The document discusses the nature of data driven service innovation. Some key points made include:
- Data driven service innovation aims to create new services by finding innovative uses of data, but this process is messy and experimental as requirements are poorly defined initially.
- Big data projects resemble research more than production, requiring agility to combine conventional project management with an ability to fail fast and learn from mistakes.
- The complexity of modern ICT systems makes perfect causal understanding impossible. We must acknowledge our ignorance and use a probe-sense-analyze-act approach.
- Developing services is challenging as the customer experience is co-created and hard to define formally. Operational staff closest to customers provide important insights.
“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and AI. “
source : www.lebigdata.fr
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
This report is based on a series of interviews across the breadth of the MoD to probe the ability of the British military to cope with a growing data deluge, and identify potential applications and hurdles to their implementation.
“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and AI. “
source : www.lebigdata.fr
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
This report is based on a series of interviews across the breadth of the MoD to probe the ability of the British military to cope with a growing data deluge, and identify potential applications and hurdles to their implementation.
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Demystifying AI via Top 10 Key Takeaways of "Unscaled" by Hemant Taneja Alec Coughlin
Artificial Intelligence (AI) isn't the easiest technology to understand but "Unscaled" does a phenomenal job demystifying it through the General Catalyst central investment philosophy and the results the VC firm has delivered.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
Ibm cognitive business_strategy_presentationdiannepatricia
IBM Cognitive Business Strategy presentation. Presented by Dianne Fodell and Jim Spohrer at the Cognitive Systems Institute Group Speaker Series call on October 8, 2015.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Listen to an experienced, global panel of insurance professionals present, discuss and answer your questions on the theme of “AI & Machine Learning”.
Brought to you by The Digital Insurer and sponsored by KPMG.
Digital Futures Webinar with Amaze CSO Rick Curtis Jan 2014amazeplc
Amaze's Chief Strategy Officer, Rick Curtis gives his thoughts on what will be hot in digital over the next 24 months and beyond. Rick discussed the following areas:
• The trends that will change the way businesses function and how we live our lives through 2014 and beyond
• What businesses should be thinking about as they compete in the continually evolving technological landscape
• What businesses need to be doing in order to remain competitive in a future being driven by Connectivity, Context and Collaboration.
To view a recording of this webinar, please click here:
https://vimeo.com/amazeplc/review/83694979/18dc31fd96
Web3 And The Next Internet - New Directions And Opportunities For STM PublishingMills Davis
The new ecosystem for scientific, technical, and medical (STM) publishing is digital, trans-semiotic, data and knowledge intensive, social, connected, collaborative, community-driven, mobile, multi-channel, immersive, and massively networked and computational.
In this era of open, co-evolving, networked techno-socio-economic processes, commercial publishing models based on exclusive literature collections are simply not enough.
By understanding changes coming with Web 3.0 and the next internet, STM publishers can identify new roles and profitable business opportunities.
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
Cognitive Business: Where digital business meets digital intelligenceIBM Watson
Ravesh Lala, Vice President, IBM Watson Solutions provided a high level overview of IBM Watson on Monday August 22, 2016 at the Electronics event in NY. Ravesh shared insights into what Watson is, and how organizations have leveraged the power of Watson to advance their place in the market.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Big Data and the Future of Journalism (Futurist Keynote Speaker Gerd Leonhard...Gerd Leonhard
This is a slightly edited version of my slides presented in London on June 7, 2013 and the Reuters Institute see https://reutersinstitute.politics.ox.ac.uk/research/conferences/forthcoming-conferences/big-data-big-ideas-for-media.html
BTW: You can download ALL of my slideshows, free books and other stuff at http://futuristgerd.com/downloads/
"Data stockpiles are growing exponentially...consumer profiles, media content usage patterns, Twitter and Facebook posts, online purchases, public records, real-time media user behavior and much more. The Big Ideas conference speakers will inspire tactics and strategies to harness these data.
The media industry's leading edge experts from journalism and business disciplines will detail their own case studies, outlining their challenges and triumphs using tools to understand complex data sets. They will outline how these experiences have paved the way to prize-winning journalism, audience insights and growing revenues..."
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Demystifying AI via Top 10 Key Takeaways of "Unscaled" by Hemant Taneja Alec Coughlin
Artificial Intelligence (AI) isn't the easiest technology to understand but "Unscaled" does a phenomenal job demystifying it through the General Catalyst central investment philosophy and the results the VC firm has delivered.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
Ibm cognitive business_strategy_presentationdiannepatricia
IBM Cognitive Business Strategy presentation. Presented by Dianne Fodell and Jim Spohrer at the Cognitive Systems Institute Group Speaker Series call on October 8, 2015.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Listen to an experienced, global panel of insurance professionals present, discuss and answer your questions on the theme of “AI & Machine Learning”.
