Key ideas in this slide:
1) Knowledge about the theory of “Human Data World”
2) Examples about Data Product in real life
3) How to build a Data Product
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
Agenda:
• Background for the development: From commodity
to experience
• Indirect use of experiences: Experience as value
adding
• Experience process
• Selling pure experiences: Using the experience realm
model
• How to develop experiences
• Creating the experience settings
Application-oriented ping-pong benchmarking: how to assess the real communica...Trieu Nguyen
Moving data between processes has often been discussed as one of the
major bottlenecks in parallel computing—there is a large body of research, striving
to improve communication latency and bandwidth on different networks, measured
with ping-pong benchmarks of different message sizes. In practice, the data to be
communicated generally originates from application data structures and needs to be
serialized before communicating it over serial network channels.
Upgrade Without the Headache: Best Practices for Upgrading Hadoop in ProductionCloudera, Inc.
Walk through some of the best practices to keep in mind when it comes to upgrading your cluster, and learn how to leverage new Upgrade Wizard features in Cloudera Enterprise 5.3.
For most mission critical workloads, downtime is never an option. Any downtime can have a direct impact on revenue and lead to frantic calls in the middle of the night. For this reason, upgrading the software that powers these workloads can often be a daunting task. It can cause unpredictable issues without access to support. That’s why an enterprise-grade administration tool is crucial for running Hadoop in production. Hadoop consists of dozens of components, running across multiple machines, all with their own configurations. That can lead to a lot of complexity and uncertainty - especially when taking the upgrade plunge.
Cloudera Manager makes it easy and is the only production-ready administration tool for Hadoop. Not only does Cloudera Manager feature zero-downtime rolling upgrades, but it also has a built in Upgrade Wizard to make upgrades simple and predictable.
Trước đây, chúng ta mới chỉ biết đến dữ liệu có cấu trúc (structure data), ngày nay, với sự kết hợp của dữ liệu và internet, đã xuất hiện một dạng khác của dữ liệu - Big
data (dịch là “dữ liệu lớn”). Dữ liệu này có thể từ các nguồn như: hồ sơ hành chính,giao dịch điện tử, dòng trạng thái (status), chia sẻ hình ảnh, bình luận, nhắn tin...của chính
chúng ta, nói cách khác chúng là dữ liệu được sản sinh qua quá trình chia sẻ thông tin trực tuyến liên tục của người sử dụng. Để cung cấp cái nhìn tổng quan, chúng tôi xin giới thiệu tóm tắt những nét chính về dữ liệu lớn cũng như những cơ hội và thách thức mà dữ liệu lớn mang lại.
This presentation on building servers explains what is Netty, why choosing it and shows how with very little code you can build an asynchronous app server.
Data Economy: Lessons learned and the Road ahead!Ahmet Bulut
Trading Privacy for Value
In the start-up culture of the 21st century, we live by the motto “move fast and break things.” What if what gets broken is society*?
how can we build data products and services that use data ethically & responsibly?
how do companies take a data (science) project from lab to production successfully?
Systems that can explain their decisions.
how can we interconnect the web of data, its agents, and their decisions to enlarge the pie?
Agenda:
• Background for the development: From commodity
to experience
• Indirect use of experiences: Experience as value
adding
• Experience process
• Selling pure experiences: Using the experience realm
model
• How to develop experiences
• Creating the experience settings
Application-oriented ping-pong benchmarking: how to assess the real communica...Trieu Nguyen
Moving data between processes has often been discussed as one of the
major bottlenecks in parallel computing—there is a large body of research, striving
to improve communication latency and bandwidth on different networks, measured
with ping-pong benchmarks of different message sizes. In practice, the data to be
communicated generally originates from application data structures and needs to be
serialized before communicating it over serial network channels.
Upgrade Without the Headache: Best Practices for Upgrading Hadoop in ProductionCloudera, Inc.
Walk through some of the best practices to keep in mind when it comes to upgrading your cluster, and learn how to leverage new Upgrade Wizard features in Cloudera Enterprise 5.3.
