THINKING ABOUT THINKING
Audience: PM & BA
Level: All
Date: May 26
Time: 11:30 AM - 12:30 PM
Description
Thinking is a big part of a Project Manager’s and Business Analyst's job. But how often have you spent time thinking about thinking? This presentation looks at thinking as a critical soft skill for project managers and how a disciplined approach to thinking improves you effectiveness as a change agent for the company in the role of project manager. The presentation will discuss the Thinking Hats, Five Types of Thinking, and brush into the entire world of Business Analytics. The presentation focuses on how the skills of Strategic Analysis, Tactical Analysis, Predictive Analysis, Data mining work together for the complete business management cycle. To add to the thinking equation, the session will explore the power of Social Media sentiment and how the way people "feel" about things is an important factor in the business equation. Think about it !!!!
1. Participants will understand the relationship between planning, analysis, problem solving, decision making and thinking.
2. Students will be able to explain an "Adapting to Whats Happening Model" that includes Data Recording, Strategic Analysis, Tactical Analysis, Predictive Analysis, and Social Media Sentiment. And how it impacts the business.
3. Students will explore various factors of human bias and how that impacts thinking. The student will understand that bias cannot not be completely eliminated, but should be embraced as a human factor in any thinking exercise. The student will understand that personal perspective/bias is a factor, but not THE factor in thinking.
Max Shron, Thinking with Data at the NYC Data Science Meetupmortardata
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Data visualization has become increasingly more important and sits at the center of how people learn about and experience the world. We process information about politics, business insights and every day decisions through “visual soundbites”. As data journalists, we have incredible power to both positively influence as well as misguide conversations with the choices that we make when presenting graphical results.
In this presentation, we will share some of the best practices that help deliver stories that matter and avoid creating those that mislead.
Knowledge Management in Healthcare AnalyticsGregory Nelson
The promise of actionable analytics in healthcare poses an inherent challenge as we seek to accelerate the time it takes to go from question to insight to action. The velocity of change, the demand for bigger data, the allure of advanced algorithms, the need for deeper insights, and the cost of inaction make knowledge capture and reuse an all too allusive goal.
In an evolving environment, healthcare organizations need to find ways to make greater use of prior investments in analytics products by reusing the commonalities of proven designs, metadata, business rules, captured learnings, and collaborative insights and applying them to future analytics products. By doing so in a strategic manner, they will be able to create rapid and efficient analytics processes and better manage time to value and reuse.
In this presentation, authors from two very different health systems with two very different patient populations will share their perspectives of the value of knowledge management and discuss the role of analytics in driving towards a learning health system. The authors will highlight opportunities and challenges using examples across clinical, financial, and operational domains.
The big-data explosion is driving a shift away from gut-based decision making. Marketing, in particular, is feeling the pressure to embrace new data-driven customer intelligence capabilities.
Marketers working 70-80 hours a week is not a great thing to hear.
But the requirement for them to have such a large amount of work time causes problems in the data selection and filtering.
Hence many marketers flunk the big data test
How to Become a Data Science Company instead of a company with Data Scientist...Ruth Kearney
The journey of becoming a data science company is more about the culture and thinking, rather than hiring and up-skilling individuals. In Novartis, while we are hiring data scientists and spending a lot of time in training and learning related to data science, the destination for us is one of cultural change, which is required to make us a data science company.
Head of the AI Hub Dublin, Ashwini Mathur will share practical insights into the Novartis journey and how each employee plays a part. He will talk about the value of using the language of data science throughout the organisation and how this takes them one step closer to becoming a data science company.
Max Shron, Thinking with Data at the NYC Data Science Meetupmortardata
Max Shron of Polynumeral shares techniques adapted from the worlds of design, consulting, the humanities and the social sciences which improve focus, communication, and results for data science campaigns.
Data visualization has become increasingly more important and sits at the center of how people learn about and experience the world. We process information about politics, business insights and every day decisions through “visual soundbites”. As data journalists, we have incredible power to both positively influence as well as misguide conversations with the choices that we make when presenting graphical results.
In this presentation, we will share some of the best practices that help deliver stories that matter and avoid creating those that mislead.
Knowledge Management in Healthcare AnalyticsGregory Nelson
The promise of actionable analytics in healthcare poses an inherent challenge as we seek to accelerate the time it takes to go from question to insight to action. The velocity of change, the demand for bigger data, the allure of advanced algorithms, the need for deeper insights, and the cost of inaction make knowledge capture and reuse an all too allusive goal.
In an evolving environment, healthcare organizations need to find ways to make greater use of prior investments in analytics products by reusing the commonalities of proven designs, metadata, business rules, captured learnings, and collaborative insights and applying them to future analytics products. By doing so in a strategic manner, they will be able to create rapid and efficient analytics processes and better manage time to value and reuse.
