This presentation analyses the beautiful TED Talk of Alan Smith on "Why should you love statistics". Gathering the insights and employing those insights is the major task of this presentation.
The document summarizes issues with how many marketers and managers utilize big data. It finds that the vast majority of marketers at Fortune 1000 companies still rely too heavily on intuition rather than data. This is a problem because the world is moving faster than these managers' experiences from decades ago, and data is needed to understand new industries, concepts, and a changing consumer landscape. Specifically, over-reliance on past experiences can increase errors, outdated views do not apply to startups or new technologies, and managers require updated data to understand evolving consumer behaviors. Additionally, even data-savvy managers may misinterpret data by assuming small differences are meaningful or using deceptive graphs, indicating a careless approach that hurts decision-making and
PitchSkills Business Presentation Template - Clean & PositiePitchSkills
The document provides three small tips to help the reader do what they love everyday. It recommends including hobbies and interests in your to-do list and schedule, reading articles to stay updated on topics you enjoy, and making these passions a regular part of your daily life. Following these tips can help you feel more fulfilled and bring the true success and happiness that comes from freedom and fulfillment.
This document provides an overview of basic statistics. It defines statistics as starting with a question rather than data, and explains that statistics can be used to spot trends and identify causes. The document notes that modern statistics developed around 1893 and is used to analyze data where there is no theoretical probability. It acknowledges that statistics can be misleading if presented selectively but also states that statistics itself does not lie - it is the interpretation that can be distorted. The document provides homework assignments for students to further their understanding of key statistical concepts like mean, median, mode, and p-values through a video and written explanations.
Trusting the data and understanding variation are two key insights from learning to think like a data scientist. Managers who lack data skills are at a disadvantage when interacting with data scientists and making decisions. Companies also need more data-savvy managers to avoid being disadvantaged compared to their competitors.
5 Key Traits of High Performing Marketing Organizations Mathew Sweezey
We've researched over 7,000 companies over the past two years, and have found the 5 key traits to high performing marketing organizations. See how you rank, and or what you need to do to become a high performer.
This document discusses why statistics are fascinating and useful, even for those without a strong math background. It argues that statistics can reveal the disconnect between people's perceptions and reality. Simplicity is key when presenting statistics, which should use clear language and visuals to communicate insights effectively. These insights from statistics are relevant for managers in India to make better decisions by understanding perceptions versus facts.
Questions Your Business Information Provider Doesn't Want You To AskCompany Watch
This document discusses questions businesses should ask their business information providers about credit ratings. It notes that companies receiving different ratings from different providers, and that over the next few years insolvency rates are expected to rise. It suggests businesses need reliable rating tools they can trust to help manage credit risk, and lists six key questions to ask providers about the accuracy, stability, and customization of their rating systems.
The document summarizes issues with how many marketers and managers utilize big data. It finds that the vast majority of marketers at Fortune 1000 companies still rely too heavily on intuition rather than data. This is a problem because the world is moving faster than these managers' experiences from decades ago, and data is needed to understand new industries, concepts, and a changing consumer landscape. Specifically, over-reliance on past experiences can increase errors, outdated views do not apply to startups or new technologies, and managers require updated data to understand evolving consumer behaviors. Additionally, even data-savvy managers may misinterpret data by assuming small differences are meaningful or using deceptive graphs, indicating a careless approach that hurts decision-making and
PitchSkills Business Presentation Template - Clean & PositiePitchSkills
The document provides three small tips to help the reader do what they love everyday. It recommends including hobbies and interests in your to-do list and schedule, reading articles to stay updated on topics you enjoy, and making these passions a regular part of your daily life. Following these tips can help you feel more fulfilled and bring the true success and happiness that comes from freedom and fulfillment.
