)
Comment
“A” 100 85 81 95% Excellent read, good drawing, good
handwriting and story telling
“B” 100 100 85 85% Just average throw away story. No
use of good writing tools such as
drawing, handwriting and story telling
5. Corporate fit testing
As and when an employee or researcher joins a new team, a “corporate” fit test for him starts in new
place of work almost on the first day. Multiple groups or job ladders at work would test the person for his
fit in new organization in a real testing environment or in a virtual one. Some examples of corporate
laddering are:
 Workers ladder
 Idea generation ladder
 Research focus and industry association ladder
 Employee and corp. “identity” ladders
 Learners, trainees and researchers ladders
Such laddering even exists in academic community and colleges. Students are profiled and placed in
some “bucket” based on their core abilities either in education rankings or in other academic testings such

15. as general knowledge and intelligence testing, sports and leadership performance or social group
networking abilities.
Following example shows students’ rankings for learners or subject matter focused students who are
highly efficient in scoring on subject matter and in some cases even using any means. Electronics,
Communication and computer science student careers benchmarked on masters or undergraduates scoring
range of student (another academic type of scale):
S. No. %age Scoring
Range
Career choices Class %age in such careers
1. 85%
Research, knowledge &
consulting
5% (A+ students)
2 80%
Knowledge, consulting &
management functions in
same area
10% (A students)
3 75%
Administration, Management,
Process & procedures
improvement consulting
15% (B= students)
4 70%
Industry & brands roles,
Management track in
companies
20% (B students)
5. 65%
Industry & brands,
Supervisory roles
25% (C+ students)
6 60% Jobs market 20% (C students)
7 55% Jobs market chance 5% (qualified, grace, * students)
Different types of work places or for that matter research environment/institute of higher learning
provide you different types of culture, feel and opportunities. From my own experience, I can hardly
forget my past experiences about certain places. For example, following memories from my past
experiences at workplaces, research opportunities or higher study opportunities are always going to stay
with me:
From my past experiences:
 DIT education program provided unparallel and exceptional tailoring autonomy and for that
purpose to just an undergraduate student i.e. open selections from wide range of course work
from electronics-mechanics-communication engineering and computer science/engineering.
 Who can forget CDOT India and Duet India breakfast line interactions with colleagues?
 DRDO India shadowing opportunity was fantastic and equally good was New York City weekend
study experience.
 Cisco University & training programs were that of a top 20 university level.
 Cornell classroom learning experience and Santa Clara classroom presence was incomparable.
 Avaya 1:1 with executive was unique and so was Sandvine small company feel.

16.  Google café would always be unforgettable and so would part-time education experience at
University of California, Santa Cruz.
Similarly every workplace or education place has some uniqueness. You would just have to imbibe
and inhale it to savor it. At very high level engineering or technical work can very well be mapped to
education goals of technical programs. For example here is a table that show target goals for technical
education in generality which should apply to any field of engineering in work place too.
Above mentioned three core groups would define even three or four major work groups in technology
oriented companies’/workplaces when it comes to major classification.
1. Job and employee groups
2. Behavioral and fit based grouping
3. Corporate organization groups
4. Social network and people groups
5. Education, research and workplace training relationship groups
One would have to pass the test and on success become part of one of these corporate groups for
sure to find a place in company for longer period of employment in any workplace.
6. Organization fit
Organization fit is also highly important attribute for proper mapping of talent to right groups.
Corporate growth or for that purpose, talent growth would hugely suffer if development of resources in
any of the needed corporate groups is not proper. It would not only affect company and career selection
“variables” of job seekers but also free market nature of job placements. These types of imbalances in

17. any organization would also affect internal and external outlook along with culture and makeup of the
company. Here are some parameters which would be very visible to people making jobs and careers
sections first time or mid-career:
1. Salary distance in the company eg. Mgmt L3 vs. Support L1 financials: Total life style and
living standards variability in the corporate or company setup i.e. someone who is Mgmt level 3
person and someone who has just joined company as support level 1 employee. Test for this
distance’s testing would be
a) Could they even become part of same type of neighborhood and by the way just forget about
same company. This is just based of salary differences.
b) Other human dignity level parameters would be secondary parameters in such comparison.
“Generative” human life differences created by social, financial and life standards differences
can only be laughed at and derided if they distance is too big.
2. Career choice distance in company eg. Mgmt L1 vs. Support L3 choice: Career selection would
also suffer. Some jobs would not only need deeper understanding of needed subject matter but
also global and social variables understanding. A person who is not a fully developed and
educated outperformer person would not be a good fit for global management type of roles in
most cases. On the other hand very good benefits for support type of work would affect initial
career selection choices of individuals giving rise to career choice option “imbalance”. You
wouldn’t have right management talent and your support talent is from a pool that has already
made compromises on education and work performance efficiencies just for probably “money”
and is probably over-fit for position resulting into problems like sandbagging, absence at work
place (un-interesting work but pays) and unnecessary anger.
And on top of it other management colleagues are not adequately prepared for managing
problem resources. Career switching world be visible and so would be clashes between parts of
corporate. Problems such as “Mgmt ladder not better qualified over R&D ladder” and support
ladders “in-charge of everyone’s life” with operation ladder usually absent and nowhere to be
seen would result. Proper company work levels laddering and qualified management development
programs would not happen or work and inadequate and less qualified operations and support
people with very generalist experience wouldn’t leave for any other similar opportunities making
room for better hiring. Both distances are shown below in the diagram:
“Corp knowledge ladders” and “ranks ladders” connectivity amongst different orgs