Brought to you by The Digital Insurer and sponsored by KPMG.
Digital Futures Webinar with Amaze CSO Rick Curtis Jan 2014amazeplc
Amaze's Chief Strategy Officer, Rick Curtis gives his thoughts on what will be hot in digital over the next 24 months and beyond. Rick discussed the following areas:
• The trends that will change the way businesses function and how we live our lives through 2014 and beyond
• What businesses should be thinking about as they compete in the continually evolving technological landscape
• What businesses need to be doing in order to remain competitive in a future being driven by Connectivity, Context and Collaboration.
To view a recording of this webinar, please click here:
https://vimeo.com/amazeplc/review/83694979/18dc31fd96
Web3 And The Next Internet - New Directions And Opportunities For STM PublishingMills Davis
The new ecosystem for scientific, technical, and medical (STM) publishing is digital, trans-semiotic, data and knowledge intensive, social, connected, collaborative, community-driven, mobile, multi-channel, immersive, and massively networked and computational.
In this era of open, co-evolving, networked techno-socio-economic processes, commercial publishing models based on exclusive literature collections are simply not enough.
By understanding changes coming with Web 3.0 and the next internet, STM publishers can identify new roles and profitable business opportunities.
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
Cognitive Business: Where digital business meets digital intelligenceIBM Watson
Ravesh Lala, Vice President, IBM Watson Solutions provided a high level overview of IBM Watson on Monday August 22, 2016 at the Electronics event in NY. Ravesh shared insights into what Watson is, and how organizations have leveraged the power of Watson to advance their place in the market.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Big Data and the Future of Journalism (Futurist Keynote Speaker Gerd Leonhard...Gerd Leonhard
This is a slightly edited version of my slides presented in London on June 7, 2013 and the Reuters Institute see https://reutersinstitute.politics.ox.ac.uk/research/conferences/forthcoming-conferences/big-data-big-ideas-for-media.html
BTW: You can download ALL of my slideshows, free books and other stuff at http://futuristgerd.com/downloads/
"Data stockpiles are growing exponentially...consumer profiles, media content usage patterns, Twitter and Facebook posts, online purchases, public records, real-time media user behavior and much more. The Big Ideas conference speakers will inspire tactics and strategies to harness these data.
The media industry's leading edge experts from journalism and business disciplines will detail their own case studies, outlining their challenges and triumphs using tools to understand complex data sets. They will outline how these experiences have paved the way to prize-winning journalism, audience insights and growing revenues..."
What Are The Latest Trends in Data Science?Bernard Marr
The benefits of a data-driven approach to business are well established but not set in stone. The relentless march of technological progress means the boundaries of what is possible are constantly being redrawn, spawning new behaviors, trends and buzzwords.
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
In the Dark? Understanding Big Data & AI: Talent Acquisition Strategies for 2018Yoh Staffing Solutions
Big Data and AI have changed the way companies acquire people. Is your organization one of them? Shed some light on this innovation with these valuable tips and gain a better understanding of the implications Big Data and AI can have on your talent acquisition strategy.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
We hear specific technology terms more frequently, however some individuals may not know what they mean.
My goal is to help you understand the topics that are changing our world and will most likely continue to play an integral part in how we interact with technology.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
How Big Data Will Change Businesses In 2018Tyrone Systems
In 2018 Big Data will include formerly dark data and virtually every other data platform used by corporations to deliver personalization.
In the world of analytics and computational data, two core concepts divide the medium — structured and unstructured data.Structured data is quickly and easily organized and searchable using basic algorithms and tools. Unstructured data, on the other hand, is more akin to human language. It doesn’t cram neatly into relational databases, nor is it necessarily compatible with algorithmic tools and search functions.
Beginning to understand the world of data demands the evolution of procedures and skillsets in tune with the rewarding trends. As the excerpts from the Fortune Business Insight article state; the market for data analytics is estimated to expand by 25% between 2021-2030. Data scientists are predicted to leverage the highest possible benefits for industries such as banking, finance, insurance, entertainment, telecommunication, automobile, etc.
Pace up with the fastest-evolving industries of all time. Make informed decisions in the world of Data Science by mastering the emerging trends in diversified realms of data. Bring in the change with the following Data Science trends set in place in time:
1. Blockchain technology
2. Natural Language Processing
3. Internet of Things
4. Auto Machine Learning
5. Immersive experiences
6. Robotic Process Automation
7. TinyML and Small Data
8. AI-powered Virtual Assistants
9. Graph Analytics
10. Cloyd computing
11. Image processing
12. Data Visualization
13. Augmented Analytics
14. Predictive Analytics
15. Scalable Artificial Intelligence
As is evident, there will be more data in the coming years. This is a clear indication of an escalated need for staying upbeat with the proposed data science industry trends for years to follow. Make the most of the opportunity by enrolling with top-ranking data science certifications from globally renowned data credentials providers.
