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.
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
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.
Big data is delivering significant value to organizations that complete projects according to a survey. The vast majority (92%) of users are satisfied with business outcomes and feel their implementation meets needs. Larger companies see big data as more important and are more likely to benefit from initial implementations. While talent shortage poses challenges, successful users leverage external resources. Users see big data as disruptive and potentially transformational, with 89% believing it will revolutionize business as the internet did.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The document discusses how big data is transforming purchasing and supply chain management. It notes that the volume, velocity, and variety of available data is immense and growing exponentially. By 2020, 40% of all data may come from internet-connected sensors. This data can provide new insights if analyzed properly using basic tools like Microsoft Office or specialized big data analytics platforms. The document recommends that purchasing professionals start small by analyzing internal supplier spend data to better understand spending patterns and identify areas for improvement and cost savings. If value is found, more advanced big data tools could be pursued. Overall, big data is leading to a transformation in purchasing where skills in categories, data, value creation, and system integration will be most important.
Webinar | Using Big Data and Predictive Analytics to Empower Distribution and...NICSA
With the proliferation of Big Data-oriented technology and its accompanying applications of advanced statistical techniques, asset managers are enabling their sales and marketing teams with more insight into the preferences and proclivities of their clients, both advisors and investors. This webinar will give attendees a general understanding of Big Data’s technologies and techniques especially as they pertain to using predictive analytics for more effective and targeted marketing and distribution.
Desired Outcomes:
Understanding Big Data and how it is enabling adopters to use data more effectively than in the past
Familiarity with some of the technological and analytical approaches Big Data enables
Understanding of attribution models for measuring advisor and investor responsiveness
Knowledge of how to prioritize campaigns and contacts by combining measures of valuation and responsiveness
Grasp of some of the more effective way to adopt predictive analysis for sales and marketing
Understanding basics of recommender systems and how next best action is determined
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
As per the PfMP Certification, it is critical to keep track of project progress in order to keep the timetable on track. Six elements included in comprehensive project reports are mentioned here.
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.
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
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.
Big data is delivering significant value to organizations that complete projects according to a survey. The vast majority (92%) of users are satisfied with business outcomes and feel their implementation meets needs. Larger companies see big data as more important and are more likely to benefit from initial implementations. While talent shortage poses challenges, successful users leverage external resources. Users see big data as disruptive and potentially transformational, with 89% believing it will revolutionize business as the internet did.
Big Data Impact on Purchasing and SCM - PASIA World Conference DiscussionBill Kohnen
The document discusses how big data is transforming purchasing and supply chain management. It notes that the volume, velocity, and variety of available data is immense and growing exponentially. By 2020, 40% of all data may come from internet-connected sensors. This data can provide new insights if analyzed properly using basic tools like Microsoft Office or specialized big data analytics platforms. The document recommends that purchasing professionals start small by analyzing internal supplier spend data to better understand spending patterns and identify areas for improvement and cost savings. If value is found, more advanced big data tools could be pursued. Overall, big data is leading to a transformation in purchasing where skills in categories, data, value creation, and system integration will be most important.
Webinar | Using Big Data and Predictive Analytics to Empower Distribution and...NICSA
With the proliferation of Big Data-oriented technology and its accompanying applications of advanced statistical techniques, asset managers are enabling their sales and marketing teams with more insight into the preferences and proclivities of their clients, both advisors and investors. This webinar will give attendees a general understanding of Big Data’s technologies and techniques especially as they pertain to using predictive analytics for more effective and targeted marketing and distribution.
Desired Outcomes:
Understanding Big Data and how it is enabling adopters to use data more effectively than in the past
Familiarity with some of the technological and analytical approaches Big Data enables
Understanding of attribution models for measuring advisor and investor responsiveness
Knowledge of how to prioritize campaigns and contacts by combining measures of valuation and responsiveness
Grasp of some of the more effective way to adopt predictive analysis for sales and marketing
Understanding basics of recommender systems and how next best action is determined
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
As per the PfMP Certification, it is critical to keep track of project progress in order to keep the timetable on track. Six elements included in comprehensive project reports are mentioned here.
