The document discusses understanding situations in business problem solving. It explains that fully understanding a situation requires considering all previous analysis steps, and may require investigating ambiguous factors. Business problems can be complex with multiple dynamically changing variables and circumstances outside of one's control. The document advises gathering as much data as possible from different perspectives before making an informed decision, though this may require more time than others prefer.
The document summarizes a presentation given to senior executives on decision making. It discusses how decision making is an important process that impacts organizations but is often not given careful thought. It outlines different types of decisions and models of decision making. It also presents a six step process for managerial decision making and emphasizes that properly defining the problem is the most important first step. Mathematical tools can help but qualitative approaches are better able to define problems and alternatives. The presentation aims to develop an effective "Super Strategy" approach to decision making.
Highly recommended course for everybody who seeks to find himself at dynamic 21st century environment! https://lnkd.in/eHabDGj
You'll find it @ https://www.coursera.org/learn/leadership-21st-century
The document provides 9 rules or ways to become more efficient and effective in business. Some of the key points include:
1) Follow the 80/20 rule - 20% of efforts will yield 80% of results. Analyze data to find opportunities.
2) Don't try to analyze everything ("don't boil the ocean") - be selective and focus on key drivers rather than getting lost in unnecessary details.
3) Be able to clearly and concisely explain your solution in 30 seconds, as in an elevator pitch.
Page 2027.1Two Kinds of Decision Making Rational and Nonratio.docxaman341480
The document discusses rational and nonrational models of decision making. It provides details on the four steps of rational decision making: 1) identify the problem, 2) think of alternative solutions, 3) evaluate alternatives and select a solution, and 4) implement and evaluate the chosen solution. It also discusses two examples of nonrational decision making: satisficing and intuition. Finally, it analyzes Starbucks' recovery from a crisis in the late 2000s through Howard Schultz's return as CEO and strategic changes to restore the company's values and atmosphere.
The document discusses redefining how work is done by focusing on results rather than physical workspace or time spent working. It argues that most work can be done remotely as long as employees produce the needed results. Virtualizing work allows reducing costs while increasing flexibility and productivity. The document envisions a future where remote work is the norm and traditional offices are less important, with organizations relying more on a supply chain of flexible workers producing results.
This document discusses a learner's guide to data analytics. It outlines several key insights:
1) Business leaders need analytics to make effective decisions and should see it as a thinking skill rather than technical skill.
2) Managers must differentiate between good and bad data analyses by thoroughly understanding problems and how data is generated. They should also question unusual results and know the data themselves.
3) These insights are relevant for Indian managers who must integrate analytics into business plans by starting with a specific problem to solve and ensuring data collection aligns with this problem. Managers should understand data sources and be inquisitive when presented with information.
The document summarizes a presentation given to senior executives on decision making. It discusses how decision making is an important process that impacts organizations but is often not given careful thought. It outlines different types of decisions and models of decision making. It also presents a six step process for managerial decision making and emphasizes that properly defining the problem is the most important first step. Mathematical tools can help but qualitative approaches are better able to define problems and alternatives. The presentation aims to develop an effective "Super Strategy" approach to decision making.
Highly recommended course for everybody who seeks to find himself at dynamic 21st century environment! https://lnkd.in/eHabDGj
You'll find it @ https://www.coursera.org/learn/leadership-21st-century
The document provides 9 rules or ways to become more efficient and effective in business. Some of the key points include:
1) Follow the 80/20 rule - 20% of efforts will yield 80% of results. Analyze data to find opportunities.
2) Don't try to analyze everything ("don't boil the ocean") - be selective and focus on key drivers rather than getting lost in unnecessary details.
3) Be able to clearly and concisely explain your solution in 30 seconds, as in an elevator pitch.
Page 2027.1Two Kinds of Decision Making Rational and Nonratio.docxaman341480
The document discusses rational and nonrational models of decision making. It provides details on the four steps of rational decision making: 1) identify the problem, 2) think of alternative solutions, 3) evaluate alternatives and select a solution, and 4) implement and evaluate the chosen solution. It also discusses two examples of nonrational decision making: satisficing and intuition. Finally, it analyzes Starbucks' recovery from a crisis in the late 2000s through Howard Schultz's return as CEO and strategic changes to restore the company's values and atmosphere.
The document discusses redefining how work is done by focusing on results rather than physical workspace or time spent working. It argues that most work can be done remotely as long as employees produce the needed results. Virtualizing work allows reducing costs while increasing flexibility and productivity. The document envisions a future where remote work is the norm and traditional offices are less important, with organizations relying more on a supply chain of flexible workers producing results.
This document discusses a learner's guide to data analytics. It outlines several key insights:
1) Business leaders need analytics to make effective decisions and should see it as a thinking skill rather than technical skill.