Download your copy & boost your chances at landing your dream Data Science Jobs with USDSI®
The 5 Biggest Data Science Trends In 2022Bernard Marr
Data has become one of today's most important business assets, and data science enables us to turn this data into value. In the field, we see fast evolutions and new advances, especially in artificial intelligence and machine learning. Here, we look at the five biggest data science trends for 2022.
The 4 Biggest Trends In Big Data and Analytics Right For 2021Bernard Marr
Big Data is a term that’s come to be used to describe the technology and practice of working with data that’s not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who’ve turned a vision into reality.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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
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
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Fontys Eric van Tol
1. Tja, De Big Data belofte: Data gedreven Service
innovatie!?
Service innovatie: creëren van niet bestaande
verlangens
Data gedreven: tegelijk zoeken naar de vraag en de
data.
2.
3.
4. The Big Data Surfers
Frightful Five: Apple, Amazon, Google,
Microsoft and Facebook
value $2.2 trillion
BAT: Baidu, Alibaba and Tencent
value $600 billion
5. The race against the machine
unemployed knowledge worker?
End of privacy
Is anonymity still possible?
Data Platform power
Can we compete Alibaba or Uber?
Societal anxiety
6. embrace new technology and adapt legacy
share valuable data
damaged reputation
(tackle privacy and trust issues)
The fear to
7. The holy grail of Big Data: Personalized services
- predict behavior – immediate response - location based
• personalized medicine (match your physiology)
• video suggestions (perfectly fit what they want to watch now)
• real time shelter advice (customized indications in case of
emergency)
• precision farming (real-time, geo-spatially data such as seeding
records, fertility applications, weather, soil, and crop health)
15. Online Gaming
Essence that you can:
• Count everything (N = All cases)
• See changes, (or near) real time
• See patterns
• from the past that says something about the future
• detailed anomalies
• trends
Personalization
Real Time & Predictive
16. Game addicts go to unconventional and dangerous
places to find all these creature
developers have reverse engineered the private,
internal Pokémon Go API creating a number of
unofficial APIs and third-party apps. Most popular GPS
spoofing
full access to Google account, all apps therein, and
the personal information each app contained.
16/17 July, DDoS (Denial-of-service) attack took
down Pokemon Go for a few hours.
The success of Pokemon Go has spawned dozens of
copycats like Unilever Ventures
Addiction, Security, Privacy, Intelectual
prpperty issues
25. Data driven service innovation
75% Dutch economy are services
and is lagging behind in services productivity
differentiation of companies depends their (big) data strategies.
finding new and innovative data driven services
https://www.rijksoverheid.nl/documenten/rapporten/2016/07/06/werkgroeprapporten-studiegroep-duurzame-groei
26. Nature of ICT systems (1)
the neat causal ICT systems are no more
where the ICT system is too complex to understand.
From neat “if X then Y else Z” rules,
to more organic and probabilistic ICT systems,
27. “…we can only fully figure out the meaning
of new technology in business and
institutions after the fact [drift]; and that we
plainly have to live with such
and state of ignorance.”
impossibility
*C. Ciborra. 2002. The Labyrinths of Information, Oxford University Press, Chapter 5 Dérive: Drift and deviation, p. 85.
Nature of ICT systems (2)
The neat causal ICT systems are no more
And it is not a new insight…
How do we recognize our ignorance?
Can we get a overview or do we live an illusion of control?
28. Nature of Services development
are complex and intangible
Difference between formal description of services and what you see
happening
"The moment of truth 'of the service is difficult to follow
Co-creation, participation and simultaneous production and
consumption of many services
Innovative service which tries establish desires that previously did
not exist.
How do we deal with this complexity?
What is the common language of a multidisciplinary team?
How do we discover new services?
29. “The algorithm did it” is not an acceptable excuse if algorithmic
systems make mistakes or have undesired consequences.
https:www.technologyreview.com/s/602933/how-to-hold-algorithms-accountable/?imm_mid=0eb199&cmp=em-data-na-na-newsltr//_20161130
Richards and King (2013); Schneier (2015).
https://www.oreilly.com/ideas/if-prejudice-lurks-among-us-can-our-analytics-do-any-better
If prejudice lurks among us, can our analytics do any better?
Human and algorithmic bias
Nature of Machine Learning (1)
a barrier to transparency
30. Nature of Machine Learning (2)
a barrier to transparency
“A mismatch between the mathematical optimization in
high-dimensionality characteristic of machine learning and
the demands of human-scale reasoning
Neural networks, especially with the rise of deep learning,
pose perhaps the biggest challenge.