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.
Evaluating Big Data Predictive Analytics PlatformsTeradata Aster
Mike Gualtieri, Principal Analyst, Forrester Research, presents at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Evaluating Big Data Predictive Analytics Platforms
Abstract: Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.
The Comparison of Big Data Strategies in Corporate EnvironmentIRJET Journal
The document discusses and compares different big data strategies that corporations can use to handle large volumes of data. It analyzes traditional relational database management systems (RDBMS), MapReduce techniques, and a hybrid approach. While each strategy has benefits, the hybrid approach that combines traditional databases and MapReduce is identified as being most valuable for companies pursuing business analytics, as it allows for efficiently handling both structured and unstructured data at large scales. The document provides an overview of these strategies and their suitability based on different corporate needs and environments.
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...Vasu S
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Smart Data Slides: Leverage the IOT to Build a Smart Data EcosystemDATAVERSITY
The document discusses leveraging the Internet of Things (IoT) and cognitive computing/artificial intelligence to build smart data ecosystems and strategies. It outlines how IoT and sensors can generate streaming data and the need for architectures to analyze live data alongside historical data. Porter's five forces model and SWOT analysis are presented as conventional strategic frameworks that must be reexamined. Finally, five ways cognitive/AI technologies combined with IoT data can help organizations better understand customers are described.
This document discusses the need for data unification across enterprises. It notes that while companies have invested trillions in IT and billions in big data and analytics, data remains extremely siloed within organizations. True big data and analytics require clean and unified data sources. The document advocates for a bottom-up, probabilistic and collaborative approach to data unification using machine learning combined with human insight and curation. This approach is needed to tackle the huge challenge of integrating and making sense of the massive variety of siloed data sources within large organizations. The document provides examples of how Tamr's data unification platform has helped large healthcare and biopharma companies achieve a unified view of their extremely diverse and decentralized data.
When Big Data and Predictive Analytics Collide: Visual Magic HappensChase McMichael
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive https://svforum.org/Business-Intelligence/Business-Intelligence-SIG-When-Big-Data-and-Predictive-Analytics-Collide SEE Dreamforce Content Hub in ACTION here http://blog.infinigraph.com/example-of-visual-content-trends-powered-by-hypercuration/
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses why 87% of data science projects fail to make it into production. It identifies three main reasons for failure: data is inaccurate, siloed and slow; there is a lack of business readiness; and operationalization is unreachable. To address these issues, the document recommends establishing data governance, defining an organizational data science strategy and use cases, ensuring the technology stack is updated, and having data scientists collaborate with data engineers. It also provides tips for successful data science projects, such as having short timelines, small focused teams, and prioritizing business problems over solutions.
Tamr | Strata hadoop 2014 Michael StonebrakerTamr_Inc
This document discusses three generations of data curation products and tools. Generation 1 involved traditional extract-transform-load (ETL) processes for data warehousing that scaled to around 25 data sources. Generation 2 added deduplication, outlier detection, and other tools to ETL but still had limited scalability. Generation 3 products like Tamr use machine learning, statistics, and human experts to automatically integrate schemas, perform deduplication, and clean data at much larger scales of thousands or tens of thousands of data sources. Tamr's architecture involves ingesting data, integrating schemas, sourcing human experts through a marketplace, deduplicating entities, data cleaning, and delivering results to data stores.
Gayatri Patel, eBay, presents at the Big Analytics 2012 Roadshow
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
This document provides information about actuaries and actuarial work. It discusses what actuaries do, including helping manage risks like insurance, pensions, and disasters. It also provides examples of actuarial work analyzing insurance pricing factors, investment returns, and life expectancies. The document aims to promote actuarial work and explain why becoming an actuary is an interesting career choice.
