This is the original keynote file for my talk at the Smart Disclosure Summit in Washington DC on March 30, 2012. I will upload a PDF with notes separately.
Become an Exponential Organization, Change the World FasterGary A. Bolles
A talk to the NetHope annual summit, an amazing group of 50+ non-profit organizations from around the world. As I told the attendees, I should have titled this, "Become an Exponential Organization, Help the World Change the World Faster" - how you can help affected populations to help themselves.
This is the original keynote file for my talk at the Smart Disclosure Summit in Washington DC on March 30, 2012. I will upload a PDF with notes separately.
Become an Exponential Organization, Change the World FasterGary A. Bolles
A talk to the NetHope annual summit, an amazing group of 50+ non-profit organizations from around the world. As I told the attendees, I should have titled this, "Become an Exponential Organization, Help the World Change the World Faster" - how you can help affected populations to help themselves.
My final project I wanted to mix a bunch of our current technologies into a new way using the theories we have discussed during the semester. I didn’t want to make the new technologies too high tech, because we need to think 10 years isn’t that long. How far have we come in 10 years? So I stuck to technologies that we have today, but added realistic traits to each. After I created these new technologies I added the theories in and how they relate to each advancement.
Dell Solutions Tour 2015- Open Stack Cloud: How UH-SKY have approached gettin...Kenneth de Brucq
Hear directly from UH-SKY about how they have designed and are delivering the Norway Academic cloud right now. We will discuss how you can also embrace the cloud whilst being sure that this is right for your organization. We will cover how other Dell customer are using cloud, developing their own solutions managing and securing their cloud services. We will provide insights as to how you can help your organization benefit best from the Cloud.
As of 2014 this presentation tries to find Google’s organizational changes using ex-post analyzes to answer the research question, “Does Consistency Matter?”. After elaborating historical findings this presentation classifies and links facts to organizational changes from startup to innovative culture. To focus the topic it does not include any topics related to industry, financial information, products, law suits, privacy related issues, political decisions and philanthropy. Each transformation is presented with a key event and accordingly influential people are analyzed under those events.
Learning for digital natives connected to life! Kingdom of Bhutan session Jun...Lukas Ritzel
Learning for digital natives connected to life! Kingdom of Bhutan session June 2014. A wake up call for acacemics for by Lukas Ritzel an honorary member of All India Association for Educational Research ( aiaer.net/ )
BT On The Productivity Puzzle in CollaborationLeon Benjamin
Leon Benjamin, Sei Mani's co-founder contributes to its strategic partner BT' and its perspective on the value of collaboration in the enterprise.
As a concept, mobile and flexible working is nothing new and the idea of where people work has widened to pretty much anywhere. The issue is no longer ‘where’ people work, the question we’re now asking is ‘how’ people work.
MINDFUL TECHNOLOGY // We’ve all been witness to both the delight and the disappointment that can happen when we let technology into the most personal parts of our lives. It can sometimes even seem that we serve the machines more than they serve us. (Are you listening, Alexa?!) There’s no doubt that ever-present technology has improved our lives, given us superpowers, and made us more efficient. But at what cost?
With our addictive apps, sticky widgets, and blindly engaging interactions, we’ve created an era of distraction. Novelty and disruption trump human connection—and these days, even factual truth. We can do better.
As the bits and bytes settle into the most intimate spaces of our lives, our homes, and even our bodies, designers have new responsibilities and obligations. Author and strategist Liza Kindred’s 20-year career in fashion and technology has explored both the challenges and benefits of (literally) weaving tech into our lives. Here she offers a host of practical examples that illustrate an eye-opening framework of design principles to guide us in how we make and use new technology. Learn how to create real insight, joy, and utility while still getting the job done.
Instead of designing for page views, it’s time to design for purpose, for calm, and for compassion. Instead of designing for engagement with interfaces, let’s design for actual engagement with the people and places we love. Instead of simply building better tech, in other words, let’s build for better human connection.
The term "Digital Native" is used frequently these day, but is it really accurate? Commonly, the digital native is someone born after 1980, someone who has lived in a digital world there entire life. But does that translate to digital literacy? Is the digital immigrant any less skilled at using technology? Digital literacy is about learning what's necessary to be able to use technology tools with wisdom and skill, and that can be done at any age. Even the digital native needs to be taught these skills. There are varying degrees of digital involvement, and not all young people are involved with technology in a variety of ways. Some use a limited number of tools and have limited skills, even if they are using the tools. Schools need to take on the responsibility of teaching kids how to use digital tools and technology effectively and wisely, because technology affects us all. It's here to stay, so it's time to focus on digital literacy.