This document provides an overview of basic statistics. It defines statistics as starting with a question rather than data, and explains that statistics can be used to spot trends and identify causes. The document notes that modern statistics developed around 1893 and is used to analyze data where there is no theoretical probability. It acknowledges that statistics can be misleading if presented selectively but also states that statistics itself does not lie - it is the interpretation that can be distorted. The document provides homework assignments for students to further their understanding of key statistical concepts like mean, median, mode, and p-values through a video and written explanations.
Trusting the data and understanding variation are two key insights from learning to think like a data scientist. Managers who lack data skills are at a disadvantage when interacting with data scientists and making decisions. Companies also need more data-savvy managers to avoid being disadvantaged compared to their competitors.
5 Key Traits of High Performing Marketing Organizations Mathew Sweezey
We've researched over 7,000 companies over the past two years, and have found the 5 key traits to high performing marketing organizations. See how you rank, and or what you need to do to become a high performer.
This document discusses why statistics are fascinating and useful, even for those without a strong math background. It argues that statistics can reveal the disconnect between people's perceptions and reality. Simplicity is key when presenting statistics, which should use clear language and visuals to communicate insights effectively. These insights from statistics are relevant for managers in India to make better decisions by understanding perceptions versus facts.
Questions Your Business Information Provider Doesn't Want You To AskCompany Watch
This document discusses questions businesses should ask their business information providers about credit ratings. It notes that companies receiving different ratings from different providers, and that over the next few years insolvency rates are expected to rise. It suggests businesses need reliable rating tools they can trust to help manage credit risk, and lists six key questions to ask providers about the accuracy, stability, and customization of their rating systems.
Alan Smith explores the mismatch between people's perception of their ability to understand statistics and the reality. While many think they have strong statistical skills, people are actually quite poor at intuitive statistics due to factors like individual experiences and media focusing on exceptions. This disconnect is important for managers to understand when making decisions based on consumer data and surveys in diverse markets like India.
Analysis of 3 ways to spot a bad statistic by mona chalabiDarpan Deoghare
This document summarizes Mona Chalabi's analysis of how to spot bad statistics. It outlines 3 key questions to ask: 1) Can we see uncertainty in the numbers? Visualizing data can overstate certainty. 2) Can we see ourselves in the data? Context is important to understand where data points fit. 3) How was the data collected? It's important to understand the methodology to properly interpret results. Bad statistics can mislead; we shouldn't dismiss numbers but should learn to scrutinize how they were produced and what uncertainties exist. Proper statistical analysis is important for effective policymaking and decision making.
A clash is emerging in British marketing between creative and data-focused professionals. The author argues both can learn from each other to become more well-rounded marketers. Creatives can learn that numbers can be beautiful, assumptions are often wrong, and outcomes are unpredictable. Data professionals can learn that marketing is about people, imagery is important for communication, and engaging both the head and heart is needed. The author advocates for finding "data artists" who combine imagination, creativity and empathy with technical data skills.
This document summarizes a TED talk about spotting bad statistics. It outlines three questions to ask: 1) Can you see uncertainty in the data? Visualizations often overstate certainty. 2) Can I see myself in the data? National statistics may not match personal experiences. 3) How was the data collected? The method of data collection impacts the results. Government data is generally more reliable than private companies' data. Managers should maintain teams that consider data uncertainty and ensure surveys target all affected people.
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Insights of “why you should love statistics” –ted talk by alan smith by darpa...Darpan Deoghare
The document summarizes a Ted Talk by Alan Smith about why people should love statistics. It discusses how statistics is the science of dealing with data about communities and how individuals relate to groups. It provides insights such as people often perceive things differently than reality and numbers can inspire and surprise. It also discusses how statistics can help businesses make better decisions, produce goods more efficiently, and support judgment. The conclusion is that statistics is really the science of understanding people and why we should be fascinated by numbers.
The speaker summarizes Alan Smith's TED talk about why statistics are important.
1) Statistics are about understanding groups and populations, not just individuals. It reveals how individuals relate to larger communities.
2) There is often a big difference between people's perceptions and statistical realities. Several surveys showed people vastly over or underestimating statistics about populations.
