This document discusses big data and provides an overview of key concepts:
- Big data is defined as datasets that are too large or complex for traditional data management tools to handle. It is characterized by volume, velocity, and variety.
- Big data comes from a variety of sources like social media, sensors, web logs, and transaction systems. It is growing rapidly due to the digitization of information.
- Big data can be used for applications like enhancing customer insights, optimizing operations, and extending security and intelligence capabilities. Example use cases are described.
- Architecting solutions for big data requires handling its scale and integrating diverse data types and sources. Both traditional and new analytics approaches are needed.
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Financial Markets Data & Analytics Led TransformationGianpaolo Zampol
How big data, advanced analytics and cognitive computing is disrupting traditional business and operating models in financial markets? New competitors, powered by social, mobile, analytics, and cloud computing, are making new business models emerging rapidly. Wealth Management, Corporate Banking and Transaction Banking & Payments are significant sources of growth in Financial Markets. How take advantage from those new technologies to face this new scenario?
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Financial Markets Data & Analytics Led TransformationGianpaolo Zampol
How big data, advanced analytics and cognitive computing is disrupting traditional business and operating models in financial markets? New competitors, powered by social, mobile, analytics, and cloud computing, are making new business models emerging rapidly. Wealth Management, Corporate Banking and Transaction Banking & Payments are significant sources of growth in Financial Markets. How take advantage from those new technologies to face this new scenario?
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
Data-driven Banking: Managing the Digital TransformationLindaWatson19
The digital revolution has arrived in banking. Evolving customer expectations, increasing cyber threats and growing volumes of data are just a few of the challenges faced by traditional financial institutions.
Analytics: The Real-world Use of Big DataDavid Pittman
UPDATE: Register now to participate in the 2013 survey: http://ibm.com/2013bigdatasurvey IBM’s Institute for Business Value (IBV) and the University of Oxford released their information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data. For additional information, including links to a podcast with one of the lead researchers and a link to download the full report, visit http://ibm.co/RB14V0
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Rapid digitization has resulted in the production of large volumes of unstructured data. This trend is expected to provide significant opportunities for graph database market in the upcoming years
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Global Business Intelligence (BI) software vendor, Yellowfin, and Actian Corporation, pioneers of the record-breaking analytical database Vectorwise, will host a series of Big Data and BI Best Practices Webinars.
These are the slides from that presentation.
The Big Data & BI Best Practices Webinars and associated slides examine the phenomenal growth in business data and outline strategies for effectively, efficiently and quickly harnessing and exploring ‘Big Data’ for competitive advantage.
Andrew Chumney, Single View Solutions Manager, Pitney Bowes with Navneet Mathur, Senior Director of Global Solutions, Neo4j and Alex Batanov, Field Engineer, Neo4j:Europe’s General Data Protection Regulations (GDPR) will go into effect in less than a year and all companies holding data on European residents will be required to comply. Are you prepared?
Watch this webinar to understand how graph-based metadata is the best guide for organizations and IT departments to use on their path to compliance.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Data Insights and Analytics: Simplifying Data Lake and Modern BI ArchitectureDATAVERSITY
Evolving into the world of Data Lakes and leveraging Big Data effectively does not have to be complicated. Current technologies are more effective than ever, and many organizations are now invested in allocating the right resources to managing data. When it comes to Data Lake and modern Business Intelligence (BI) architecture, simplicity can be key.
Join John and Kelle for this webinar to discover:
What “simplifying” really means
What processes are needed to derive a modern BI architecture
What is required to deploy the process
How to bridge the gap from traditional BI to contemporary BI and Data Lakes
Simplifying Big Data Analytics for the BusinessTeradata Aster
Tasso Argyros, Co-Founder & Co-President, Teradata Aster presents at the 2012 Big Analytics Roadshow.
The opportunity exists for organizations in every industry to unlock the power of iterative, big data analysis with new applications such as digital marketing optimization and social network analysis to improve their bottom line. Big data analysis is not just the ability to analyze large volumes of data, but the ability to analyze more varieties of data by performing more complex analysis than is possible with more traditional technologies. This session will demonstrate how to bring the science of data to the art of business by empowering more business users and analysts with operationalized insights that drive results. See how data science is making emerging analytic technologies more accessible to businesses while providing better manageability to enterprise architects across retail, financial services, and media companies.
Data-driven Banking: Managing the Digital TransformationLindaWatson19
The digital revolution has arrived in banking. Evolving customer expectations, increasing cyber threats and growing volumes of data are just a few of the challenges faced by traditional financial institutions.
