The document discusses big data trends and developments. It notes that while firms recognize the importance of data, they currently only utilize a small percentage of the data available to them. It also discusses how data sources are continuing to multiply and how most business intelligence remains backward-looking. The document outlines some key shifts in big data and analytics, including self-service tools and predictive analytics. It provides examples of how some companies are using big data technologies and lessons learned, emphasizing that skills, security, and business context are important considerations.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Intel, Cloudera and guest speaker Forrester Research, Inc. discuss the strategy of pervasive analytics and real life examples of how analytics have already been embedded into applications and workflows.
Le sfide legate alla gestione di un IT sempre piu’ dinamica e pervasiva non possono essere imbrigliate in un approccio che deve conoscere a priori quali sono oggetti, metriche e situazioni da osservare per intercettare e risolvere gli “incidents” di servizio. Oggi e’ possibile raccogliere, memorizzare e analizzare in real time TUTTE le informazioni prodotte dinamicamente da infrastrutture, applicazioni, servizi IT e utenti – i BIG DATA dell’IT – per derivarne nuova conoscenza e azioni atte a prevenire o risolvere velocemente le anomalie : e’ l’IT Operations Analytics secondo HP.
Mauro Ferrami , HP Software Business Consultant
This presentation shows how Predictive Analytics can be more futuristic than BI in using past events to predict the future.
Furthermore, we explore the best practices in Predictive Analytics, the challenges in deployment and how this solution can be used to create business value for the organization.
Presented by Ajay Gopikrishnan, our expert in Predictive Analytics and Data Mining at the BA4ALL (Business Analytics Insight 2014) event in the Netherlands.
http://www.capgemini.com/big-data-analytics
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
My goal today is to inspire you to make a strong business case for applying big data in your enterprise, a key part of which is taking big data beyond analytics.
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Intel, Cloudera and guest speaker Forrester Research, Inc. discuss the strategy of pervasive analytics and real life examples of how analytics have already been embedded into applications and workflows.
Le sfide legate alla gestione di un IT sempre piu’ dinamica e pervasiva non possono essere imbrigliate in un approccio che deve conoscere a priori quali sono oggetti, metriche e situazioni da osservare per intercettare e risolvere gli “incidents” di servizio. Oggi e’ possibile raccogliere, memorizzare e analizzare in real time TUTTE le informazioni prodotte dinamicamente da infrastrutture, applicazioni, servizi IT e utenti – i BIG DATA dell’IT – per derivarne nuova conoscenza e azioni atte a prevenire o risolvere velocemente le anomalie : e’ l’IT Operations Analytics secondo HP.
Mauro Ferrami , HP Software Business Consultant
This presentation shows how Predictive Analytics can be more futuristic than BI in using past events to predict the future.
Furthermore, we explore the best practices in Predictive Analytics, the challenges in deployment and how this solution can be used to create business value for the organization.
Presented by Ajay Gopikrishnan, our expert in Predictive Analytics and Data Mining at the BA4ALL (Business Analytics Insight 2014) event in the Netherlands.
http://www.capgemini.com/big-data-analytics
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
My goal today is to inspire you to make a strong business case for applying big data in your enterprise, a key part of which is taking big data beyond analytics.
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Conflict in the Cloud – Issues & Solutions for Big DataHalo BI
Halo BI CEO, Keith Peterson, presents at the 6th Annual Cloud Computing Conference - AITP San Diego: Conflict in the Cloud – Issues & Solutions for Big Data.
Cloud services make money based on the volume of data stored in the cloud – and big data delivers that volume. But companies seeking to use big data are looking for economies of scale from the Cloud.
How to Scale BI and Analytics with Hadoop-based PlatformsArcadia Data
You’re using Apache Hadoop and cloud-based data platforms, but can your BI and analytics tools keep up? Can you provide fast, secure, self-service access to all the data business users want?
Analyzing big data poses multiple challenges. Highly parallel distributed data architecture is one solution, but until recently it has been mostly limited to databases, not business intelligence (BI) application servers.