My final project I wanted to mix a bunch of our current technologies into a new way using the theories we have discussed during the semester. I didn’t want to make the new technologies too high tech, because we need to think 10 years isn’t that long. How far have we come in 10 years? So I stuck to technologies that we have today, but added realistic traits to each. After I created these new technologies I added the theories in and how they relate to each advancement.
Dell Solutions Tour 2015- Open Stack Cloud: How UH-SKY have approached gettin...Kenneth de Brucq
Hear directly from UH-SKY about how they have designed and are delivering the Norway Academic cloud right now. We will discuss how you can also embrace the cloud whilst being sure that this is right for your organization. We will cover how other Dell customer are using cloud, developing their own solutions managing and securing their cloud services. We will provide insights as to how you can help your organization benefit best from the Cloud.
As of 2014 this presentation tries to find Google’s organizational changes using ex-post analyzes to answer the research question, “Does Consistency Matter?”. After elaborating historical findings this presentation classifies and links facts to organizational changes from startup to innovative culture. To focus the topic it does not include any topics related to industry, financial information, products, law suits, privacy related issues, political decisions and philanthropy. Each transformation is presented with a key event and accordingly influential people are analyzed under those events.
Learning for digital natives connected to life! Kingdom of Bhutan session Jun...Lukas Ritzel
Learning for digital natives connected to life! Kingdom of Bhutan session June 2014. A wake up call for acacemics for by Lukas Ritzel an honorary member of All India Association for Educational Research ( aiaer.net/ )
BT On The Productivity Puzzle in CollaborationLeon Benjamin
Leon Benjamin, Sei Mani's co-founder contributes to its strategic partner BT' and its perspective on the value of collaboration in the enterprise.
As a concept, mobile and flexible working is nothing new and the idea of where people work has widened to pretty much anywhere. The issue is no longer ‘where’ people work, the question we’re now asking is ‘how’ people work.
MINDFUL TECHNOLOGY // We’ve all been witness to both the delight and the disappointment that can happen when we let technology into the most personal parts of our lives. It can sometimes even seem that we serve the machines more than they serve us. (Are you listening, Alexa?!) There’s no doubt that ever-present technology has improved our lives, given us superpowers, and made us more efficient. But at what cost?
With our addictive apps, sticky widgets, and blindly engaging interactions, we’ve created an era of distraction. Novelty and disruption trump human connection—and these days, even factual truth. We can do better.
As the bits and bytes settle into the most intimate spaces of our lives, our homes, and even our bodies, designers have new responsibilities and obligations. Author and strategist Liza Kindred’s 20-year career in fashion and technology has explored both the challenges and benefits of (literally) weaving tech into our lives. Here she offers a host of practical examples that illustrate an eye-opening framework of design principles to guide us in how we make and use new technology. Learn how to create real insight, joy, and utility while still getting the job done.
Instead of designing for page views, it’s time to design for purpose, for calm, and for compassion. Instead of designing for engagement with interfaces, let’s design for actual engagement with the people and places we love. Instead of simply building better tech, in other words, let’s build for better human connection.
The term "Digital Native" is used frequently these day, but is it really accurate? Commonly, the digital native is someone born after 1980, someone who has lived in a digital world there entire life. But does that translate to digital literacy? Is the digital immigrant any less skilled at using technology? Digital literacy is about learning what's necessary to be able to use technology tools with wisdom and skill, and that can be done at any age. Even the digital native needs to be taught these skills. There are varying degrees of digital involvement, and not all young people are involved with technology in a variety of ways. Some use a limited number of tools and have limited skills, even if they are using the tools. Schools need to take on the responsibility of teaching kids how to use digital tools and technology effectively and wisely, because technology affects us all. It's here to stay, so it's time to focus on digital literacy.
In this presentation, you will discover how you can begin to leverage on the power and potential of Machine Learning as a technology tool and as a framework for growth.