In this presentation, authors from two very different health systems with two very different patient populations will share their perspectives of the value of knowledge management and discuss the role of analytics in driving towards a learning health system. The authors will highlight opportunities and challenges using examples across clinical, financial, and operational domains.
The big-data explosion is driving a shift away from gut-based decision making. Marketing, in particular, is feeling the pressure to embrace new data-driven customer intelligence capabilities.
Marketers working 70-80 hours a week is not a great thing to hear.
But the requirement for them to have such a large amount of work time causes problems in the data selection and filtering.
Hence many marketers flunk the big data test
How to Become a Data Science Company instead of a company with Data Scientist...Ruth Kearney
The journey of becoming a data science company is more about the culture and thinking, rather than hiring and up-skilling individuals. In Novartis, while we are hiring data scientists and spending a lot of time in training and learning related to data science, the destination for us is one of cultural change, which is required to make us a data science company.
Head of the AI Hub Dublin, Ashwini Mathur will share practical insights into the Novartis journey and how each employee plays a part. He will talk about the value of using the language of data science throughout the organisation and how this takes them one step closer to becoming a data science company.
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This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
What Is Prescriptive Analytics? Your 5-Minute OverviewShannon Kearns
This slide deck walks you through the basis of understanding prescriptive analytics. Understand the different kinds of prescriptive analytics, how it works, its value, where to find use cases and more!
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
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Text Analytics can be fun, useful and distracting. It is not just about the tools, but about how to use tools to drive business outcome. In this deck, you will get a sneak peak into some uses of text analytics in Life Insurance Transformation
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Data is the new oil and Analytics is the combustion engine. Internal data plays a special role in every organization. See how one can become internal data rich and move the value needle. Through what we call thoughtful data engineering, we found good data trumped good models time and again.
Content will range start with why does Text Analytics need a special session on convincing boss, followed by a role play summarizing current mistakes, a sample elevator pitch for your boss and a proposed execution plan. The content is tailored for Mid to Senior Level Managers trying to convince Leaders/Executives/Heads. It doesn’t provide any technical details –methodologies, tools, vendors or hardware investments.
This was presented at Text Analytics West Summit 2014 at San Francisco. Questions? Reach out at Ramkumar Ravichandran @ Linkedin.
Data Analysis has been the buzz word this decade. Data Storytelling will be the next big influencer in Value creation.
Find out how Gramener is shaping the Data Storytelling landscape.
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The new imperative for organisational success will be 'hyper-decision making'. Gain insights into executive research around decision making; the concept of 'optimal' decision; the costs of lost opportunity associated with decision making; the factors that determine your organisation's 'decision intelligence quotient'; the drivers of 'risk'; what a systematic, integrated and comprehensive decision making process looks like; and more! Enjoy!
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This talk is a completely non-technical discussion of how the law currently regulates artificial intelligence (if it does at all) and what is likely to change in the near future. The talk is geared towards technically-minded practitioners of data driven intelligence with the aim of increasing discussion of the social and ethical impact of data driven AI and how to code responsibly.
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responsible for the coordinated management of multiple projects that are in alignment with organizational objectives. They are required to direct and manage
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Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
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Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
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Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
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3. A Little About My Career
• Alliance Data Project Manager –
Enhanced Mobile Loyalty Suite
• Nationwide Insurance – Benefits
Realization Advisor
• Project Manager for Consumer
Energy
• Program Manager for AT&T
eBonding Projects
• Adjunct Instructor
• Knowledge Management Advisor
• University Academic Advisor
6. PMI Talent Triangle(SM)
Hard skills
Certification
Common Glossary
Application of Best Practices
Soft skills
Behavior Changes
Influence
Advisement
Emotional Intelligence
Decision Making
Acumen
Products and Services
Quality
Process / Workflow
7. How We Think People Think
• It’s a question of focus
Facts Logic Truth
8.
9. Basic Functions of a Human
Thinking
Make sense
of the world
• Analyzing
• Judging
• Comparing
• Synthesis
Feeling
Tells us how
we are
doing
• Happy
• Sad
• Included /
Excluded
• Stressed /
Calm
Wanting
Drives us to
act the way
we do
• The Rule of
Remarkability
• Goals
• Purpose
• Agenda
10.
11. Analytics vs. Analysis
• is a multi-dimensional discipline.
– There is extensive use of mathematics and statistics, the
use of descriptive techniques and predictive models to
gain valuable knowledge from data - data analysis.
– The insights from data are used to recommend action or
to guide decision making rooted in business context.