Join this informative webinar with guest speaker Boris Evelson, VP and principal analyst at Forrester Research, and Priyank Patel, co-founder and chief product officer at Arcadia Data. Enterprise architects, data scientists, and application development and delivery (AD&D) pros will learn:
What is a distributed BI platform? How is it different from existing BI tools?
How to scale BI and visual analytics for users without moving data
What features matter most for distributed BI platforms for Hadoop
How to unify security natively in Hadoop without more administration
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
Emerging opportunities in the age of dataEjaz Siddiqui
We live in a data-driven world. There are more than 4 billion people around the world using the internet.
This show an unprecedented spread and growth of digital devices. These digital devices (Mobiles, Computers, Watches, IoT etc) are the factories for creating data. It means we live in the Age of Data, and it’s expanding at astonishing rates. We may need to unplug and take a break from time to time, but data never sleeps.
This generation of huge data presents many new challenges as well as opportunities. There would be huge opportunity for the people who could collect, process, manage, drive insights and make useful decisions from this data. Certain fields are becoming very important and necessary to manage and process this data.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Conflict in the Cloud – Issues & Solutions for Big DataHalo BI
Halo BI CEO, Keith Peterson, presents at the 6th Annual Cloud Computing Conference - AITP San Diego: Conflict in the Cloud – Issues & Solutions for Big Data.
Cloud services make money based on the volume of data stored in the cloud – and big data delivers that volume. But companies seeking to use big data are looking for economies of scale from the Cloud.
How to Scale BI and Analytics with Hadoop-based PlatformsArcadia Data
You’re using Apache Hadoop and cloud-based data platforms, but can your BI and analytics tools keep up? Can you provide fast, secure, self-service access to all the data business users want?
Analyzing big data poses multiple challenges. Highly parallel distributed data architecture is one solution, but until recently it has been mostly limited to databases, not business intelligence (BI) application servers.
Join this informative webinar with guest speaker Boris Evelson, VP and principal analyst at Forrester Research, and Priyank Patel, co-founder and chief product officer at Arcadia Data. Enterprise architects, data scientists, and application development and delivery (AD&D) pros will learn:
What is a distributed BI platform? How is it different from existing BI tools?
How to scale BI and visual analytics for users without moving data
What features matter most for distributed BI platforms for Hadoop
How to unify security natively in Hadoop without more administration
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
Emerging opportunities in the age of dataEjaz Siddiqui
We live in a data-driven world. There are more than 4 billion people around the world using the internet.
This show an unprecedented spread and growth of digital devices. These digital devices (Mobiles, Computers, Watches, IoT etc) are the factories for creating data. It means we live in the Age of Data, and it’s expanding at astonishing rates. We may need to unplug and take a break from time to time, but data never sleeps.
This generation of huge data presents many new challenges as well as opportunities. There would be huge opportunity for the people who could collect, process, manage, drive insights and make useful decisions from this data. Certain fields are becoming very important and necessary to manage and process this data.
Tweet alert - semantic analysis in social networks for citizen opinion miningSngular Meaning
Description of a configurable, real-time system for automatic record, analysis and visualization of information from user interactions in Twitter. The system is designed to provide public bodies (government agencies) with a powerful tool to rapidly and easily understand what the citizen behavior trends are, what their opinion about city services, events, etc. is, and also may be used as a primary alert system to improve the efficiency of emergency systems. The citizen is here observed as a proactive city sensor capable of generating huge amounts of very rich, high-level and valuable data through social media platforms, which, after properly processed, summarized and annotated, allows city officers to better understand citizen needs. The architecture and component blocks are described and some key details of the design, implementation and scenarios of application are discussed. Textalytics APIS are used for the semantic analysis of relevant tweets.
Presentation by DAEDALUS, UPM and UC3M at PEGOV 2014, 2nd International Workshop on Personalization in eGovernment Services and Applications, Aalborg, Denmark, in conjunction with the 22nd Conference on User Modeling, Adaptation and Personalization - UMAP 2014.