IT Consumerization – iPad’ing the Enterprise or BYO Malware?Barry Caplin
Companies are increasingly encouraging employees to purchase their own devices such as smartphones, tablets and laptops to use at work according to a recent survey by CIO magazine. The acronyms BYOC and BYOD (like Bring Your Own Beer - Bring Your Own Computer/Device) have become mainstream technology terms. But what does BYOD mean for the enterprise? Can we mix personally owned devices and enterprise workstations/cellphones in our environment? How do we control configuration and data on personal devices? What about malware and other security concerns? What about improper disclosure of private data and intellectual property? And how will staff get work done when they are busy playing Angry Birds?
Is BYOD the flavor of the week or is the future of end-user hardware? Regardless of how security leaders may feel about the concept, we need to be prepared. We must understand what is driving BYOD, how it may, or may not, fit our environments, and have policy and tools ready.
In this interactive session we will discuss: What is IT Consumerization/BYOD? What are the benefits and concerns? Is there a cost savings? What are the Security concerns - BYOMalware? How do we protect data? And how can I start BYOD in my organization?
And yes, you can Bring Your Own Devices to this session!
Secure360 05-13-2013.
Chapters 1 and Chapters 8 (on mobile analytics) of this book that is still "in progress" ... I was in the middle of writing this book when Open Marketing (my agency) got acquired by Bislr / Autopilot
The simplest definition of Big Data is large and complex unstructured data (images posted on Facebook, email, text messages, GPS signals from mobile phones, tweets, and other social media updates…etc.) that cannot be processed by traditional database tools.
eMarketer Webinar: Perspectives on Digital Privacy—Marketers, Consumers, FedseMarketer
Join eMarketer Principal Analyst David Hallerman as he helps companies involved in the digital ad space figure out the best questions to ask and next steps to take to address digital privacy.
Inclusive Design: Designing with all the genders in mind - Anat Katz-Arotchas...nois3
Women represent half the users and control almost 80% of the consumer market, and yet products are often blind to gender differences, perpetuate gender biases, and fail to provide a balanced solution to women’s and men’s needs and preferences. How can we as designers change that? How can we overcome our own unconscious biases and make our products more gender inclusive? And how would it impact the growth of our products and the society we live in? An inspiring session which includes research based methodology and use cases that will open your mind to the potential and practicalities of creating gender inclusive products.
::: Anat Katz-Arotchas :::
Anat Katz-Arotchas is a pioneer in the field of Gendered Product Innovation, promoting gender thinking in tech. Bringing together her expertise in product and gender, Anat is the founder of Standpoint, a former member of the European Union’s gender innovation team and a local representative of the Danish UX firm- Design-people. Anat is also the co-founder of XX-UX Israel, a community of women promoting gender balance in the UX field.
Our Guide to Digital disruption Update 2019John Ashcroft
A collection of our articles on Digital Disruption and Change Management updated for 2019.
Don't thumb your nose at Digital Disruption
So what do we mean by digital disruption
The six forces shaping digital disruption
Digital Disruption Industries of the future
Which jobs will be at risk in the years ahead
Digital Disruption and the UK Banking System
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
AI - How Artificial Intelligence Will Impact Your BusinessPaul Barter
AI - How Artificial Intelligence Will Impact Your Business
DESCRIPTION:
AI (Artificial Intelligence) has the potential to radically transform employment, productivity and society. Business decision makers need to mitigate underlying risks and invest appropriately to drive future competitive advantage.
Similar to The big-data revolution in healthcare (20)
Analysis of ted talk, "Lies, Damned Lies and Statistics" by Sebastian WernickeVaibhav Srivastav
This presentation gives brief analysis of the TED talk, "Lies, Damned Lies and Statistics" by Sebastian Wernicke.
This gives relevant insights from the talk about certain key points that are needed to be kept in mind while forming a certain speech.
Analysis of ted talk, "3 ways to spot a bad statistics" by Mona ChabaliVaibhav Srivastav
This presentation does analysis of the ted talk, "3 ways to spot a bad statistics" by Mona Chabali.
It goes through key insights mentioned by her during her talk.
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Slide 1: Beauty of Data Visualization Intro
Slide 2: The Billion Dollar O-gram
Slide 3: World's Fear Landscape overtime in media
Slide 4: Explanation of landscape.
Slide 5: Break-up times, as per Facebook status updates.
