Non-technical talk for managers and Data Protection Officers about how the reasons behind the automation of creating a global data mapping for GDPR (at least), the challenges and possible methodologies using a new concept of Process Mining based on Data Activities
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Data as a Strategic Asset by Lilian CoralData Con LA
Abstract:- The City of Los Angeles, with 4 million residents and nearly 50 million visitors annually moving across 469 square miles, is not only one of the most densely populated cities, it also hosts one of the largest, most complex city infrastructures in the world. 6,000 miles of sewer underlie 22,000 miles of paved streets, that connect over 4,500 intersections, 50,000 city connected street lights and 2,000,000 google/waze connected sensors. This network of people and infrastructure are connected through the data and the systems that support them. As data transforms from an unstructured asset into the organizational wisdom that can drive this Smart City, the City of Los Angeles and the Office of Mayor Eric Garcetti work to identify new technologies and strategies for managing and harnessing the growing amount of data available to inform decision-making.
The document outlines tips for implementing an open data policy more effectively, including having political will, operative resources, focusing IT systems and projects on open data, procurement that supports data sharing, training civil servants, tools for workflows and dialog, using a city's own open APIs and data, employing in-house software developers, cooperation within a city and with other cities/government, and interacting with the community.
CIO’s are gearing up to unlock their Big Data value to gain actionable insights and fuel the Digital Transformation journey. Here are some facts that illustrate how Big Data is getting bigger.
Non-technical talk for managers and Data Protection Officers about how the reasons behind the automation of creating a global data mapping for GDPR (at least), the challenges and possible methodologies using a new concept of Process Mining based on Data Activities
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Data as a Strategic Asset by Lilian CoralData Con LA
Abstract:- The City of Los Angeles, with 4 million residents and nearly 50 million visitors annually moving across 469 square miles, is not only one of the most densely populated cities, it also hosts one of the largest, most complex city infrastructures in the world. 6,000 miles of sewer underlie 22,000 miles of paved streets, that connect over 4,500 intersections, 50,000 city connected street lights and 2,000,000 google/waze connected sensors. This network of people and infrastructure are connected through the data and the systems that support them. As data transforms from an unstructured asset into the organizational wisdom that can drive this Smart City, the City of Los Angeles and the Office of Mayor Eric Garcetti work to identify new technologies and strategies for managing and harnessing the growing amount of data available to inform decision-making.
The document outlines tips for implementing an open data policy more effectively, including having political will, operative resources, focusing IT systems and projects on open data, procurement that supports data sharing, training civil servants, tools for workflows and dialog, using a city's own open APIs and data, employing in-house software developers, cooperation within a city and with other cities/government, and interacting with the community.
CIO’s are gearing up to unlock their Big Data value to gain actionable insights and fuel the Digital Transformation journey. Here are some facts that illustrate how Big Data is getting bigger.
this presentation will let you know the in and out of bigdata growing trends... market potential , solutions provided by bigdata, advantages and disadvantages.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
The document discusses how data storage needs have grown exponentially over time. In the 1980s, a 10MB hard disk cost $3,398 while in 2000 storage costs dropped to $20 per gigabyte. By 2014, 1TB of storage cost $80 or less. This growth in data is due to factors like the rise of digital media and billions of pieces of user-generated content added daily to social networks. To manage this "big data," cloud computing has become important for cost-effective storage and analysis of the growing unstructured data in the digital universe. The future will see even more data growth and value from big data analytics.
CBS lecture at the opening of Data Science Campus of ONSPiet J.H. Daas
The document summarizes work done at the Center for Big Data Statistics, including case studies and methodological research. Some examples of projects are:
1) Visualizing income data in 2D and 3D heat maps showing relationships between age, income, and amount.
2) Analyzing road sensor data to show relationships between traffic intensity and GDP.
3) Tracking "ginger bread" product sales from scanner data around Saint Nicolas festivities.
4) Developing a social tension indicator using Twitter data.
5) Identifying web-only shops and innovative companies using web page archives.
This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
This document provides an overview of big data. It defines big data as large volumes of data that are high in velocity and variety, requiring new techniques and tools to analyze. Examples are given of the huge amounts of data generated daily by companies like Facebook, Twitter, and YouTube. The benefits of big data analytics are described as enabling better business decisions through hidden patterns, customer insights, and competitive advantages. The future of big data is promising, with the market expected to grow substantially in both revenue and jobs required to manage large amounts of data.