The document provides information on three career paths - actuary, accountant, and financial analyst - and lists three job positions for each career path with descriptions of their responsibilities, requirements, and other details. The actuary positions are in asset liability management, health insurance research, and an assistant role. The accountant positions include an audit analyst, staff accountant, and an accountant maintaining accounting systems. The financial analyst positions are in real estate operations analysis, preparing reports for a real estate group, and a learning-focused role in California. All require degrees in relevant fields like mathematics or accounting and some require professional exams or certifications.
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.
Evaluating Big Data Predictive Analytics PlatformsTeradata Aster
Mike Gualtieri, Principal Analyst, Forrester Research, presents at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Evaluating Big Data Predictive Analytics Platforms
Abstract: Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.
The Comparison of Big Data Strategies in Corporate EnvironmentIRJET Journal
The document discusses and compares different big data strategies that corporations can use to handle large volumes of data. It analyzes traditional relational database management systems (RDBMS), MapReduce techniques, and a hybrid approach. While each strategy has benefits, the hybrid approach that combines traditional databases and MapReduce is identified as being most valuable for companies pursuing business analytics, as it allows for efficiently handling both structured and unstructured data at large scales. The document provides an overview of these strategies and their suitability based on different corporate needs and environments.
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...Vasu S
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Smart Data Slides: Leverage the IOT to Build a Smart Data EcosystemDATAVERSITY
The document discusses leveraging the Internet of Things (IoT) and cognitive computing/artificial intelligence to build smart data ecosystems and strategies. It outlines how IoT and sensors can generate streaming data and the need for architectures to analyze live data alongside historical data. Porter's five forces model and SWOT analysis are presented as conventional strategic frameworks that must be reexamined. Finally, five ways cognitive/AI technologies combined with IoT data can help organizations better understand customers are described.
This document discusses the need for data unification across enterprises. It notes that while companies have invested trillions in IT and billions in big data and analytics, data remains extremely siloed within organizations. True big data and analytics require clean and unified data sources. The document advocates for a bottom-up, probabilistic and collaborative approach to data unification using machine learning combined with human insight and curation. This approach is needed to tackle the huge challenge of integrating and making sense of the massive variety of siloed data sources within large organizations. The document provides examples of how Tamr's data unification platform has helped large healthcare and biopharma companies achieve a unified view of their extremely diverse and decentralized data.
When Big Data and Predictive Analytics Collide: Visual Magic HappensChase McMichael
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive https://svforum.org/Business-Intelligence/Business-Intelligence-SIG-When-Big-Data-and-Predictive-Analytics-Collide SEE Dreamforce Content Hub in ACTION here http://blog.infinigraph.com/example-of-visual-content-trends-powered-by-hypercuration/
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses why 87% of data science projects fail to make it into production. It identifies three main reasons for failure: data is inaccurate, siloed and slow; there is a lack of business readiness; and operationalization is unreachable. To address these issues, the document recommends establishing data governance, defining an organizational data science strategy and use cases, ensuring the technology stack is updated, and having data scientists collaborate with data engineers. It also provides tips for successful data science projects, such as having short timelines, small focused teams, and prioritizing business problems over solutions.
Tamr | Strata hadoop 2014 Michael StonebrakerTamr_Inc
This document discusses three generations of data curation products and tools. Generation 1 involved traditional extract-transform-load (ETL) processes for data warehousing that scaled to around 25 data sources. Generation 2 added deduplication, outlier detection, and other tools to ETL but still had limited scalability. Generation 3 products like Tamr use machine learning, statistics, and human experts to automatically integrate schemas, perform deduplication, and clean data at much larger scales of thousands or tens of thousands of data sources. Tamr's architecture involves ingesting data, integrating schemas, sourcing human experts through a marketplace, deduplicating entities, data cleaning, and delivering results to data stores.