3) Even statistics experts performed poorly when asked to estimate statistics about their own cities, showing perceptions can be misleading compared to data. Managers need statistical insights and data-driven analysis to make accurate decisions.
This document discusses the field of statistics and its applications. Statistics is the science of uncertainty and drawing inferences from numerical data using probability. It is useful across many fields as it allows conclusions to be drawn from prior observations. There is often a difference between perception and reality when it comes to statistical measurement, and the source of information is crucial. Businesses use statistics to their advantage in areas like product design and marketing, advertising, and analyzing competitors. The proper collection and analysis of data is important for drawing accurate statistical conclusions.
Analysis of “what do you do with all this big data” –ted talk by susan etlingerDarpan Deoghare
The document summarizes key points from a Ted Talk about managing big data. It notes that big data comes from many sources like social media, smartphones, and online activities. While big data can provide insights, it also needs to be interpreted carefully to avoid misinterpretations. Managers need to focus on critical thinking when analyzing big data and consider factors beyond just facts and figures to avoid misleading conclusions. Proper analysis and communication is needed to ensure insights are derived while maintaining public trust in how data is used and interpreted.
Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.
This document discusses perspectives on statistics from both pro-stats and anti-stats viewpoints. Pro-stats advocates see statistics as objective measures of societal progress, while anti-stats are skeptical of statistics being elitist or not reflecting everyday experiences. It also outlines key points to consider when looking at statistics, such as understanding uncertainty in data, seeing yourself represented, how data is collected, the importance of sample size for government data, and being wary of manipulated visualizations.
Digitas Health LifeBrands took a trip to The Lone Star state and immersed ourselves in all things South by Southwest (SxSW).
The days went by fast and furious as we were pulled into speed sessions, meet-ups, brainstorms, demonstrations, hack-a-thons, pitches, accelerators, and a myriad of other Austin-style opportunities.
The next few slides are our attempt to bring some of these learnings home with an emphasis on why the message is relevant to healthcare marketers. Enjoy!
Statistics is the practice of collecting and analyzing numerical data, especially to infer characteristics of a whole population based on a representative sample. It is useful for organizing and understanding data in daily life and society. While people may have strong perceptions, statistics provide a reality check, as there can be a big difference between what we think and what the measurable data shows. For managers in India, it is important to make decisions based on statistical analysis rather than relying solely on perceptions, as statistics can provide insights relevant for understanding issues.
This document discusses statistics and perceptions. It notes that two statisticians, Mr. Pullinger and Mr. Paxman, showed that being good does not mean having sufficient understanding. It also discusses how personal experiences and external influences shape individual perceptions of reality, which may differ from actual statistics. The document recommends that managers find smarter, interactive ways to promote effective data collection and analysis, and that gamifying data can help obtain more accurate information by playing on preconceptions.
Insights of why you should love statistics by sheshaShesha
This document summarizes a TED talk about why statistics should be loved. It discusses two main insights from the talk: 1) there is often a disconnect between people's perceptions and reality, and statistics can help provide a clearer picture of reality, and 2) this century requires becoming more comfortable with numbers as everything needs to be recorded and understood through data. It argues that statistics is important for managers to make better decisions by reducing errors and bias from relying solely on intuition or perception.
Analysis : Why you should love statistics by Alan SmithShloka Srivastava
This document discusses key points from Alan Smith's TEDx talk on statistics. It questions whether we truly know things with certainty or rely too much on heuristics and biases. It notes that over half the population struggles with basic math, so estimates should be based on analysis rather than gut feelings. The document also emphasizes that as managers in India, it is important to understand diversity across knowledge bases, provide information people can understand tailored to their location, and build upon past data and insights to create impact.
This document summarizes an article that is critical of the term "Big Data" and argues that it is primarily a marketing term used by business intelligence vendors. Some key points:
1) The author argues that "Big Data" is just the latest marketing campaign by BI vendors and does not represent a meaningful change, as data has always been large and growing exponentially.