Ecosystm IoT forecast by geographic regionChris White
The document provides a forecast for the global IoT market from 2017 to 2022 by geographic region. It predicts that the Asia Pacific region will become the global center for IoT solutions by 2022, with China remaining the leading country market, even larger than the United States. China's commitments to smart cities, manufacturing, transportation, and healthcare will drive massive data needs, fueling technologies like analytics, machine learning, augmented reality and blockchain. The forecast methodology considers factors like projected IoT sensors, connectivity protocols, industry adoption rates, economic reports, and interviews with IoT solution buyers and providers.
The document introduces a new student group called Students for Urban Data Systems and Analytics (SUDS) at Carnegie Mellon University. SUDS will bring together students from different disciplines interested in how cities are collecting and analyzing large amounts of data to improve services. It will provide an open space for students to share ideas, host speakers on related topics, organize field trips to local tech companies, work with the city on open data projects, and help members pursue careers in data analytics. SUDS seeks founding members from all backgrounds to help shape its activities and impact.
Data journalism involves using facts and data to hold organizations accountable and analyze policies. It brings data to life by visualizing it in ways the public can understand. While journalism has always relied on facts, data journalism systematically collects and analyzes large datasets using tools like spreadsheets, data visualization programs, and freedom of information requests. This emerging field is growing in popularity but faces challenges in integrating with traditional news systems. The future of data journalism depends on journalists becoming faster and more specialized in working with data.
Data Con LA 2018 Keynote - How city data sparks community change by Sari-Ladi...Data Con LA
The document discusses Los Angeles Mayor Garcetti's vision for open data, which aims to promote civic engagement, innovation, and problem solving through making city data available on intuitive platforms. It outlines how open data can transform government by spurring civic engagement, improving operations, and delivering more equitable services. It also highlights Los Angeles' world-class open data assets and role of data literacy training in helping residents engage more with and use open data to explore their communities.
1) Statistics Netherlands is working on several Big Data projects to produce new official statistics in a timely manner using large alternative data sources like road sensors.
2) Their Center for Big Data Statistics aims to reduce response burden, deepen methodological knowledge, and stimulate cooperation using an ecosystem of partners.
3) As a proof of concept, they have produced the first Big Data-based official statistic on regional traffic intensity using minute-level road sensor data from 20,000 sensors on Dutch highways. This required data cleaning, transformation, estimation techniques, and integrating skills from statistics, IT, and subject-matter expertise.
This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
Presentation on informatics and digital priorities for social care by Andrew Fenton, Department of Health, at the Local Digital Futures - Working as One: Platforms & Sharing event held on 4 March 2016 in London.
04022021 Miapetra Kumpula-Natri: A Human-centric Data strategy and sustainabl...Sitra / Hyvinvointi
Sitra webinar 4.2.2021
The sustainable use of data – the European competitive advantage
Key note: Miapetra Kumpula-Natri: A Human-centric Data strategy and sustainable use of data
This document discusses big data and Hadoop. It defines big data and Hadoop, and explains how big data can transform businesses through predictive analytics, understanding markets and customers, and optimizing business processes. It also outlines the challenges of utilizing big data, including data, process, security, and privacy challenges. Hadoop is introduced as an open source framework for storing and processing big data across clustered systems, and some of the challenges in implementing Hadoop are discussed.
SMART PAPER D.U.A.L. book: based at MyData, OurData, printed on-demand, circular lifecycle, real time updated, intuitive tool for mobile learning, accessible, for integration to digital society
The impact of data-enabled innovation in local public services in the UK - Ja...mysociety
This was presented at mySociety's TICTeC Local 2019 conference, which was held on 1st November 2019 at City Hall in London. More details on the conference can be found here: https://tictec.mysociety.org/local/2019
SC4 Workshop 1: Logistics and big data German herreroBigData_Europe
This document discusses the potential of big data in logistics. It notes that big data in logistics is characterized by large volumes of diverse data from both structured and unstructured sources. Applying advanced analytics to big data can provide greater insights across supply chains, enabling more efficient routing, inventory control, and issue resolution. Key challenges to realizing big data's potential in logistics include identifying appropriate business cases, gaining data sharing between stakeholders, and developing data science expertise in the logistics field.
this presentation will let you know the in and out of bigdata growing trends... market potential , solutions provided by bigdata, advantages and disadvantages.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
The document discusses how data storage needs have grown exponentially over time. In the 1980s, a 10MB hard disk cost $3,398 while in 2000 storage costs dropped to $20 per gigabyte. By 2014, 1TB of storage cost $80 or less. This growth in data is due to factors like the rise of digital media and billions of pieces of user-generated content added daily to social networks. To manage this "big data," cloud computing has become important for cost-effective storage and analysis of the growing unstructured data in the digital universe. The future will see even more data growth and value from big data analytics.