Analytics: The Real-world Use of Big DataDavid Pittman
UPDATE: Register now to participate in the 2013 survey: http://ibm.com/2013bigdatasurvey IBM’s Institute for Business Value (IBV) and the University of Oxford released their information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data. For additional information, including links to a podcast with one of the lead researchers and a link to download the full report, visit http://ibm.co/RB14V0
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Rapid digitization has resulted in the production of large volumes of unstructured data. This trend is expected to provide significant opportunities for graph database market in the upcoming years
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Global Business Intelligence (BI) software vendor, Yellowfin, and Actian Corporation, pioneers of the record-breaking analytical database Vectorwise, will host a series of Big Data and BI Best Practices Webinars.
These are the slides from that presentation.
The Big Data & BI Best Practices Webinars and associated slides examine the phenomenal growth in business data and outline strategies for effectively, efficiently and quickly harnessing and exploring ‘Big Data’ for competitive advantage.
Andrew Chumney, Single View Solutions Manager, Pitney Bowes with Navneet Mathur, Senior Director of Global Solutions, Neo4j and Alex Batanov, Field Engineer, Neo4j:Europe’s General Data Protection Regulations (GDPR) will go into effect in less than a year and all companies holding data on European residents will be required to comply. Are you prepared?
Watch this webinar to understand how graph-based metadata is the best guide for organizations and IT departments to use on their path to compliance.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Data Insights and Analytics: Simplifying Data Lake and Modern BI ArchitectureDATAVERSITY
Evolving into the world of Data Lakes and leveraging Big Data effectively does not have to be complicated. Current technologies are more effective than ever, and many organizations are now invested in allocating the right resources to managing data. When it comes to Data Lake and modern Business Intelligence (BI) architecture, simplicity can be key.
Join John and Kelle for this webinar to discover:
What “simplifying” really means
What processes are needed to derive a modern BI architecture
What is required to deploy the process
How to bridge the gap from traditional BI to contemporary BI and Data Lakes
Simplifying Big Data Analytics for the BusinessTeradata Aster
Tasso Argyros, Co-Founder & Co-President, Teradata Aster presents at the 2012 Big Analytics Roadshow.
The opportunity exists for organizations in every industry to unlock the power of iterative, big data analysis with new applications such as digital marketing optimization and social network analysis to improve their bottom line. Big data analysis is not just the ability to analyze large volumes of data, but the ability to analyze more varieties of data by performing more complex analysis than is possible with more traditional technologies. This session will demonstrate how to bring the science of data to the art of business by empowering more business users and analysts with operationalized insights that drive results. See how data science is making emerging analytic technologies more accessible to businesses while providing better manageability to enterprise architects across retail, financial services, and media companies.
Entry Points – How to Get Rolling with Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and IBM
Live Webcast Sept. 24, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7501927&rKey=664935ceb7de1aec
Where to begin? That question remains prominent for many organizations who are trying to leverage the value of big data analytics. Most sources of big data are quite different than traditional enterprise data systems. This requires new skill sets, both for the granular integration work, as well as the strategic business perspective required to design useful solutions.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the pain points associated with modern data volumes and types. He will be briefed by Rick Clements of IBM, who will tout IBM's big data platform, specifically InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. He will also present specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.
Visit InsideAnalysis.com for more information
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Praxis Telekommunikation
Für Telekommunikationsunternehmen ist es mit Big Data auf Grund der verfügbaren Kundendaten, möglich, diese Beziehungen besser zu monetarisieren. Mobily, Saudi Arabiens Telekommunikationsunternehmen mit rund 20 Millionen Kunden, beauftragte Roland Berger eine “Big Data Monetization Strategy“ zu erarbeiten. Wie das Unternehmen dadurch seine eigene Leistungsfähigkeit steigert und seine Kunden mit passgenauen Serviceleistungen anspricht, das erzählte Andreas Tiefengraber von Roland Berger Strategy Consultants beim Werbeplanung.at Summit SPEZIAL am 3. Dezember 2013 in der Uni Wien.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
33. How do you address these challenges?
These experiences reveal a great irony -- that while the impact of Big Data
will be transformational, the path to effectively harnessing it is not. The
journey is evolutionary versus revolutionary, incremental and iterative
– Demystifying Big Data, TechAmerica Report, October 2012
Is your organization characterized by one or more of the following
traits?
1. Executive Management wants a big data plan
2. Executive Management wants it to be realistic and drive value as it is
being implemented
3. Wants a partner to rely on for guidance & expertise to lower risk
4. Big Data must be leveraged with the existing infrastructure
5. Concerned about the complexity & risk of Big Data acquisition
✔
✔
✔
✔
✔