2) While vendors tout new sources of data and increased volumes, the author claims this is just "more of the same" and does not require fundamentally new approaches. Greater data does not necessarily lead to better insights or decisions.
3) Quotes and claims by vendors about the potential value and benefits of "Big Data" are exaggerated and
The document discusses the limitations of relying too heavily on quantitative analysis and spreadsheets for strategy development. It argues that important qualitative factors cannot be measured and are often overlooked. While data is important, human and organizational aspects like emotion, motivation and culture are also crucial but difficult to capture numerically. An over-emphasis on metrics can reduce understanding of complex problems and lead to misguided strategies. A balanced approach considering both quantitative and qualitative elements is needed.
Where Data and Story Meet - Building the Data Storytelling CapabilityRanda McMinn
Data is rapidly transforming the way companies are transacting and engaging with customers. Gone are the days of not having enough data, now we are being inundated with too much data and are struggling to find ways to make sense of it. As a business leader, especially in the roles of data science and marketing, your success is heavily reliant on making sense of data, so it is becoming imperative to build and nurture a great data storytelling capability.
In this piece, we explore the increasing demands in skillsets for the modern data scientist and marketer. Further, we explore the mindset of data scientists and whether or not that mindset differs from a group of analytics professionals who have been identified as great data storytellers. We also reveal different ways to build the data storytelling capability.
This document discusses the need for strong data storytelling skills among data scientists and marketers. It argues that while data scientists are often hired to make sense of large amounts of data, few possess the storytelling skills needed to communicate insights to others. Similarly, modern marketers need a blend of analytical and creative skills that many lack. The document explores how to define data storytelling and profiles the ideal skills of modern data scientists and marketers. It identifies three types of data scientists - those focused on analysis, building tools, and consulting/storytelling. It concludes that organizations need to foster interdisciplinary collaboration and build internal storytelling capabilities to bridge the gap between data and action.
This presentation reports the statistical analysis of TED Talks. It reports various parameters which define the success of a TED Talk and similary failure of a TED Talk. It is the summary of the TED Talk "Lies, Damned Lies and Statistics" by Sebastian Wernicke
Alan Smith explores the mismatch between people's perception of their ability to understand statistics and the reality. While many think they have strong statistical skills, people are actually quite poor at intuitive statistics due to factors like individual experiences and media focusing on exceptions. This disconnect is important for managers to understand when making decisions based on consumer data and surveys in diverse markets like India.
Analysis of 3 ways to spot a bad statistic by mona chalabiDarpan Deoghare
This document summarizes Mona Chalabi's analysis of how to spot bad statistics. It outlines 3 key questions to ask: 1) Can we see uncertainty in the numbers? Visualizing data can overstate certainty. 2) Can we see ourselves in the data? Context is important to understand where data points fit. 3) How was the data collected? It's important to understand the methodology to properly interpret results. Bad statistics can mislead; we shouldn't dismiss numbers but should learn to scrutinize how they were produced and what uncertainties exist. Proper statistical analysis is important for effective policymaking and decision making.
A clash is emerging in British marketing between creative and data-focused professionals. The author argues both can learn from each other to become more well-rounded marketers. Creatives can learn that numbers can be beautiful, assumptions are often wrong, and outcomes are unpredictable. Data professionals can learn that marketing is about people, imagery is important for communication, and engaging both the head and heart is needed. The author advocates for finding "data artists" who combine imagination, creativity and empathy with technical data skills.
This document summarizes a TED talk about spotting bad statistics. It outlines three questions to ask: 1) Can you see uncertainty in the data? Visualizations often overstate certainty. 2) Can I see myself in the data? National statistics may not match personal experiences. 3) How was the data collected? The method of data collection impacts the results. Government data is generally more reliable than private companies' data. Managers should maintain teams that consider data uncertainty and ensure surveys target all affected people.