CBS lecture at the opening of Data Science Campus of ONSPiet J.H. Daas
The document summarizes work done at the Center for Big Data Statistics, including case studies and methodological research. Some examples of projects are:
1) Visualizing income data in 2D and 3D heat maps showing relationships between age, income, and amount.
2) Analyzing road sensor data to show relationships between traffic intensity and GDP.
3) Tracking "ginger bread" product sales from scanner data around Saint Nicolas festivities.
4) Developing a social tension indicator using Twitter data.
5) Identifying web-only shops and innovative companies using web page archives.
This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
This document provides an overview of big data. It defines big data as large volumes of data that are high in velocity and variety, requiring new techniques and tools to analyze. Examples are given of the huge amounts of data generated daily by companies like Facebook, Twitter, and YouTube. The benefits of big data analytics are described as enabling better business decisions through hidden patterns, customer insights, and competitive advantages. The future of big data is promising, with the market expected to grow substantially in both revenue and jobs required to manage large amounts of data.
Ecosystm IoT forecast by geographic regionChris White
The document provides a forecast for the global IoT market from 2017 to 2022 by geographic region. It predicts that the Asia Pacific region will become the global center for IoT solutions by 2022, with China remaining the leading country market, even larger than the United States. China's commitments to smart cities, manufacturing, transportation, and healthcare will drive massive data needs, fueling technologies like analytics, machine learning, augmented reality and blockchain. The forecast methodology considers factors like projected IoT sensors, connectivity protocols, industry adoption rates, economic reports, and interviews with IoT solution buyers and providers.
The document introduces a new student group called Students for Urban Data Systems and Analytics (SUDS) at Carnegie Mellon University. SUDS will bring together students from different disciplines interested in how cities are collecting and analyzing large amounts of data to improve services. It will provide an open space for students to share ideas, host speakers on related topics, organize field trips to local tech companies, work with the city on open data projects, and help members pursue careers in data analytics. SUDS seeks founding members from all backgrounds to help shape its activities and impact.
Data journalism involves using facts and data to hold organizations accountable and analyze policies. It brings data to life by visualizing it in ways the public can understand. While journalism has always relied on facts, data journalism systematically collects and analyzes large datasets using tools like spreadsheets, data visualization programs, and freedom of information requests. This emerging field is growing in popularity but faces challenges in integrating with traditional news systems. The future of data journalism depends on journalists becoming faster and more specialized in working with data.
Data Con LA 2018 Keynote - How city data sparks community change by Sari-Ladi...Data Con LA
The document discusses Los Angeles Mayor Garcetti's vision for open data, which aims to promote civic engagement, innovation, and problem solving through making city data available on intuitive platforms. It outlines how open data can transform government by spurring civic engagement, improving operations, and delivering more equitable services. It also highlights Los Angeles' world-class open data assets and role of data literacy training in helping residents engage more with and use open data to explore their communities.
1) Statistics Netherlands is working on several Big Data projects to produce new official statistics in a timely manner using large alternative data sources like road sensors.
2) Their Center for Big Data Statistics aims to reduce response burden, deepen methodological knowledge, and stimulate cooperation using an ecosystem of partners.
3) As a proof of concept, they have produced the first Big Data-based official statistic on regional traffic intensity using minute-level road sensor data from 20,000 sensors on Dutch highways. This required data cleaning, transformation, estimation techniques, and integrating skills from statistics, IT, and subject-matter expertise.
This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
Presentation on informatics and digital priorities for social care by Andrew Fenton, Department of Health, at the Local Digital Futures - Working as One: Platforms & Sharing event held on 4 March 2016 in London.