Gayatri Patel, eBay, presents at the Big Analytics 2012 Roadshow
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
This document provides information about actuaries and actuarial work. It discusses what actuaries do, including helping manage risks like insurance, pensions, and disasters. It also provides examples of actuarial work analyzing insurance pricing factors, investment returns, and life expectancies. The document aims to promote actuarial work and explain why becoming an actuary is an interesting career choice.
The document provides information on three career paths - actuary, accountant, and financial analyst - and lists three job positions for each career path with descriptions of their responsibilities, requirements, and other details. The actuary positions are in asset liability management, health insurance research, and an assistant role. The accountant positions include an audit analyst, staff accountant, and an accountant maintaining accounting systems. The financial analyst positions are in real estate operations analysis, preparing reports for a real estate group, and a learning-focused role in California. All require degrees in relevant fields like mathematics or accounting and some require professional exams or certifications.
Actuaries use mathematics and statistics to analyze financial risks and uncertain future events. They study how risk affects insurance premiums, pension plans, and other financial areas. Actuaries work in insurance companies, consulting firms, and other businesses to help manage risks and financial decisions.
This document discusses careers as an actuary. It notes that actuarial work involves statistical techniques, product design, investments, underwriting, and medical knowledge. Those interested can qualify through exam bodies in various countries. Career paths include life insurance companies, general insurance companies, and consultancies. The responsibilities of an appointed actuary include ensuring long-term solvency and balancing objectives of shareholders and policyholders. Employers seek graduates with strong analytical skills, an understanding of actuarial concepts, initiative, and a range of interests.
PYA Principal Shannon Sumner co-presented “Enterprise Risk Management” at the HCCA Board Audit Committee Compliance Conference, February 27-28, 2017, in Scottsdale, Arizona.
The presentation covered:
The role of the governing Board of an organization in enterprise risk management (ERM)
Effective ERM in today’s healthcare setting
When ERM fails: “The perfect storm”
The document discusses operational risk and provides guidance on defining, identifying, measuring, monitoring, controlling, and mitigating operational risk according to the Basel Committee on Banking Supervision. It addresses issues with operational risk loss data and outlines principles for developing an appropriate operational risk management environment, process, and framework. The document also examines challenges with using internal and external loss data for quantifying operational risk capital requirements.
The document provides a feasibility report for a catering service business. It discusses the history of catering, types of catering establishments and services offered. A PEST analysis is included covering political, economic, social and technological factors. A SWOT analysis identifies strengths, weaknesses, opportunities and threats. Details are given on raw materials, equipment, production process, packaging, quality standards, capacity and sales projections. A market analysis covers customer preferences and acceptance levels. The 7Ps of marketing are outlined covering product, price, place, promotion, process, physical evidence and people.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
Business analytics uses quantitative processes to help businesses make optimal decisions and discover business insights from data. It includes statistics, prediction, and optimization. Big data refers to extremely large data sets that are too large to be processed with traditional data processing applications. The combination of business analytics and big data allows businesses to gain insights from large, diverse data sets to improve processes, make predictions, optimize performance, and support decision making through techniques like predictive modeling, forecasting, and response analysis. Examples include a bank using a Hadoop cluster to build a more accurate risk score to better manage customer portfolios.
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
What is big data?
Big data is a mix of structured, semi-structured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects, predictive modeling, and other advanced analytics applications.
Systems that process and store big data have turned into a typical part of data the board architectures in organizations, joined with tools that support big data analytics uses. Big data is regularly portrayed by the three V's:
the enormous volume of data in numerous environments; • the wide variety of data types regularly stored in big data systems, and
the velocity at which a significant part of the data is created, gathered and processed.
These characteristics were first recognized in 2001 by Doug Laney, then, at that point, an analyst at consulting firm Meta Group Inc.; Gartner further promoted them after it gained Meta Group in 2005. All the more as of late, several other V's have been added to various descriptions of big data, including veracity, value and variability.