2) Managers must differentiate between good and bad data analyses by thoroughly understanding problems and how data is generated. They should also question unusual results and know the data themselves.
3) These insights are relevant for Indian managers who must integrate analytics into business plans by starting with a specific problem to solve and ensuring data collection aligns with this problem. Managers should understand data sources and be inquisitive when presented with information.
One histogram is sufficient when capturing data for a specific event at a specific time. However, for processes that occur over a period of time, multiple histograms may be needed to fully understand the data and make informed decisions. In the examples, one histogram would suffice for the physics exam scores but multiple histograms would be necessary to forecast manufacturing production over a week.
The operations manager created a histogram showing low production on Monday between 8-9am, 11am-12pm, and 12-1pm. However, looking at histograms for the rest of the week revealed the actual problematic times were 8-9am and 1-2pm. Examining multiple data points over several days provided a more accurate understanding of the production issues than a single day alone.
The document discusses histograms and explains that a single histogram only provides a snapshot of data at a single point in time. It may miss seasonal variations or overall trends by not looking at data over an extended period. The document recommends exploring multiple histograms over time to better understand patterns in a process rather than single events. A single histogram only tells part of the story, while multiple histograms can provide a more complete picture.
The document discusses how histograms can hide important information if the bins are too wide or narrow. It provides an example of a company's revenue histogram with overly broad quarterly bins that obscures drops in monthly revenue. Narrowing the bins to a monthly interval reveals that revenue shortfalls in specific months contributed to the company missing its annual goal.
Here are some potential arguments for different bin sizes in the revenue histogram example:
- One day bin size: Could show day-to-day fluctuations and identify specific high/low revenue days. However, 365 bins may be too granular and show random noise.
- One month bin size: Could identify seasonal trends across months while still showing monthly variations. 12 bins balances granularity and visibility.
- One quarter bin size: Would simplify the histogram but may hide important variations across months within each quarter. Only 4 bins may obscure seasonal patterns.
The appropriate bin size depends on whether the goal is to identify daily, monthly, or quarterly trends. Monthly binning (12 bins) seems best for capturing seasonal patterns in revenue
The document discusses interpreting histograms by understanding what they represent and looking for inconsistencies compared to expected patterns. It explains that histograms show frequency distributions of data and notes some key steps for interpretation: 1) Understanding what histograms show, 2) Comparing to historical data to identify unexpected peaks or valleys, and 3) Looking for inconsistencies compared to a typical distribution curve. The example histogram shows a skewed distribution of website visit durations that may be normal given most visits are short.
Here are some common mistakes to watch out for with histograms:
- Unequal bin widths - bins should represent equal intervals of data
- Missing or combined bins - all data values should have their own bin
- Too few or too many bins - too few bins obscure details, too many bins appear random
- Unclear or misleading labels - scales, titles, units should be unambiguous
- Inaccurate or fabricated data - data should be verified as real and measured properly
- Biased selection of data or timeframe - should represent the overall population or period fairly
Paying close attention to these details can help identify potential issues or attempts to manipulate the presentation of information in a histogram.
Here are a few examples where a histogram would be more useful than a bar chart for large frequency distribution data:
- Test score results for a large class - showing the distribution across ranges of scores rather than individual scores
- Monthly website visitor durations - showing distribution of visit times across duration ranges
- Employee salary ranges across a large company - showing frequency of salaries within pay brackets
The key is that histograms are better for continuous data divided into ranges, especially for large data sets, as they can show the overall shape and distribution more clearly than individual bars. Bar charts are better for discrete categorical comparisons.
Histograms are useful for quickly understanding large datasets but can conceal details depending on how bins are constructed. They follow a standard distribution curve normally but a non-standard curve may indicate an issue. Histograms are a snapshot in time so multiple snapshots are needed over time to understand data evolution. Bin construction and only using one histogram can change the story told.
The document discusses how graphs and charts represent data but histograms have more flexibility which can be used to conceal or change the story told by the underlying data. Specifically, histograms can distort the representation by manipulating variables like bin size. The key point is that the narrative from data depends on properly using and presenting the right data.
One histogram is sufficient when capturing data for a specific event at a specific time. However, for processes that occur over a period of time, multiple histograms may be needed to fully understand the data and make informed decisions. In the examples, one histogram would suffice for the physics exam scores but multiple histograms would be necessary to forecast manufacturing production over a week.
The operations manager created a histogram showing low production on Monday between 8-9am, 11am-12pm, and 12-1pm. However, looking at histograms for the rest of the week revealed the actual problematic times were 8-9am and 1-2pm. Examining multiple data points over several days provided a more accurate understanding of the production issues than a single day alone.