04022021 Miapetra Kumpula-Natri: A Human-centric Data strategy and sustainabl...Sitra / Hyvinvointi
Sitra webinar 4.2.2021
The sustainable use of data – the European competitive advantage
Key note: Miapetra Kumpula-Natri: A Human-centric Data strategy and sustainable use of data
This document discusses big data and Hadoop. It defines big data and Hadoop, and explains how big data can transform businesses through predictive analytics, understanding markets and customers, and optimizing business processes. It also outlines the challenges of utilizing big data, including data, process, security, and privacy challenges. Hadoop is introduced as an open source framework for storing and processing big data across clustered systems, and some of the challenges in implementing Hadoop are discussed.
SMART PAPER D.U.A.L. book: based at MyData, OurData, printed on-demand, circular lifecycle, real time updated, intuitive tool for mobile learning, accessible, for integration to digital society
The impact of data-enabled innovation in local public services in the UK - Ja...mysociety
This was presented at mySociety's TICTeC Local 2019 conference, which was held on 1st November 2019 at City Hall in London. More details on the conference can be found here: https://tictec.mysociety.org/local/2019
SC4 Workshop 1: Logistics and big data German herreroBigData_Europe
This document discusses the potential of big data in logistics. It notes that big data in logistics is characterized by large volumes of diverse data from both structured and unstructured sources. Applying advanced analytics to big data can provide greater insights across supply chains, enabling more efficient routing, inventory control, and issue resolution. Key challenges to realizing big data's potential in logistics include identifying appropriate business cases, gaining data sharing between stakeholders, and developing data science expertise in the logistics field.
Where Next for Open Data in the Russian FederationAndrew Stott
This document summarizes a World Bank report on open data in Russia. It shows that Russia has made progress in opening government data but more remains to be done. The report recommends focusing on three areas: using open data to spur economic growth and business innovation, building an open data ecosystem, and ensuring technical excellence. It provides details on actions under each area, such as releasing high-value business data, supporting startups, developing skills, and adopting open standards. The document ends by calling for sustained leadership and realistic targets to take open data forward in Russia.
Presentation at the City Platform as a Service (CPaaS.io) Stakeholder Summit. 14th of December 2018 in Tokyo.
About the Role of Open Data and co-creation in the Smart City Zürich.
The document discusses the new data economy and fair data sharing. It notes that trust is a barrier preventing some Europeans from using digital services due to lack of control over personal data. A business survey found that companies support fair data principles but there is a gap between attitudes and commitment to sharing data. Developing common rules and guidelines could help address this gap and enable innovation. The presentation discusses a "rulebook" approach where ecosystem members agree to common terms to facilitate data sharing in a transparent, consensual manner.
This document provides an introduction to data science. It discusses what data science is, where data comes from, and why there is excitement around data science. It also outlines the data science process, including formulating questions, generating hypotheses, collecting and cleaning data, exploring and transforming data, building machine learning models, and evaluating and deploying models. Machine learning models like supervised learning, unsupervised learning, and reinforcement learning are also introduced. Examples of how data science has helped companies like Southwest, UPS, Amazon, Netflix, and predictions of US elections are provided to illustrate real-world applications.
How to reach a culture for analytics 2017Bart Redder
Updated for 2017: How to reach a culture for analytics.
Companies struggle to become information driven. This presentation shows
A data driven maturity model,
How to use strategy maps to align stakeholders and use it the strategy a cornerstone for corporate KPI's.
How to centralize corporate KPI's for instance in a customer dashboard
How to use (Big) data as a growth engine
How to use the value chain of insights for data governance
How to make things actionable across departments with the value chain of insights
In this presentation, Paul Ballew, D&B's Chief Data and Analytics Officer, explains the three levels of insight needed to gain an informed perspective for smarter decisions involving big data.
This document discusses how open data can fuel innovation both internally and externally for organizations like the US Census Bureau. It provides examples of how the Census Bureau's open data and APIs are being used by developers and communities to create applications and solutions. The document advocates for treating open government data as a product that can drive public sector innovation through platforms and ecosystems that bring together data producers and consumers.
Big Data in Transport: Gaps and OpportunitiesNOESIS project
The aim of this presentation is to present the main gaps and opportunities that exist in the Big Data in Transport domain. The results of this presentation are part of NOESIS project (https://noesis-project.eu).
NOESIS project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769980.