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Insights of “why you should love statistics” –ted talk by alan smith by darpa...Darpan Deoghare
The document summarizes a Ted Talk by Alan Smith about why people should love statistics. It discusses how statistics is the science of dealing with data about communities and how individuals relate to groups. It provides insights such as people often perceive things differently than reality and numbers can inspire and surprise. It also discusses how statistics can help businesses make better decisions, produce goods more efficiently, and support judgment. The conclusion is that statistics is really the science of understanding people and why we should be fascinated by numbers.
The speaker summarizes Alan Smith's TED talk about why statistics are important.
1) Statistics are about understanding groups and populations, not just individuals. It reveals how individuals relate to larger communities.
2) There is often a big difference between people's perceptions and statistical realities. Several surveys showed people vastly over or underestimating statistics about populations.
3) Even statistics experts performed poorly when asked to estimate statistics about their own cities, showing perceptions can be misleading compared to data. Managers need statistical insights and data-driven analysis to make accurate decisions.
This document discusses the field of statistics and its applications. Statistics is the science of uncertainty and drawing inferences from numerical data using probability. It is useful across many fields as it allows conclusions to be drawn from prior observations. There is often a difference between perception and reality when it comes to statistical measurement, and the source of information is crucial. Businesses use statistics to their advantage in areas like product design and marketing, advertising, and analyzing competitors. The proper collection and analysis of data is important for drawing accurate statistical conclusions.
Analysis of “what do you do with all this big data” –ted talk by susan etlingerDarpan Deoghare
The document summarizes key points from a Ted Talk about managing big data. It notes that big data comes from many sources like social media, smartphones, and online activities. While big data can provide insights, it also needs to be interpreted carefully to avoid misinterpretations. Managers need to focus on critical thinking when analyzing big data and consider factors beyond just facts and figures to avoid misleading conclusions. Proper analysis and communication is needed to ensure insights are derived while maintaining public trust in how data is used and interpreted.
Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.
This document discusses perspectives on statistics from both pro-stats and anti-stats viewpoints. Pro-stats advocates see statistics as objective measures of societal progress, while anti-stats are skeptical of statistics being elitist or not reflecting everyday experiences. It also outlines key points to consider when looking at statistics, such as understanding uncertainty in data, seeing yourself represented, how data is collected, the importance of sample size for government data, and being wary of manipulated visualizations.
Digitas Health LifeBrands took a trip to The Lone Star state and immersed ourselves in all things South by Southwest (SxSW).
The days went by fast and furious as we were pulled into speed sessions, meet-ups, brainstorms, demonstrations, hack-a-thons, pitches, accelerators, and a myriad of other Austin-style opportunities.
The next few slides are our attempt to bring some of these learnings home with an emphasis on why the message is relevant to healthcare marketers. Enjoy!
Statistics is the practice of collecting and analyzing numerical data, especially to infer characteristics of a whole population based on a representative sample. It is useful for organizing and understanding data in daily life and society. While people may have strong perceptions, statistics provide a reality check, as there can be a big difference between what we think and what the measurable data shows. For managers in India, it is important to make decisions based on statistical analysis rather than relying solely on perceptions, as statistics can provide insights relevant for understanding issues.
This document discusses statistics and perceptions. It notes that two statisticians, Mr. Pullinger and Mr. Paxman, showed that being good does not mean having sufficient understanding. It also discusses how personal experiences and external influences shape individual perceptions of reality, which may differ from actual statistics. The document recommends that managers find smarter, interactive ways to promote effective data collection and analysis, and that gamifying data can help obtain more accurate information by playing on preconceptions.
Insights of why you should love statistics by sheshaShesha
This document summarizes a TED talk about why statistics should be loved. It discusses two main insights from the talk: 1) there is often a disconnect between people's perceptions and reality, and statistics can help provide a clearer picture of reality, and 2) this century requires becoming more comfortable with numbers as everything needs to be recorded and understood through data. It argues that statistics is important for managers to make better decisions by reducing errors and bias from relying solely on intuition or perception.