– Analytics is not so much concerned with individual
analyses or analysis steps, but with the entire
methodology.
– There is an increasing use of the term advanced
analytics, typically used to describe the technical aspects
of analytics, especially predictive modeling, machine
learning techniques, and neural networks.
• Analytics
12. Analytics vs. Analysis
• is the process of breaking a complex topic or
substance into smaller parts to gain a better
understanding of it
– Business Analysis is the practice of enabling change
in an organizational context, by defining needs and
recommending solutions that deliver value to
stakeholders.
– Requirements analysis – encompasses those tasks that
go into determining the needs or conditions to meet for
a new or altered product, taking account of the possibly
conflicting requirements of the various stakeholders,
such as beneficiaries or users.
– Traditionally involves creating a null hypothesis and
then validating that with data or test results.
• Analysis
15. What are Analytics
• Discovery and communication
of meaningful patterns in data.
• analytics relies on the
simultaneous application of
statistics, data extraction
tools, and operations research
to quantify performance.
• Analytics often favors data
visualization to communicate
insight (knowledge)
16. Critical Thinking Knowledge Skills
To be a professional of any kind in
the next 20 years, or even an
enlightened citizen, will require a
complicated set of thinking skills,
more than reading and writing.
The world isn’t filtered as it once
was. Kids are thinking. What we’re
trying to do is have them do it well.
PeterScharf,Professorof SocialEcology
17. Circumstance 1
• Observations
– You wake up in the morning and the lawn is
wet.
– You do not have a sprinkling system, hose, or
other watering capability.
– The street is also wet
• What can you guess happened last night?
19. Circumstance 2
• Sara and her Mother drove to the
computer store. Sara had her money she
had received for Christmas and the money
she had saved. She waited a long time for
this day. Finally, she would be able to look
up all the things she needed for school on
a computer and email her friends.
• What happened next?
23. Be Careful
• The inferential process can be valid even if
the premise is false:
• Lets use the deduction sited below:
– There is no such thing as drought in the West.
– California is in the West.
– California need never make plans to deal with
a drought.
24. Be Careful
In the example, though the inferential process itself
is valid, the conclusion is false because the
premise, There is no such thing as drought in the
West, is false. A syllogism yields a false conclusion
if either of its propositions is false. A syllogism like
this is particularly insidious because it looks so
very logical–it is, in fact, logical. But whether in
error or malice, if either of the propositions above
is wrong, then a policy decision based upon it
(California need never make plans to deal with a
drought) probably would fail to serve the public
interest.
25. Six Thinking Hats For Success
Green Hat
the creative side:
alternatives, out-of-
the-box ideas. What
additional
possibilities are
there? What else
can we try?
Blue Hat
the organizing view:
Manage the thinking
process. How should
we think about this?
Red Hat
The emotional view:
feelings, hunches,
intuition. What’s
your gut
reaction? How do
you feel about this?
Black Hat
The downsides: caution,
difficulties, weaknesses,
barriers. Why can’t we do
this? What might not
work? What are the
dangers and risks?
White Hat
Facts and
figures. What
information are
available and
needed?
Yellow Hat
The upsides: benefits,
values, positive
outcomes. How can we
do this? What are the
potential returns? Why
is it worth doing?
31. Business Intelligence
• Demographics / categories
• Terms such as profiling,
segmentation, or clustering, and
they fall under descriptive analytics.
• What is the behavior and who is
doing it?
32. Descriptive Analytics
• This is all about knowing what is going on,
the more real-time the better.
• What is the past 30-day response rate and
conversion rate for a specific campaign?
How did the response curve move through
time? When is the optimal day part for
email blast or ad broadcast? What is the
ROI? What channel and product offering is
the winning combination?
• This can be in a form of dashboard
reporting or any other conventional
reporting, but many call it simply
“analytics”.
33. Optimization Analytics
• Requires a complex type of modeling,
where “what if” type of questions are
answered.
• What if we spend more money on mass
media than on direct channel? What would
be the most optimal combination of
marketing spending that yields maximum
return? What would be the ultimate ROI?
This type of question is typically answered
by marketing agencies, and it involves
econometrics modeling.
• This type of analytics calls for different
types of data in comparison to typical
predictive modeling for 1-to-1 marketing,
but the whole process is also called
analytics.
34. Predictive Analytics
• we start asking questions in future tenses. Who
will respond to this campaign, and for what
product and through what channel?
• What are the potential values of each customer
and prospect? Who will stop subscription of
your service, and when would that be? When it
comes to predictive analytics, we need carefully
structured statistical models, which will return
“scores” that define likelihood of customers
behaving a certain way in the future.