The document outlines a proposed smart data strategy for the UAE government. It begins by establishing a vision of "data driven Smart Government" and a mission of "Utilising Smart Data for Smart Services". Three strategic priorities are identified: Efficiency, Effectiveness, and Engagement. For each priority, objectives and key performance indicators are defined. Projects are then prioritized based on their impact on the strategic priorities. The strategy is designed to strengthen the government's data value chain and foster outcomes like improved rankings, productivity, and citizen happiness.
Local Open Data: a perspective from local government in England 2014Gesche Schmid
The document discusses open data from the perspective of local government in England. It outlines four phases of working with open data: 1) publishing data, 2) standardizing data, 3) analyzing and using data, and 4) engaging users. The benefits of open data include innovation, improved services, and empowering citizens, businesses and communities. However, engagement with users has been limited due to lack of skills and understanding of what can be done with data. Efforts are needed to stimulate interest, find and analyze relevant data, and tell stories with data to empower communities.
Local Open Data: A perspective from local government in England by Gesche SchmidOpening-up.eu
Local Open Data: A perspective from local government in England
to help government and companies to
develop innovative services through the
use of open data and to encourage smart
use of Social Media
By Sander Janssen, Research Team Leader of Earth Observation and Environmental Informatics at Alterra, Wageningen UR,
12 April 2017- 14:00 CET
--The webinar was held as part of ASIRA (Access to Scientific Information Resources in Agriculture) Online Course for Low-Income Countries--
This presentation focus on the political context of open data publishing, methodological frameworks for estimating the impacts of open data and highlight the Open Data Journal for Agricultural Research as publication channel for open data sets. It will also build on personal reflections on publishing open data from Dr. Janssen’s own research career.
For more on the topic: http://aims.fao.org/activity/blog/join-free-webinar-publishing-open-data-agricultural-research
This document summarizes Jisc's current and future projects in three key areas: shared digital infrastructure and services, expert advice and assistance, and sector-wide deals. It discusses several ongoing projects, including a digital capability service, learning analytics service, and national research data shared service. It also outlines Jisc's visions for the future, exploring ideas like combining different institutional data sources to enable better retention, attainment, and more efficient campuses through analytics and adaptive technologies.
Similar to Open Data and the learning curve of Datamanagement (20)
The document outlines 8 critical steps for getting started with industrial data collection: 1) Assess equipment and IT systems, 2) Map pain points and objectives, 3) Set a quick-win proof of concept, 4) Form a small dedicated IIoT team, 5) Resist problem-specific solutions, 6) Decide on cloud or on-premise storage, 7) Involve machine suppliers early, and 8) Choose an IIoT integrator wisely. Each step provides questions to consider. The key takeaways are to think big but start small, involve colleagues and partners, and keep the system open rather than locked into one technology.
Samuel Van de Velde (CEO & founder of Pozyx) spoke about 'Usecases of Ultra-wideband (UWB) indoor positioning' in Industry 4.0 at the DataOps Ghent Meetup.
UWB has become the latest technology in location awareness, with Apple utilizing the technology in its phones, watches and airtags. With Pozyx, we bring the same technology to the professional world with applications in agriculture, manufacturing and logistics.
This talk focuses on how Pozyx collects and analyzes hyper-accurate positioning data and how indoor localization can enable smart manufacturing.
Batist Leman from Azumuta gave a talk on data capture with man and machine. A keynote on why it is super important to make data capture as easy as possible and how to enable your company to use accurate real-time insights.
Second speaker of the DOG meetup on 25 February 2021 was Yields.io’s co-founder and CEO, Jos Gheerardyn.
Jos has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale. A zealous proponent of model risk governance & strategy, Jos is on a mission to empower quants, risk managers and model validators with smarter tools to turn model risk into a business driver.
Jos told us more about monitoring data quality.
This document provides an agenda for an event on fintech and dataops. The program includes talks from two speakers on using big data for consumer fintech and monitoring data quality. Recordings of the talks will be shared online. Attendees are asked to mute themselves during talks and can ask questions via chat. A survey link is provided to collect feedback on the event.