Analysis : Why you should love statistics by Alan SmithShloka Srivastava
This document discusses key points from Alan Smith's TEDx talk on statistics. It questions whether we truly know things with certainty or rely too much on heuristics and biases. It notes that over half the population struggles with basic math, so estimates should be based on analysis rather than gut feelings. The document also emphasizes that as managers in India, it is important to understand diversity across knowledge bases, provide information people can understand tailored to their location, and build upon past data and insights to create impact.
This document summarizes an article that is critical of the term "Big Data" and argues that it is primarily a marketing term used by business intelligence vendors. Some key points:
1) The author argues that "Big Data" is just the latest marketing campaign by BI vendors and does not represent a meaningful change, as data has always been large and growing exponentially.
2) While vendors tout new sources of data and increased volumes, the author claims this is just "more of the same" and does not require fundamentally new approaches. Greater data does not necessarily lead to better insights or decisions.
3) Quotes and claims by vendors about the potential value and benefits of "Big Data" are exaggerated and
The document discusses the limitations of relying too heavily on quantitative analysis and spreadsheets for strategy development. It argues that important qualitative factors cannot be measured and are often overlooked. While data is important, human and organizational aspects like emotion, motivation and culture are also crucial but difficult to capture numerically. An over-emphasis on metrics can reduce understanding of complex problems and lead to misguided strategies. A balanced approach considering both quantitative and qualitative elements is needed.
Where Data and Story Meet - Building the Data Storytelling CapabilityRanda McMinn
Data is rapidly transforming the way companies are transacting and engaging with customers. Gone are the days of not having enough data, now we are being inundated with too much data and are struggling to find ways to make sense of it. As a business leader, especially in the roles of data science and marketing, your success is heavily reliant on making sense of data, so it is becoming imperative to build and nurture a great data storytelling capability.
In this piece, we explore the increasing demands in skillsets for the modern data scientist and marketer. Further, we explore the mindset of data scientists and whether or not that mindset differs from a group of analytics professionals who have been identified as great data storytellers. We also reveal different ways to build the data storytelling capability.
This document discusses the need for strong data storytelling skills among data scientists and marketers. It argues that while data scientists are often hired to make sense of large amounts of data, few possess the storytelling skills needed to communicate insights to others. Similarly, modern marketers need a blend of analytical and creative skills that many lack. The document explores how to define data storytelling and profiles the ideal skills of modern data scientists and marketers. It identifies three types of data scientists - those focused on analysis, building tools, and consulting/storytelling. It concludes that organizations need to foster interdisciplinary collaboration and build internal storytelling capabilities to bridge the gap between data and action.
This presentation reports the statistical analysis of TED Talks. It reports various parameters which define the success of a TED Talk and similary failure of a TED Talk. It is the summary of the TED Talk "Lies, Damned Lies and Statistics" by Sebastian Wernicke
This presentation analyzes the HBR Article on "Big Data Hype (and Reality)" by Gregory Piatetsky-Shapiro. It emphasizes on the slow improvement of the technology, but in the end provides the areas where big data is useful.
This presentation analyzes the TED Talk by Sebastian Wernicke
on "How to use data to make a hit tv show". It analyzes the importance of logic in decision making rather than purely depending on data.
The document discusses challenges with hiring data scientists and suggests alternative approaches. It recommends empowering small cross-functional data-oriented teams explicitly tasked with delivering measurable business benefits. This builds internal data capabilities rather than just hiring expertise. It also stresses the importance of making data science a cultural value throughout the organization so that all employees understand basic principles and practices of data science.
This document discusses how to spot bad statistics. It provides three questions to ask: 1) Can you see uncertainty in the data? Many visualizations overstate certainty. 2) Can I see myself in the data? Data needs context about how it relates to people's lives. 3) How was the data collected? It's important to understand how surveys and studies were conducted. Bad statistics can mislead decision making, so it's crucial to evaluate data collection methods and understand limitations to get full context. Statistics are still important for policymaking, but they must be questioned and interpreted carefully.