The document discusses histograms and explains that a single histogram only provides a snapshot of data at a single point in time. It may miss seasonal variations or overall trends by not looking at data over an extended period. The document recommends exploring multiple histograms over time to better understand patterns in a process rather than single events. A single histogram only tells part of the story, while multiple histograms can provide a more complete picture.
The document discusses how histograms can hide important information if the bins are too wide or narrow. It provides an example of a company's revenue histogram with overly broad quarterly bins that obscures drops in monthly revenue. Narrowing the bins to a monthly interval reveals that revenue shortfalls in specific months contributed to the company missing its annual goal.
Here are some potential arguments for different bin sizes in the revenue histogram example:
- One day bin size: Could show day-to-day fluctuations and identify specific high/low revenue days. However, 365 bins may be too granular and show random noise.
- One month bin size: Could identify seasonal trends across months while still showing monthly variations. 12 bins balances granularity and visibility.
- One quarter bin size: Would simplify the histogram but may hide important variations across months within each quarter. Only 4 bins may obscure seasonal patterns.
The appropriate bin size depends on whether the goal is to identify daily, monthly, or quarterly trends. Monthly binning (12 bins) seems best for capturing seasonal patterns in revenue
The document discusses interpreting histograms by understanding what they represent and looking for inconsistencies compared to expected patterns. It explains that histograms show frequency distributions of data and notes some key steps for interpretation: 1) Understanding what histograms show, 2) Comparing to historical data to identify unexpected peaks or valleys, and 3) Looking for inconsistencies compared to a typical distribution curve. The example histogram shows a skewed distribution of website visit durations that may be normal given most visits are short.
Here are some common mistakes to watch out for with histograms:
- Unequal bin widths - bins should represent equal intervals of data
- Missing or combined bins - all data values should have their own bin
- Too few or too many bins - too few bins obscure details, too many bins appear random
- Unclear or misleading labels - scales, titles, units should be unambiguous
- Inaccurate or fabricated data - data should be verified as real and measured properly
- Biased selection of data or timeframe - should represent the overall population or period fairly
Paying close attention to these details can help identify potential issues or attempts to manipulate the presentation of information in a histogram.
Here are a few examples where a histogram would be more useful than a bar chart for large frequency distribution data:
- Test score results for a large class - showing the distribution across ranges of scores rather than individual scores
- Monthly website visitor durations - showing distribution of visit times across duration ranges
- Employee salary ranges across a large company - showing frequency of salaries within pay brackets
The key is that histograms are better for continuous data divided into ranges, especially for large data sets, as they can show the overall shape and distribution more clearly than individual bars. Bar charts are better for discrete categorical comparisons.
Histograms are useful for quickly understanding large datasets but can conceal details depending on how bins are constructed. They follow a standard distribution curve normally but a non-standard curve may indicate an issue. Histograms are a snapshot in time so multiple snapshots are needed over time to understand data evolution. Bin construction and only using one histogram can change the story told.
The document discusses how graphs and charts represent data but histograms have more flexibility which can be used to conceal or change the story told by the underlying data. Specifically, histograms can distort the representation by manipulating variables like bin size. The key point is that the narrative from data depends on properly using and presenting the right data.
2. In the previous two activities we practiced critical
thinking, attention to detail, and data interpretation skills.
In the final activity for Module 2, we’ll focus on
understanding the situation.
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3. The reason we left understanding the situation to the last is
that it is inclusive of all the previous steps and then some.
Each problem you encounter in business is different. Some
are straight forward, and some are very ambiguous. Your
expert sleuthing skills may be required to uncover what is
really happening.
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4. Oftentimes business problems are caused by
multiple, dynamically changing variables and are governed
by very nebulous, intangible circumstances like employee
attitudes. Other circumstances might be outside of your
control like government regulations or stock market
behaviors. Just because variables are hard to identify or
control doesn’t mean they can’t greatly impact your
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5. In the fast-paced business world, we want to have the
answer yesterday, and our haste can sometimes get us in
trouble. Making solid, informed decisions can take time, and
might ruffle a few feathers on people who want the
problem solved right now.
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6. In the fast-paced business world, we want to have the
answer yesterday, and our haste can sometimes get us in
trouble. Making solid, informed decisions can take time, and
might ruffle a few feathers on people who want the
problem solved right now.
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7. When encountering a business decision you should:
1. Formulate an opinion of your decision
2. Explore other points of view
3. Gather as much data as possible
4. Look for inconsistencies (use critical thinking and
attention to detail)
5. Does the data support your opinion? Why or why
not?
6. Solidify or revise your position
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8. CRITICAL THINKING: Why would understanding a
situation from as many perspectives as possible
be important in business problem solving?
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