Second talk on the DataOps Ghent Meetup on 30/01/2020: "DIY with data of NMBS, Infrabel and more." by Pieter Colpaert of iRail, Open Knowledge Belgium and Ghent University
This document announces a meetup event on the topic of mobility and dataops. It introduces two speakers who will present on collecting traffic data through citizen science and utilizing open data from Belgian transportation agencies. The event details that additional mobility and dataops workshops will take place in Ghent in April and encourages attendees to follow the organizers on social media, join their Slack channel, and view past presentations. The meetup on mobility and dataops is scheduled to begin with talks at 7:30pm.
This document discusses practical use cases for data-driven digital marketing. It provides two case studies:
1) For Eurotunnel, audience segmentation was used to better target customers based on location, booking horizon, and vehicle type. This improved acquisition rates and ROI.
2) For Carglass, dynamic creative optimization (DCO) was implemented to automate ad creation and delivery based on variables like product, brand, and language. This reduced production costs while allowing more ad testing and personalization early in the customer funnel.
The document emphasizes leveraging first-party data to gain customer insights, build audience profiles, and personalize the customer experience across digital channels. This improves marketing performance metrics like conversion rates and
Some examples explaining the value of open data in health care and life scienceDataops Ghent Meetup
- "Some Examples explaining the Value of Open Data in HealthCare and Life Science." by Hans Constandt of ONTOFORCE at the DataOps Ghent Meetup of 24th October 2019.
The document discusses an event about DataOps and open data. It lists three speakers for the event: Joran Van Daele, Open Data Manager for Stad Gent; Toon Vanagt, Managing Partner of Data.be; and Bart Rosseau, CDO of Stad Gent. The document then repeats "DataOps" several times and provides a definition of open data from Wikipedia as "data that is freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control." It concludes by listing some related terms: API and CRM.
This document welcomes attendees to the first DataOps meetup. It discusses how DataOps helps bring together different data professionals, addresses common struggles around accessing, validating, and keeping up with changing data. The meetup promotes methodologies, tools, technologies, and help from experts to empower all attendees and encourage experimentation as they become more well-rounded data professionals.
DataScouts provides a collaborative platform to gather competitive intelligence on companies using automated workflows. The platform collects both structured and unstructured data from various sources to build up-to-date company profiles, market reports, and allows for systematic analysis. It utilizes machine learning techniques to automatically compose firmographic information, scores and ranks companies, and identifies relationships between entities.
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.
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
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.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
3. Data in a local government
> Maps, registries, permits, demographics, events,
news, bylaws, …
> Used for policymaking, operational decisions,
analysis, communication
• Data & Policy
> Open Data in coalition agreement for 10+ years
> New coalition agreement: ‘datadriven’
> GDPR put data on political agenda
Open Data for better datamanagement 317 juli 2019
5. Open Data
From USB with 15 excel files to
‘FIVE STAR LINKED OPEN DATA SEMANTIC API
PUBLICATION FOR QUADRUPLE HELIX COCREATION OF
SOLUTIONS FOR SOCIETAL CHALLENGES’
Open Data for better datamanagement 517 juli 2019
8. Datamaturity
>Externalising the data raises sensitivity
>First users of open data are our
colleagues
>Reuse shows link between dataquality
and result
Open Data for better datamanagement 817 juli 2019
10. Data & Informatie
• 1 team, many topics
– Datamanagement
– Data-analysis
– Information management
– IT alignment
– International projects
– 3D
– International networks
– (linked) open data
– Data-awareness projects (events, …)
– …
Ambition!
Strategic information management
Open Data for better datamanagement 1017 juli 2019
14. Yearly hackathon, since 2010.
> Hackathon?
> Yearly theme
– Maps, sports, …
– 2019: Museum
– 2020: Mobility
> Incentive for city departments to share data
> Collect data AND challenges
presentation + ideation (datadive)
hackathon
feedback
Open Data for better datamanagement 1417 juli 2019
15. Incentive for open data collection, you say?
> Less open data minded departments!
> “What’s in it for me?”
> Awareness & use of open data!
> Next steps are clear!
> Connections!
Open Data for better datamanagement 1517 juli 2019
16. Incentive for open data collection: results!
Open Data for better datamanagement 1617 juli 2019
22. Finding new open data?
> How can we create a more frequent ‘stream’ of ‘new’
open data?
– Update existing open data
– Deleting existing open data
– Uploading new open data
> Also, how can we communicate this?
– Datadives? Hackathons?
– …
Open Data for better datamanagement 2217 juli 2019