The presentation analyzes the HBR article "A Predictive Analytics Primer" by Tom Davenport. It gathers insights on How can we predict better, with better assumptions.
Hans Rosling gives a TED talk debunking myths about developing countries with compelling statistics. Thomas Davenport argues that data is useless without good communication. Hans Rosling advocates making publicly available data searchable and visualized to improve understanding. A manager learns that companies should transform database data into logical infographics while protecting confidential information, and encourage using data insights. Better visualizations of available information can improve decision making.
The document discusses the importance of communicating data effectively. It notes that there is a pressing need for more businesspeople who can make decisions based on data analysis. While it is not necessary for managers to crunch numbers themselves, they must be able to communicate quantitative insights to diverse audiences. Effective communication involves understanding the audience, presenting the appropriate level of detail, and focusing on implications rather than just results. When data is communicated well, it can help organizations make better decisions.
The document discusses how data-driven companies are more profitable and provides insights into becoming data-driven. It recommends making decisions throughout the organization to free up senior time. It also stresses investing in quality data sources that others can trust to align with decisions. Managers should push decision making down, invest in quality data, and bring new data technologies into their organizations to reap the profit benefits of a data-driven approach.
Jer Thorp is a data artist who adds humanity to technology by discovering relationships on the internet and building narratives from pieces of information. He argues that numbers represent real world things and are inherently human. His insights are that data gains meaning when put in a human context by bringing the human element into stories, which builds empathy and respect missing from technology. There is a need for more inclusion of artists, poets and writers to highlight humanity in data science.
The document discusses the challenges of drawing insights from big data. It notes that interpreting big data requires critical thinking to understand human expression and account for uncertainty. Managers can better understand data by asking focused questions, considering language and cultural differences, and using multiple disciplines like linguistics and ethics. While big data offers opportunities, organizations must thoughtfully source, analyze, and communicate data to earn and maintain public trust.
The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
3. Whether we consider
ourselves math people or
not, our ability to
understand and work with
numbers is terribly limited,
says Data visualization
expert Alan Smith.
4. Alan Smith at TEDxExeter
In this delightful talk,
Smith explores the
mismatch between
what we know and
what we think we
know.
6. Clearly, there's a numeracy iss
Alan Smith states numerous
examples to convey that,
most people lack numeracy
skills, especially when it
comes to statistical
guessing.
7. Nearly 40 percent of
young people in the
US have low
numeracy. There are
seven OECD
countries with figures
above 20 percent. So
there's definitely a
numeracy problem
that we want to
address.
8. Daniel Kahneman, for
example, the Nobel-winning
economist and his colleague,
Amos Tversky, spent years
researching this
disjoint between what people
perceive and the reality, the
fact that people are
actually pretty poor
intuitive statisticians.
10. Statistics is the science of
dealing with data about the
state or the community
that we live in. So
statistics are about us as a
group, not us as
individuals.
11. So very often, we talk
about statistics as
being the science of
uncertainty. Alan Smith
provides a contrast by
stating that Statistics
is the science of
us. And that's why
we should be
fascinated by
numbers.
13. It’s very vivid from
the discussion that
there is a strict need
to incorporate
“Statistical Literacy”
among the people,
especially the
students and the
corporate working
professionals.
14. The first insight reveals
that there is huge
difference between
people’s perception and
the reality, when it comes
to statistical guessing.
If a manager could
possibly understand the
depth of the insight,
he/she could work out
wonders for the
company.
15. How many times during a
business, we come across
situations where we need to guess
a rough estimate for the sales.
All further strategies are decided
upon the statistical prediction. A
manager should realize that it is
high time to equip himself/herself
and the employees with the
required statistical knowledge and
numeral literacy.
16. The second insight talks
about how Statistics could
lead to better understanding
of our peer group.
A manager needs to very
aware about the employees,
their desires and their
needs.
And if the manager could
maintain this useful data,
he/she could predict the
behavior of the company in
the upcoming years, and
thus take the appropriate
action.