For most mission critical workloads, downtime is never an option. Any downtime can have a direct impact on revenue and lead to frantic calls in the middle of the night. For this reason, upgrading the software that powers these workloads can often be a daunting task. It can cause unpredictable issues without access to support. That’s why an enterprise-grade administration tool is crucial for running Hadoop in production. Hadoop consists of dozens of components, running across multiple machines, all with their own configurations. That can lead to a lot of complexity and uncertainty - especially when taking the upgrade plunge.
Cloudera Manager makes it easy and is the only production-ready administration tool for Hadoop. Not only does Cloudera Manager feature zero-downtime rolling upgrades, but it also has a built in Upgrade Wizard to make upgrades simple and predictable.
Trước đây, chúng ta mới chỉ biết đến dữ liệu có cấu trúc (structure data), ngày nay, với sự kết hợp của dữ liệu và internet, đã xuất hiện một dạng khác của dữ liệu - Big
data (dịch là “dữ liệu lớn”). Dữ liệu này có thể từ các nguồn như: hồ sơ hành chính,giao dịch điện tử, dòng trạng thái (status), chia sẻ hình ảnh, bình luận, nhắn tin...của chính
chúng ta, nói cách khác chúng là dữ liệu được sản sinh qua quá trình chia sẻ thông tin trực tuyến liên tục của người sử dụng. Để cung cấp cái nhìn tổng quan, chúng tôi xin giới thiệu tóm tắt những nét chính về dữ liệu lớn cũng như những cơ hội và thách thức mà dữ liệu lớn mang lại.
This presentation on building servers explains what is Netty, why choosing it and shows how with very little code you can build an asynchronous app server.
Data Economy: Lessons learned and the Road ahead!Ahmet Bulut
Trading Privacy for Value
In the start-up culture of the 21st century, we live by the motto “move fast and break things.” What if what gets broken is society*?
how can we build data products and services that use data ethically & responsibly?
how do companies take a data (science) project from lab to production successfully?
Systems that can explain their decisions.
how can we interconnect the web of data, its agents, and their decisions to enlarge the pie?
NCompass Live - March 13, 2019
http://nlc.nebraska.gov/NCompassLive/
In the presentation on February 13, I covered what emerging technology is and how it relates to libraries. Now it’s time to dive into what that means on a larger scale. What makes technology good or bad? Who is really qualified to make that determination? Anyone who tracks emerging technology will start to think about how this technology will affect the future of our communities and the world. What will make a piece of technology influential and powerful in the world? Tune in if you want to learn more about the following topics:
•Which ethics matter most in technology?
•What makes technology good or bad?
•What potential dangers should we be aware of with new technology?
•What factors might affect society in the long-run?
There are no easy answers to any of these questions. The concept of ‘ethics’ tends to be a gray area for many, and understandably so. This presentation won’t give you a finite right or wrong stance on all technology. But it will provide you with the tools to make the decision for yourself.
Presenter: Amanda Sweet, Technology Innovation Librarian, Nebraska Library Commission.
Wild Apricot Expert Webinar: Need Volunteers Four Tech Trends You Need to KnowWild Apricot
If you’re struggling to attract and mobilize volunteers, you’re not alone. Many organizations are experiencing the effects of four tech trends that are changing public expectations and behavior. In this webinar, volunteerism expert and international thought leader, Tobi Johnson, shares how successful organizations can attract and sustain volunteer involvement in today’s rapidly-evolving digital world.
In this free webinar, Tobi will show you:
- How new technology is impacting the public’s expectations of causes and charities
- 4 key tech trends that will help you recruit, grow, engage, and recognize volunteers
- Low-cost, simple tips to address common volunteer challenges using technology
https://www.wildapricot.com
Privacy, Ethics, and Future Uses of the Social WebMatthew Russell
A presentation to the Owen Graduate School of Management (Vanderbilt University) about social media and some of the technology behind the future uses of social media that are likely to shape the future of the Web as we know it.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
CRO PROS - Optimisation Insights | Journey and Lessons on building a productCatchi
In this compelling session we learned how different industries (politics, streaming, fintech) go about optimise their digital assets by using conversion rate optimisation and personalisation techniques.