Albeit big data doesn't liken to a specific volume of data, big data deployments frequently involve terabytes, petabytes, and even exabytes of data made and gathered over time.
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.
This document discusses big data analytics projects and some of the challenges involved. It notes that while gaining insights from big data is desirable, it is difficult to do due to the volume, variety and velocity of data, as well as complexity. The document provides advice on questions businesses should consider when developing a big data analytics strategy and system, such as data timeliness, interrelatedness of data sources, historical data needs, and vendor experience. Understanding these issues is key to identifying the right technology to support a big data analytics initiative.
Practical analytics john enoch white paperJohn Enoch
This document discusses using data analytics to provide value to businesses. It recommends starting with smaller, more manageable data sets and business intelligence (BI) projects that have clear goals and can yield quick wins, like analyzing travel costs. While big data holds promise, the author advises focusing first on consolidating existing data that is stuck in silos and using BI to improve processes and save costs in areas employees already know need improvement. Starting small builds skills for larger initiatives and ensures analytics provides practical benefits.
Big data is a mix of structured, semistructured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects,
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
March Towards Big Data - Big Data Implementation, Migration, Ingestion, Manag...Experfy
Gartner, IBM, Accenture and many others have asserted that 80% or more of the world’s information is unstructured – and inherently hard to analyze. What does that mean? And what is required to extract insight from unstructured data?
Unstructured data is infinitely variable in quality and format, because it is produced by humans who can be fastidious, unpredictable, ill-informed, or even cynical, but always unique, not standard in any way. Recent advances in natural language processing provides the notion that unstructured content can be included in data analysis.
Serious growth and value companies are committed to data. The exponential growth of Big Data has posed major challenges in data governance and data analysis. Good data governance is pivotal for business growth.
Therefore, it is of paramount importance to slice and dice Big Data that addresses data governance and data analysis issues. In order to support high quality business decision making, it is important to fully harness the potential of Big Data by implementing proper Data Migration, Data Ingestion, Data Management, Data Analysis, Data Visualization and Data Virtualization tools.
Check it out: https://www.experfy.com/training/courses/march-towards-big-data-big-data-implementation-migration-ingestion-management-visualization
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
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.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
The document outlines an agenda for a presentation on big data. It discusses key topics like the state of big data adoption, a holistic approach to big data, five high value use cases, technical components, and the future of big data and cloud. The presentation aims to provide an overview of big data and how organizations can take a comprehensive approach to leveraging their data assets.
This document discusses best practices for big data analytics projects. It begins by defining big data and explaining that while gaining insights from large and diverse data sets is desirable, operationalizing big data analytics can be complex. It emphasizes understanding an organization's unique needs and challenges before selecting technologies. The document also explores how in-memory processing can help speed up analysis by reducing data transfer times, but only if the insights are integrated into decision-making processes.
This document provides an introduction to big data including:
- An overview of what big data is and the challenges it presents in terms of capture, curation, storage, search, sharing, transfer, analysis and visualization of large, complex datasets.
- The 3Vs of big data - volume, velocity and variety - and examples of the scale of data being generated every day from sources like social media, sensors and scientific instruments.
- The technologies and architectural approaches needed to harness big data including Hadoop, Spark, data warehouses, graph databases, and cloud computing platforms.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
This document discusses strategies for improving life insurance underwriting processes. It outlines traditional multi-step underwriting stacks that can take over a month versus accelerated processes using new analytics that can provide results in minutes. The document advocates using behavioral science to improve how medical questions are asked. It also explores using new data sources like credit scores, facial analytics, and marketing data to supplement underwriting decisions. The goal is to streamline underwriting while still accurately assessing risk to provide more immediate insurance coverage at a lower cost and with a better customer experience.