Demo or Die: Where advertising meets product designChristine Outram
This presentation explores the role of rapid prototyping in the age of digital advertising and how it is transforming a "traditional creative process" into a lean, interactive, and multidisciplinary endeavor. Advertising is evolving; the best ads are not always ads; demo or die.
An overview of traditional spatial analysis tools, an intro to hadoop and other tools for analyzing terabytes or more of data, and then a primer with examples on combining the two with data pulled from the Twitter streaming API. Given at the O'Reilly Where 2.0 conference in March 2010.
Social media data for Social science researchDavide Bennato
This is the talk I gave at the Lipari Summer School on Computational social science 2013. What are relationship between social science and big data? With a focus on Twitter and its social media mining tools
http://www.tecnoetica.it/2013/08/07/lipari-summer-school-computational-social-science-big-data-e-twitter/
With the tremendous growth of social networks, there has been a growth in the amount of new data that is being created every minute on these networking sites. The notion of community in this social networking world has caught lots of attention. Studying Twitter is useful for understanding how people use new communication technologies to form social connections and maintain existing ones. We analysed how geo-tagged tweets in Twitter can be used to identify useful user features and behavior as well as identify landmarks/places of interests. We also analysed several clustering algorithms and proposed different similarity measures to detect communities.
Twitter Text Mining with Web scraping, R, Shiny and Hadoop - Richard Sheng Richard Sheng
Based on an Analytics Week article of the Top 200 Influencers in Big Data and Analytics, I used R and Hadoop to analyze the Twitter Feeds of these leaders with Text Mining, Web Scraping and Visualization techniques.
Hadoop, Pig, and Twitter (NoSQL East 2009)Kevin Weil
A talk on the use of Hadoop and Pig inside Twitter, focusing on the flexibility and simplicity of Pig, and the benefits of that for solving real-world big data problems.
Data Virtualization - Enabling Next Generation AnalyticsDenodo
Watch full webinar here: https://goo.gl/3gNMXX
Webinar featuring guest speaker Boris Evelson, Vice President, Principal Analyst at Forrester Research and Lakshmi Randall, Director of Product Marketing, Denodo.
Majority of enterprises today are data-aware. Being data-aware, or even data-driven, however, is not enough. Are your data-driven applications providing contextual and actionable insight? Are your analytics applications driving tangible business outcomes? Are you deriving insights from all the enterprise data? Enter Systems Of Insight (SOI), Forrester's latest analytical framework for insights-driven businesses.
In this webinar you will learn about the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. Specifically the webinar will explore roles data virtualization (aka Data Fabric) plays in modern SOI architectures such as:
• A single virtual catalog / view on all enterprise data sources including data lakes.
• A more agile and flexible virtual enterprise data warehouse.
• A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric).
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries J...Kevin Pledge
Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
Delivering Analytics at The Speed of Transactions with Data FabricDenodo
Watch full webinar here: https://bit.ly/3aAMTDD
It is no more an argument that data is the most critical asset for any business to succeed. While 85% of organizations want to improve their use of data insights in their decision making, according to a Forrester Survey, 91% of the respondents report that improving the use of data insights in decision making is challenging. To make data driven decision, organizations often turn to the data lakes, data lakehouses, cloud data warehouse etc. as their single source data repository. But the hard reality is that data is and will be spread across various repositories across cloud and regional boundaries.
Learn from renowned Forrester analyst and VP at Forrester, Noel Yuhanna:
- Why Data Fabric Is the best way to unify distributed data
- How Data Fabric be leveraged for data discovery, predictive analytics, data science and more
- Why data virtualization technology is key in building an Enterprise Data Fabric
As new technologies emerge, it can be difficult to identify the benefits of the many different options available. In an effort to understand the NOSQL options better, specifically graph databases, Objectivity, Inc. has formed an internal Performance Center to evaluate the features, performance and functionality of different graph database solutions that are available today. This webinar will focus on understanding the complementary nature, use cases and value of graph databases for “Big Data” solutions. Please join us with guest speaker Noel Yuhanna, Principal Analyst serving Enterprise Architecture Professionals, Forrester Research Inc, for an overview of the NOSQL market and Brian Clark, Vice President Objectivity, presenting an overview of initial Performance Center Findings.