How to track and improve Customer Experience with LEO CDPTrieu Nguyen
1) Why CX measurement is so important
2) Introduction to key metrics of CX
2.1 Customer Feedback Score (CFS)
2.2 Customer Effort Score (CES)
2.3 Customer Satisfaction Score (CSAT)
2.4 Net Promoter Score (NPS)
3) Using Journey Map to CX Data Management
4) Introduction to LEO CDP and demo
[Notes] Customer 360 Analytics with LEO CDPTrieu Nguyen
Part 1: Why should every business need to deploy a CDP ?
1. Big data is the reality of business today
2. What are technologies to manage customer data ?
3. The rise of first-party data and new technologies for Digital Marketing
4. How to apply USPA mindset to build your CDP for data-driven business
Part 2: How to use LEO CDP for your business
1. Core functions of LEO CDP for marketers and IT managers
2. Data Unification for Customer 360 Analytics
3. Data Segmentation
4. Customer Personalization
5. Customer Data Activation
Part 3: Case study in O2O Retail and Ecommerce
1. How to build customer journey map for ecommerce and retail
2. How to do customer analytics to find ideal customer profiles
The ideal customer profile in a B2B context
The ideal customer profile in a B2C context
3. Manage product catalog for customer personalization
4. Monitoring Data of Customer Experience (CX Analytics)
CX Data Flow
CX Rating plugin is embedded in the website, to collect feedback data
An overview of CX Report
A CX Report in a customer profile
5. Monitoring data with real-time event tracking reports
Event Data Flow
Summary Event Data Report
Event Data Report in a Customer Profile
Part 4: How to setup an instance of LEO CDP for free
1. Technical architecture
2. Server infrastructure
3. Setup middlewares: Nginx, ArangoDB, Redis, Java and Python
Network requirements
Software requirements for new server
ArangoDB
Nginx Proxy
SSL for Nginx Server
Java 8 JVM
Redis
Install Notes for Linux Server
Clone binary code for new server
Set DNS hosts for LEO CDP workers
4. Setup data for testing and system verification
Part 5: Summary all key ideas
Why should you invest in LEO CDP ?
Purpose: Big data and AI democracy for SMEs companies
Problem: Customer Analytics and Customer Personalization
Solutions: CDP + CX + Personalization Engine
Product demo: LEO CDP for Ecommerce and Fintech
Business model: Freemium → Ecosystem → Subscription
Market size: 20 billion USD in 2026 and CAGR 34.6%
Differentiation: cloud-native software
Go-to-market approach: Community → Free → Paid
Team: 1 full-stack dev, 1 data scientist and 12,000 fans of BigDataVietnam.org Community
Need 150,000 USD for scaling business (you get 20% share)
Lộ trình triển khai LEO CDP cho ngành bất động sảnTrieu Nguyen
1) Hiểu bài toán số hoá trải nghiệm khách hàng
2) Nghiên cứu giải pháp LEO CDP
3) Lộ trình triển khai
Phát triển / số hoá điểm chạm khách hàng
Xây dựng bản đồ hành trình khách hàng
Định nghĩa các metrics và KPI quan trọng
Xây dựng web portal và mobile data hub
Xây dựng kế hoạch Digital Marketing
Triển khai CDP và Marketing Automation
Xây dựng đội Analytics để phân tích dữ liệu
From Dataism to Customer Data PlatformTrieu Nguyen
1) How to think in the age of Dataism with LEO CDP ?
2) Why is Dataism for human, business and society ?
3) How should LEO Customer Data Platform (LEO CDP) work ?
4) How to use LEO CDP for your business ?
Data collection, processing & organization with USPA frameworkTrieu Nguyen
1) How to think in the age of Dataism with USPA framework ?
