Sustaining_Your_Startup_Success

Sustaining Your Startup’s Success
Prepared by Leon Khaimovich, OEC Island
for 1/5/2017 meeting
of IEEE Technology and Engineering Management Society
at AMD Campus, Sunnyvale, CA
This presentation is based on the paper with the same title, which can be found on
www.oecisland.com/Sustaining_Startup_Success.pdf

Presentation Outline
• The Problem and Its Extent
• The Reason for the Problem’s Persistence
• Possible Solutions
• Model of Problem-Solving Team Effectiveness
• Case Study
• Something Else
“Sustaining Your Startup’s Success” presented for IEEE SCV TEMS on January 5, 2017. AMD Commons, Sunnyvale.
Why are all big companies
so ineffective and why does
nobody really like working
there anymore?

The Problem and Its Extent
• 3 engineers, who later formed Nohau, are
creating a better in-circuit emulator for Intel’s
chip, than Intel’s own department of 100 people;
• Facebook decided to buy a 50-people WhatsApp
for $19B, because it made sense given their
ability to innovate;
• About 100-people strong Riverbed is beating
Cisco 9:1 during sales cook-offs, when charging a
lot for its appliance and competing with Cisco’s
product provided for free.

What Is To Be Done?
• We need a superior product, right?
• No, we need to make 1000s of good decisions
necessary to conceive, build, market, and sell
the superior product.
• That’s all?
• No, all Startup 101 success criteria are also
important, but…

To build the kind of organization,
which promotes and amplifies
individual effectiveness, rather
than hampers it.
There is one more necessary condition for a
company to sustain its initial success, when it is
growing:

Administrative Behavior
and Its Purpose
To develop “a careful and realistic picture of the
decisions that are required for the organization’s
activity, and of the flow of premises that
contribute to these decisions.”
Herbert Simon?
Never heard of him.

The Reason for the Problem’s
Persistence
No
Awareness
No Desire
to Be Aware
Are we against the
invisible enemy and
nothing can be done?

Possible Solutions
• Split Large Organizations into Smaller Units
• Increase Market Competition
• Use AI for Making Better Decisions
• Increase Our Understanding of Collectively
Making Better Decisions

Increase Our Understanding of Collectively
Making Better Decisions, Better AI and BI
1. “… it was necessary, first, to determine what important types of
decisions are made by operating executives; how accounting data might
be useful in making these decisions, and at what point in the decision-
making process the data most usefully be injected. By observation of the
actual decision-making process, specific types of data needs were
identified at particular organizational levels—the vice presidential level,
the level of the factory manager, and the level of the factory department
head, for example—each involving quite distinct problems of
communication for the accounting department.
2. Out of the analysis of data requirements, and the specific locations
where the needs have to be met, a pattern of accounting department
organization was developed that would be effective in providing data for
operating executives. …
3. The recommendations for organizational change were to be
implemented by bringing about changes in the communication pattern—
in the pattern of who-talks-to-whom-how-often-about-what—rather
than by formal changes in organization chart.”

Better BI and AI, #1
Put emphasis on observing and analyzing the actual
decision-making process. This will not happen by
itself, if a company deploys a general purpose BI
tool, which was designed to be flexible and
company-agnostic for the sake of widest
applicability.
A concerted and non-trivial effort has to be made
for using the tool for monitoring and evaluating the
health of decision-making and problem-solving
processes.

The current notion of “data science” is
somewhat naïve, and is showing lack of the
maturity in any field, which is using it.
In order to understand organizational problem-
solving processes, one needs a deep knowledge
of social and cognitive psychology, as well as
sociology of knowledge.

Modern AI is mostly based on statistics. Even if it
is using multivariate non-linear correlations and
multilayer neural networks for effectively
searching large problem spaces, it is not meant
for providing explanations, which would be
understandable by humans. Modern AI can
provide answers, but it does not show why they
are correct and does not help much with causal
explanations, which allow humans to extend
their understanding.

In order to be properly serving the needs of
increasing how effective and inspiring an
organization can be, BI has to be broad enough
to avoid dehumanization stemming from being
excessively focused on goals like profitability and
regarding employees as merely instruments for
reaching it.
How broad is
broad enough?

Definition of Term “organization”
“… the term organization refers to the complex
pattern of communications and other relations
in a group of human beings. This pattern
provides to each member of the group much of
the information, assumptions, goals, and
attitudes that enter into his decisions, and
provides him also with a set of stable and
comprehensible expectations as to what the
other members of the group are doing and how
they will react to what he says and does.”

Model of
Problem-Solving Team Effectiveness
• paying attention equally to cognitive and
emotional processes of collaborative problem
solving (CEMA = cognition + emotion +
motivation + action)
• based on data collected not in a lab but in a
company by observing a team, which was tasked
with finding solutions, which would be
implemented and would directly affect the team
• modeling just one team and one business process
it was designing

5 Health Indicators of
Collaborative Problem Solving
• S – quality of decisions and solutions;
• Du - understanding of solutions and other
knowledge generated during the problem solving;
• Dw - willingness to use solutions and other
knowledge generated during the problem solving;
• Iu - understanding of roles, goals, abilities, and
problem-solving preferences of others;
• Iw - willingness to work with others.

Case Study of a Declining Company
S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
others PS Health Indicators
Contour of No-Return
Initial team of dedicated bright people

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
Technical debt starts to grow.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
New people join the teams.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
New people have difficulty figuring out
old code. They introduce more bugs.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
Without proper onboarding during
team formation, people are not
progressing through “form, storm,
norm, perform.”

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
Vicious cycle kicks in:
Declining Du and Dw cause more misunderstandings
and frustration, leading to the further decline
of Iu and Iw. In turn, declining Iu and Iw make
people less inclined and capable to communicate in
productive ways, leading to the further decline
of Du and Dw.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
The growing technical debt makes
estimation and planning much more
difficult, which leads to the decline
of Du and Dw of managerial decisions.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
Iu, Iw, Du, Dw, and S continue to fall. The
company becomes highly political rather
than technical. At this point the reversal
becomes very difficult or impossible.

S – quality of
solutions
Du -
understanding
of solutions
Dw -
willingness to
use solutions
Iu -
understanding
of others
Iw - willingness
to work with
Technical guys, who stubbornly, relentlessly
and exclusively focus on improving S, will
exacerbate problems with Iu and Iw. Burn
out and firing “those jerks” will eliminate
them and cause further quick decline in S.

Learning from the Past, Edward Deming
“If you can't describe what you are doing as a
process, you don't know what you're doing.”
“It is not enough to do your best; you must know
what to do, and then do your best.”

Learning from the Past, Peter Senge
“There’s no other way out of this. We may not be
able to change the larger system overnight, but we
can commit to the continual development of
awareness and the capacity to choose. That’s why
personal cultivation is so important. It keeps you
sensitive and ‘in the matrix,’ so to speak.”

Time to
chirp!

Sustaining_Your_Startup_Success

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