Guest Speaker:
Noel Yuhanna
Principal Analyst serving Enterprise Architecture Professionals, Forrester Research, Inc.
Noel serves Enterprise Architecture Professionals. He primarily covers database management systems (DBMSes), infrastructure-as-a-service (IaaS), data replication and integration, data security, data management tools, and related online transaction processing issues. His current primary research focus is on customer usage experiences and broad industry trends of DBMS, IaaS, data security, enterprise data grids, outsourcing, information life-cycle management, open source databases, and other emerging database technologies.
Presenter:
Brian Clark
Corporate Vice President, Objectivity
Brian Clark has nearly 30 years of software and technology experience, and was one of the early architects of Objectivity/DB. Before joining Objectivity, Brian worked at Automation Technology Products, providing leading tools in the MCAD market. Prior to that, he was with Project Management Services at International Computers Limited, one of Europe’s leading computer companies at the time. Brian holds a B.S
View the webinar at: https://attendee.gotowebinar.com/recording/5730303120063488770
Accelerating Fast Data Strategy with Data VirtualizationDenodo
"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 201...Edgar Alejandro Villegas
Presentation slides of:
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 2013 - PDF
Scott Mackenzie - Sr. Director, Platform & Analytics CoE
Michael Golzc - CIO for SAP Americas
Ken Demma - VP, Insight Driven Marketing
20 Aug 2013 - Webcast - http://goo.gl/T74WAL
Learn All about Data Science from the Best Private University in KarnatakaREVA University
Completing Masters in Data Science degree can reshape your career path, though it demands dedication and time to gain the necessary skills and land the right job. To assist you, we've crafted a detailed plan for building a career in Data Science.
The objective of this module is to provide an overview of what the future impacts of big data are likely to be.
Upon completion of this module you will:
Gain valuable insight into the predictions for the future of Big Data
Be better placed to recognise some of the trends that are emerging
Acquire an overview of the possible opportunities your business can have with Big Data
Understand some of the start up challenges you might have with Big Data
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
The 2014 IDG Enterprise Big Data research was completed with the goal of gaining a better understanding of organizations’ big data initiatives, investments and strategies. Additional information on the research can be found here: http://bit.ly/1cW0wWR.
Intel Big Data Analysis Peer Research Slideshare 2013Intel IT Center
This PowerPoint presentation provides insights into results of a 2013 survey about big data analytics, including a comparison to 2012 big data survey results.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Similar to Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester (20)
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It provides an end-to-end platform that can collect, curate, analyze, and act on data in real-time, on-premises, or in the cloud with a drag-and-drop visual interface. It’s being used across industries on large amounts of data that had stored in isolation which made collaboration and analysis difficult.
Join industry experts from Hortonworks and Attunity as they explain how Apache NiFi and streaming CDC technology provides a distributed, resilient platform for unlocking the value of data in new ways.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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
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/
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester
1. Big Data – What’s Hype, What’s
Reality?
Martha Bennett
Principal Analyst
Hortonworks Breakfast Seminar
London, July 9th, 2013
A review of trends and developments
3. Firms recognize the importance of data . . .
13%
17%
18%
18%
19%
19%
20%
26%
27%
27%
28%
32%
37%
4%
6%
6%
7%
7%
8%
7%
7%
8%
8%
9%
11%
18%
Implement a bring-your-own PC, smartphone, and/or tablet strategy
Create a comprehensive mobile and tablet strategy for employees
Shift spending from core systems to applications driving
engagement with customers
Create a comprehensive cloud strategy
Develop smart product APIs that improve product & service
capabilities
Create a comprehensive mobile and tablet strategy for customers or
business partners
Cut overall IT costs due to economic conditions
Reorganize or retrain IT to better align with business outcomes and
drive innovation
Help the organization better manage and integrate its partners and
suppliers
Improve IT budget performance
Develop new skills to better support emerging technologies and
business innovation
Improve IT project delivery performance
Improve the use of data and analytics to improve business decisions
and outcomes
High priority
Critical priority
Source: Forrsights Business Decision-Makers Survey, Q4 2012
Base: 3,616 business decision-makers from firms with 100 or more employees
4. 1
2
3
4
5
6
7
. . . and BI is a top investment priority . . .