• In terms of complexity, this is the most
demanding type of analytics, where trained
statisticians work with specifically designed
marketing databases with all kinds of custom
variables.
35. Prescriptive Analytics
• The emerging technology of prescriptive analytics goes
beyond descriptive and predictive models by
recommending one or more courses of action -- and
showing the likely outcome of each decision.
– "Prescriptive analytics is a type of predictive analytics,“
– "It's basically when we need to prescribe an action, so the
business decision-maker can take this information and
act.“
– Prescriptive analytics doesn't predict one possible future,
but rather "multiple futures" based on the decision-
maker's actions.
• In addition, prescriptive analytics requires a predictive
model with two additional components: actionable data
and a feedback system that tracks the outcome
produced by the action taken.
• "Since a prescriptive model is able to predict the
possible consequences based on different choice of
action, it can also recommend the best course of action
for any pre-specified outcome"
36. Adapting to what's happening
Company
Data
Recording
Data
Tactical
Analytics
Predictive
Analytics
Social
Media
Sentiment
Strategic
Analytics
How many we
sold
How many we
sold last year
How many
we’ll sell next
quarter
How people
feel about
them
Steve Lucas, SAP's SVP of database technology
Behavior Change
based on
Analytics
37. Thinking
• Can be considered an open-minded
process of:
– discovery and understanding
– analysis and application
– synthesis and evaluation
• Grouping Information based on data
• Extracting Knowledge from
Information
• Applying wisdom using Knowledge
38. Reasoning
• The relationship between thinking
and overall human genetics in the
application of reasoning between
what is known and what is unknown.
• Different types of reasoning:
– Inductive reasoning
– Deductive reasoning
– Predictive reasoning
39. Human Deficiencies
• As human beings we are blessed with the
ability to be creative and random in our ability
to determine a root cause analysis.
• However this ability also comes with several
defects that can hamper good analysis.
– Distinguishing between a symptom and a cause
– Dealing with our own bias and prejudices as a
factor in the root cause analysis, not the factor.
– Dealing with the illusions that can be created
between cause and effect
– Understanding Reasoning
– Understanding filters and how they can cloud
defining the problem
– Reducing false conclusions that were obtained
by faulty reasoning.
40.
41.
42.
43. Ursa Major – Great Bear
Navigation by Polaris
North
Credit: Stellarium.org Credit: Graham Bryant
Hampshire Astronomical Group
44. Types of Thinking
• Critical thinking is the mental process of objectively
analyzing a situation by gathering information from all
possible sources, and then evaluating both the tangible
and intangible aspects, as well as the implications of
any course of action.
• Implementation thinking is the ability to organize ideas
and plans in a way that they will be effectively carried
out.
• Conceptual thinking consists of the ability to find
connections or patterns between abstract ideas and
then piece them together to form a complete picture.
• Innovative thinking involves generating new ideas or
new ways of approaching things to create possibilities
and opportunities.
• Intuitive thinking is the ability to take what you may
sense or perceive to be true and, without knowledge or
evidence, appropriately factor it in to the final decision.
45. Higher Order Thinking
• System Thinking
– Higher-order thinking essentially means thinking that takes place
in the higher-levels of the hierarchy of cognitive processing.
Bloom’s Taxonomy is the most widely accepted hierarchical
arrangement of this sort in education and it can be viewed as a
continuum of thinking skills starting with knowledge-level
thinking and moving eventually to evaluation-level of thinking.
• "Size up and define a problem that isn't neatly packaged.
– Determine which facts and formulas stored in memory might be
helpful for solving a problem.
– Recognize when more information is needed, and where and
how to look for it.
• Carry out complex analyses or tasks that require
planning, management, monitoring, and adjustment.
– Exercise judgment in situations where there aren't clear-cut
'right' and 'wrong' answers, but more and less useful ways of
doing things.
– Step outside the routine to deal with an unexpected breakdown
or opportunity."
• Thought
– "Every day thinking, like ordinary walking, is a natural
performance we all pick up. But good thinking, like running the
100-yard dash, is a technical performance.
46. Power of Negative Thinking
• Bobby Knight – didn’t win games
as much as he didn’t lose them
• Avoid the mistakes
• Be prepared
• Be thoughtful of what could go
wrong
47. Critical thinkers tend to:
1. Be capable of taking a position or changing a position as evidence
dictates
2. Remain relevant to the point
3. Seek information as well as precision in information
4. Be open minded
5. Take the entire situation into account
6. Keep the original problem in mind
7. Search for reasons
8. Deal with the components of a complex problem in an orderly
manner
9. Seek a clear statement of the problem
10. Look for options
11. Exhibit sensitivity to others’ feelings and depth of knowledge
12. Use credible sources