18. There are multiple factors of association which would attract someone to an organization and
vice-versa i.e. personal attributes of a personal would push him or attach him to certain specific
groups in an organization. Some of these attribute parameters are very simple such as regional or
locality match, language affinity, schooling, sexuality, hobbies and interests. But some of the other
factors are way too complex factors of association such as special interest groups, career or job match
groups, associations on daily activity and discipline level, outside of work associations, eating habits,
sports interest, interests in social organizations etc.
Underneath hidden drivers for interests in a company could be many. For example learning
capability and daily activity level of a person is highly associated with person’s age group. Young
people usually are very good learners and would choose very structured ways of learning.
Usually they have very active life style and better prepared for busy routine and stress. Preference
for efforts with such requirements would be more in this ground while people who are in little older
age group by body and personality development would prefer stress-less work and easy learning
choices. Genetics would also dictate person’s behavior:
For example age groups and learning attitude genetic tuning based on person whole life expectations:
A person who is genetically from let’s say a lower life expectancy group would usually age faster and
would show signs of older age much earlier in life. Choice set around following life activities would also
be such
 Active life style
 Attitude towards obstacles and problems
 Mindset for education and learning
 Social connectivity needs

19. On the other hand person who is from a higher life expectancy group would usually age slower than
peers and would stay active, would have better learning ability and would be ready for stressful work
even in middle part of life. Some of such events would be
 open for relocations,
 ready for travel and longer hours per day
 “diverse” type of job requirements.
7. Personal development tracks
Career planning involves doing things with deeper analysis and data driven decision making whether
it is at the start of someone’s career or it is something a person plans mid career. It is also an important
step in setting up personal development goals along with other necessary life goals. Different type of
folks would do things differently based on factors of differentiated development in their life as everyone
knows that diversity is almost essential for uniform growth of humanity and it creates vibrancy and colors
added different “faces” of folks in social life.
Some folks are “macro” decision makers and some are “micro” decision makers. And many people
depend on someone else for their effective decision making. That is why we have so many career choices
and so many types of job performers.
Along with career choices, career growth planning differs as well among people. Line focus is the
only choice for some employees while multi-dimensional growth “even if it is horizontal” is almost an
“OK” and good enough choice for others. Vertical growth at the cost of limiting knowledge and
compromising with ethical and moral code is not even a choice for many even if rewards are handsome.
This creates imbalances in career growth for people.
Comparison between two career choice scenarios and their effect on personal development is outlined
below and is also shown in diagram that follows the scenario comparison.
Scenario 1:
Candidate with only one focused area through 4 levels of growth and only focusing on vertical
management ladder growth track, essentially transferring few years of work as simple and straight
forward tasks and 1 field “jobs” to levels below thereby adding more and more simple easy to replicate
jobs for lower skill job groups. Some of the important characteristics for such jobs would be:
1) Number of active words in vocabulary: Up to 1500
2) Number of easily accessible information elements: Up to 150,000
Scenario 2:
Candidate with preference for diverse horizontal growth over vertical growth for same period while
ignoring people management opportunities and adding adjacent skills (up to 6 in example case) with each
new such pass thereby creating a job profile that is not very easy to replicate and but is essentially a

20. higher skill hard to replicate new “job” type for future learners to ponder and work on. Mentionable
characteristics for this type of job creator would be:
1) Number of active words in vocabulary: Up to 6000
2) Number of easily accessible information elements: Up to 600,000
Two circles in above diagram show all-round growth as shaded area on the wheel. Covered area
on wheel shows achieved personal growth and essentially control realm of decision making for any type
of simple, multi-dimensional & multi-disciplinary work. It is very clear from these simple circles that
decision making for someone who has focus on growth in just one area would be limited to that area only.
Hence skill set would mostly be only suitable for “line” work type of jobs. While a person who has
focused on all-round growth would be a better decision maker for “whole” decisions involving multiple
organizations and work teams using tools from multiple fields of study.