Slide 6: Attack probability data of countries
Slide 7: "Let dataset, change your mindset." -Hans Rosling
Slide 8: Language of the eye (Pattern) and language of the brain (learn) creates beautiful visualizations.
Slide 9: How could managers use data visualization?
Slide 10: Data Visualization can compile a huge number of databases into few pages.
Slide 11: It will help giving a visual aspect to your data.
Slide 12: It will help in discovering new facts.
Slide 13: Beautiful, Lovely data.
Slide 14: Thank You.
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.Vaibhav Srivastav
The presentation is based on analysis of the ted talk by Jer Thorp on 'Make Data more Human.'
Slide 1: Introduction to the topic
Slide 2: Jer Thorp makes one of the most beautiful data visualizations in the world.
Slide 3: He designed the naming algorithm for the 09/11 Memorial, Ney York city, wherein all are connected near to the person they were connected with.
Slide 4: He has insisted on giving data human traits, and treat them like one.Following methods:
Slide 5: Data is bigger, doesn't mean its better. It may have lots of errors, redundancy, irrelevancy.
Slide 6: We need to give data a human-like context.
Slide 7: Data can be related to the most human of the attribute, Emotion.
Slide 8: Visualization of data solves the majority of the problems.
Slide 9: Jer Thorp co-founded icascade, a start-up meant to construct a detailed picture of how information propagated through the social media space.
Slide 10: Visualization helps plot huge amounts of complex data, clarifying all the factors.
Slide 11: Benefits of Data treatment
Slide 12: Absorb info. in new and more constructive ways.
Visualize relationships and patterns between operational and business activities.
Identify and act on emerging trends.
Manipulate and interact directly with data.
Foster a new business language.
Slide 13: Have a storytelling data.
Slide 14: openpaths.cc a site to donate your data for research purposes.
Slide 15: Thank You
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...Vaibhav Srivastav
Slide 1: Welcome slide on analysis of the article by Thoman C Redman on 'How to start thinking like a Data Scientist?'
Slide 2: Why, Why do we need to think like Data Scientists?
Slide 3: Because Data are forcing their way into all the industries. Data is the new currency.
Slide 4: Procedure to think like a Data Scientist
Slide 5: Step 1- Define the problem statement to be solved.
Slide 6: Step 2- Think about all the data that can solve your problem.
Slide 7: Step 3- Collect your data using necessary functions and protocols.
Slide 8: Clean your data for missing and irregular files.
Slide 9: Have confidence in the efficiency of your data.
Slide 10: Be wise to your data, Don't get too hard on it.
Slide 11: Visualize your data, Plot the graphs.
Slide 12: Do data analysis.
Slide 13: Check for variations in the data.
Slide 14: Formation of hypothesis based on observation from data.
Slide 15: Test your hypothesis on real-valued function.
Slide 16: Communicate the results of the evaluation.
Slide 17: Don't be data illiterate.
Slide 18: Thank You!
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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.
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
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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."
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
2. igdata?
All of the information that we're
generating through our interaction with
and over the Internet, everything from
Facebook and Twitter to music
downloads, movies, streaming.
3. Biggest problem with Big Data
The biggest
problem is we
have so much
information. The
biggest problem
is: how do we
organize all that
information?
6. Paper form > Digital Storage
Once you've collected all that data, some
person is going to have to type that into
a computer. All the collected tons of
paper data needs to be stored digitally
into computer for faster processing.
8. PalmPilot
In 1995, Joel Selanikio was
stuck with the idea to use Palm
Pilot for data collection.
We can just collect the data
electronically, digitally, from
the very beginning, we can
just put a shortcut right
through that whole process of
typing, of having somebody
type that stuff into the
computer. We could skip
straight to the analysis and
then straight to the use of the
data to actually save lives.
9. Main obstacle of Palm Pilot!
The main obstacle, it turned out
-- and this is a sad realization --
the main obstacle was Joel Selanikio, himself.
He had developed a process
whereby he was the center of the
universe of this technology. If you
wanted to use this technology, you
had to get in touch with him. Any
system that depends on a single
human being or two or three or
five human beings -- it just doesn't
scale.
14. Relevant Insights
# INSiGHT 2
“Improvement at scale”
Determine the opportunity of the new value
pathways, Evaluate a range of health-care
initiatives and assess their potential impact as
total annual cost savings.