The top seven software applications in firms’ adoption plans by year
Source: Enterprise and SMB Software Survey, North American And Europe, Q3 2007; Enterprise And SMB Software Survey, North America And Europe,
Q4, 2008; Enterprise And SMB Software Survey, North American And Europe, Q4 2009; Forrsights Software Survey, Q4 2010; Forrsights Software Survey,
Q4 2011; and Forrsights Software Survey, Q4 2012
Note: We first included industry-specific software in the Q4 2008 survey; we first included finance and accounting in the Q4 2009 survey.
2008
(N = 1,158)
2009
(N = 1,021)
2010
(N = 455)
2011
(N = 913)
2012
(N = 1,092)
2013
(N = 1,631)
Source: May 27, 2011 , “Forrsights: The Software Market In Transformation, 2011 And Beyond” Forrester report
Business intelligence
Customer relationship management
Collaboration software
Finance & accounting
Industry-specific software
Enterprise resource planning
Human capital management
5. . . . but they don’t use most of their data
Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012
Unstructured
50TB
Semi-
structured
2 TB
Structured
12 TB
Utilized
12%
Average data volume
per company
9 TB 75 TB
0.6 TB 5 TB
4 TB 50 TB
SMBs: LEs:
Base: 634 business intelligence users and planners
10. “Big data” is:
Techniques and technologies
that make handling data at
extreme scale affordable.
Several different
technologies!
Many different use
cases!
Extreme in different
dimensions!
15. 7% 13% 7% 17% 31%
Implemented, not expanding Expanding/upgrading implementation
Planning to implement in the next 12 months Planning to implement in more than 1 year
Interested but no plans
Base: 634 business intelligence users and planners
“What best describes your firm's current usage/plans to adopt big data technologies and solutions?”
Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012
Big data analytics is growing quickly
20% have
implemented
some big data
technology
37% are planning a big data
technology project in 2013 or beyond
16. Production Logistics Sales ServiceSourcing
(Singapore bank)
high-performance risk
analysis [SAS]. 45,000 instruments
with 100,000 parameters: 8.8 billion
risks analyzed in less than 1minute
(down from 18 hours), aggregated risk
portfolio. Upfront strategy evaluation.
(Retailer) price
optimization [SAS].
Based on sales and competition -> 270
million price calculations in less than 2
hours (down from 30 hours); now
several price changes per day.
(Telecom) churn/
loyalty management
[HP]. Call analysis (more than 500
million/day) combined with social media
analysis to assign risk scores to
business lines and individual
customers.
(Bus service) carrier
service optimization
[Fujitsu]. 200,000 input/output
operations/second. Response <1 ms:
status, position, ETA, consumption, co
mpliance -> all real-time
(Semiconductor)
manufacturing
optimization [Exasol]. 5 billion data
points for production
processes, material, movements, produ
ct per production cycle ->
monitoring, archiving, comparison, opti
mization.
(Retailer) workforce
scheduling and
optimization [Blue-
Yonder]. Predictive analysis (450,000
/week) based on sales, weather, traffic ->
improved employee/customer satisfaction
(Retailer) inventory
optimization
[BlueYonder] Based on weekly sales
forecast (135 GB), 300 million data sets
(sales, campaigns, products), improved
forecast 40% (1 billion/year), real-time
Royal Tech Institute
Stockholm [IBM]
optimized traffic
management. Real-time
250,000 GPS/s (signals) -> 20% less
traffic/emissions, 50% shorter trips
High-performance
computing for drilling
site evaluation [IBM,
summer 2010]. 50 TB
per survey. Increased success rate
from 1 in 5 to 1 in 3.