2) How to collect customer data
3) Data Segmentation Processing for flexibility and scalability
4) Data Organization for personalization and business activation
Part 1: Introduction to digital marketing technologyTrieu Nguyen
Outline of this course
1. Digital Media Models in the age of marketing 4.0
2. Strategic Thought as It Relates to Digital Marketing
3. Web: The Center of Digital Marketing Delivery Mix
4. Content Management System (CMS) and headless CMS
5. Search Engine Marketing
6. Email Marketing
7. Social Media and Mobile Marketing
8. Introduction to Advertising Technology (Ad Tech)
9. Introduction to Customer Database and Customer Data Platform (CDP)
10. Legal Issues: Data privacy, Security, and Intellectual Property
11. Case study: IKEA - from business strategy to digital marketing strategy
12. Recommended books for self-study
Transform your marketing and sales capabilities with Big Data and A.I
1) Why is Customer Data Platform (CDP) ?
Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing
Question: Why can CDP disrupt business model for restaurant industry (B2C) ?
2) How would CDP work in practice ?
Introducing USPA.tech as logical framework for implementing CDP in practice
How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ?
3) How can we implement CDP for business?
Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)
Video Ecosystem and some ideas about video big dataTrieu Nguyen
Introduction to Video Ecosystem Mind Map
Video Streaming Platform
Video Ad Tech Platform
Video Player Platform
Video Content Distribution Platform
Video Analytics Platform
Summary of key ideas
Q & A
Concepts, use cases and principles to build big data systems (1)Trieu Nguyen
1) Introduction to the key Big Data concepts
1.1 The Origins of Big Data
1.2 What is Big Data ?
1.3 Why is Big Data So Important ?
1.4 How Is Big Data Used In Practice ?
2) Introduction to the key principles of Big Data Systems
2.1 How to design Data Pipeline in 6 steps
2.2 Using Lambda Architecture for big data processing
3) Practical case study : Chat bot with Video Recommendation Engine
4) FAQ for student
Apache Hadoop and Spark: Introduction and Use Cases for Data AnalysisTrieu Nguyen
Growth of big datasets
Introduction to Apache Hadoop and Spark for developing applications
Components of Hadoop, HDFS, MapReduce and HBase
Capabilities of Spark and the differences from a typical MapReduce solution
Some Spark use cases for data analysis
Introduction to Recommendation Systems (Vietnam Web Submit)Trieu Nguyen
1) Why do we need recommendation systems ?
2) How can we think with recommendation systems ?
3) How can we implement a recommendation system with open source technologies ?
RFX framework https://github.com/rfxlab
Apache Kafka: https://kafka.apache.org
Apache Spark: https://spark.apache.org
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Introduction to Human Data Theory for Digital Economy
1. Introduction to Human Data Theory
for Digital Economy
How to build YOUR data-driven business
in the dawn of Dataclysm ?
http://BigDataVietnam.org
2. Key ideas in this slide:
● Knowledge about the theory of “Human Data World”
● Examples about Data Product in real life
● How to build a Data Product
4. 3 core ideas of the theory of “Human Data World”
1. Soft data of life ( the emotional needs )
E.g: Love experience, Job experience, Friendship ,...
2. Hard data of life ( the fundamental human needs )
E.g: Food, Security, Money, Job, Education, ...
3. Spiritual data of life ( the inner Self needs )
a. Belief: What do you believe in your life ?
b. Mind: How do you see the World from your brain ?
c. Life: What is the experience between you and your
current reality ?
13. Human Needs → Data → Digital Service
1. Physiological needs:
food
2. Safety needs:
Financial security
3. Love and belonging:
Friendship
4. Esteem: engage in a
profession or hobby
to gain recognition
5. Self-actualization:
becomes a better
one
1. Food + reviews
2. Financial data
3. User Profile Data
4. Education data
5. Networking data
1. Foody, Google,..
2. Mobile banking
3. Facebook, Tinder
4. LinkedIn, Coursera
5. Facebook, LinkedIn
18. Data Product for Health
● Need: We need a good health
● Input: Our daily activity data → step tracking
● Output: sample step tracker app with report and virtual assistant
19. Key ideas for simple health analytics app
1. Step statistics data
2. Tracking user activity data
3. Provide information about good habits: favorite
exercise programs
27. How to build a Data Product
1. Identify your customer needs ( 3 core features)
2. Begin a list of basic questions ? E.g: What is output of my service
3. Start design thinking about your product: input, output, function