European Union regulations on algorithmic decision-making and a “right to explanation Bryce Goodman & Seth Flaxman 2016 https://arxiv.org/pdf/1606.08813v2.pdf
How do we make Machine Learning trackable or traceable?
How do we explain a algorithmic decision to the user?
31. Nature of social media
daily life is published
Consumers are publishing everything of their
daily life's. To control this is not doable and most
of the time undesirable.
Social Media players share less and less data…
How do we get access to social media data?
How do we do that without losing the trust of a consumer or citizen?
32. Nature of autonomously acting algorithms
unforeseen dynamics
Autonomously acting algorithms that exist on the Internet
• vast, global, connected, always on, silent, unseen
• self-discovery, self-organising, self-healing, self-learning
Generates side effects, unexpected consequences, unforeseen dynamics
high-frequency trading, mobile add auction, and general IoT as example
How do we deal with unforeseen dynamics?
Do we trust a self acting military drone?
33. Nature of the prosumer (1)
risk naive
“People aren’t stupid. The problem is that our educational
system has an amazing blind spot concerning risk literacy.
We teach our children the mathematics of certainty
but not the mathematics of uncertainty, statistical
thinking. And we teach our children biology
but not the psychology that shapes their fears and desires.
Do we know enough? What is sufficient to cope?
Gerd Grigerenzer, auteur of ‘Risk Savvy’
34. Nature of the prosumer (2)
knowing is not yet doing
From the behavioral sciences it has been shown
that the ability of people to weigh information
and to take rational choices are limited
https://www.wrr.nl/publicaties/rapporten/2017/04/24/weten-is-nog-geen-doen
So, how feasible is a good risk assessment?
35. Nature of the prosumer (3)
unconscious part of the service
A prosumer is a person who consumes and produces media.
Self-service origin
From ATMs to e-commerce to mobile payments,
lower costs and more convenience
https://en.wikipedia.org/wiki/Prosumer
http://www.itif.org/files/2010-self-service-economy.pdf
How to balance convenience and privacy?
How do we ensure that the consumer is in control?
36. Nature of regulation
GDPR (General Data Protection Regulation) May 2018,
Implementation by the Personal Data Authority in the Netherlands
To much restriction of service development?
PSD2 (Payment Services Directive2) January 2018
Opportunity for fintech companies? Threat to banks?
Do we oversee the consequences?
Can we comply?
37. A Big Data project is an experiment with a continuous interaction between
poor requirement articulation and naive exploration of data sets.
A continuous iteration between knowing the problem and having the data
Data gets value during use
37
Nature of a Big Data project (1)
messy experiments
What is a good Big Data project?
Is there a 'best practice’ or even a ‘good practice’?
38. Data agility separates winners and losers
Big data projects are more research projects than production
projects
Conventional project management combine wisdom with data
agility (light, fast and accurate)
Well, and how do we do that?
Does it work for large ICT infrastructural projects?
Nature of a Big Data project (2)
messy experiments
39. Nature of ICT systems
Acknowledge your ignorance! When are you in the ‘factory’ and when in ‘chaos’? Sense - Analyse or Act - probe.
Nature of Services development
Trust operational people in ‘the moment of truth’ of the service – and digital service design
Nature of Machine Learning
Alternative traceable algorithms - AI assisted human decisions in sensitive situations
Nature of autonomously acting algorithms
Depending application
Nature of social media
Do not use all insights
Nature of innovation
Be a smart improviser in search of surprises. What is the use of a map if you do not know the terrain?
Nature of the prosumer
Educate ‘learn to learn’. risk savvy and consumer is conscious steering part the service creation.
Nature of regulation
GDPR as opportunity to get more customer focus. PDP2 to get innovation in finance.
Nature of a Big Data project
dare to fail, share your failures – more than saying that you do
Nature of data driven digital service innovation
40.
41. without prejudice How to avoid unfair conclusions even if they are true?
without guesswork How to answer questions with a guaranteed level of accuracy?
ensures confidentiality How to answer questions without revealing secrets?
provides transparency How to clarify answers such that they become indisputable?
Data driven service innovation is FACTual?
FACT (fair, accurate, confidential, transparent) algorithms
Wil van der Aalst http://www.vsnu.nl/digital-society-introduction-researchers/big-data.html
Data driven service innovation is FAIR?
FAIR (findable, accessible, interoperable, reusable) data
https://wetenschapsagenda.nl/nwo-honoreert-aanvragen-startimpuls-nationale-wetenschapsagenda/
Route Waardecreatie door verantwoorde toegang en gebruik van big data
Verantwoorde Waardecreatie met Big Data