Challenges and Opportunities by Steve FretwellKevin Pledge
The document discusses some of the key challenges and opportunities for Acceptiv Inc. in developing their online business. Some challenges mentioned include managing channel conflicts between direct-to-consumer and agent sales channels, navigating different regulatory considerations across provinces, and overcoming challenges with cross-selling different products that have separate underwriting. Opportunities include leveraging lessons learned from other successful online retailers, using customer data to provide more personalized value-added services, and standing out from competitors by optimizing the customer experience to make the online purchasing process quick and easy.
Introduction to Online Distribution Conference by Alan RyderKevin Pledge
The document discusses the trend of declining life insurance sales to the middle market in North America and the opportunity for online distribution to address this issue. It notes that younger buyers prefer online buying and self-service, while traditional distribution systems have become ineffective. It also outlines some of the challenges of attracting prospects, selling life insurance online, and integrating the sales and underwriting processes to allow for point-of-sale underwriting through an online system. The document proposes that online distribution can increase accessibility of life insurance in a cost-effective, scalable manner by replacing high variable costs with modest fixed costs.
Digital Branding - acquisition through to transaction by Andrew BergstromKevin Pledge
Session 2 from Acceptiv Online Distribution Conference Sept 2017. Designing a front-end acquisition engine to pull prospective customers through to an online transaction platform. This presentation will identify how to build a customer acquisition engine front-end that is hyper-efficient, measurable and seamless through to an online transaction platform.
Teachers Life - an Online Case Study by Doug BakerKevin Pledge
Session 4 from Acceptiv Online Distribution Conference Sept 2017. Three years’ ago, Teachers Life moved to an exclusively digital strategy; learn from their experience, what they would do differently and why they feel they are in a much stronger position today to capitalize from early mover advantage.
Online Insurance - Value of PartnershipsKevin Pledge
Session 8 from Acceptiv Online Distribution Conference Sept 2017.
A successful online strategy means working with partners such as reinsurers, marketing consultants, hosting, software developers, underwriters, TPAs and behavior scientists. Understand how this ecosystem works together for all-round success.
Online Insurance - Challenges and OpportunitiesKevin Pledge
Session 5 from Acceptiv Online Distribution Conference Sept 2017. Breakout discussion covering challenges and opportunities in moving to an online distribution strategy
Session 6 from Acceptiv Online Distribution Conference Sept 2017.
A digital sales journey requires immediate underwriting decisions made by a machine. Learn how external data can add value and how to maximize immediate decisions without devaluing the underwriting quality.
The Experience of Online Distribution Elsewhere in the WorldKevin Pledge
Session 7 from Acceptiv Online Distribution Conference Sept 2017
Some say North America is at least 5 years behind the UK when it comes to online distribution. Is this true, and what lesson can we learn from the UK and other countries?
The document discusses innovation and the importance of vision and leadership in driving innovation. It uses examples of insurance companies - Beagle Street, Teachers Life, and a large insurer - to illustrate how their approaches to functions like product development, marketing, sales, and claims management differ based on the resources available. The key lesson is that vision and leadership are what make the difference between incremental improvements and true innovation. Leaders need a vision for meaningful change and the ability to guide others towards realizing that vision.
2015 SOA Annual Meeting - Beagle Street and Teachers LifeKevin Pledge
Presentation by Chris Samuel featuring two case studies - Beagle Street (UK) and Teachers Life (Canada), discussing how their approach from selling online is different from rest of the market and how they are making an impact.
2015 SOA Annual Meeting - Engaging the CustomerKevin Pledge
Presentation given by Chris Samuel on how the life insurance industry can do a better job of engaging our customers. The current practice when policyholders receive their policy is file and forget. Chris explains we can do better than this and gives examples of how insurance companies have found imaginative ways to better engage with their customers.
3 Simple Lessons from Other IndustriesKevin Pledge
This document outlines 3 lessons that the insurance industry can learn from other industries:
1. Go where the money is - the insurance industry should focus more on online distribution channels as that is where many customers now search for and buy insurance.
2. Disruption happens - new technologies and competitors will disrupt existing business models, as seen with online travel agents disrupting travel agents.
3. Question your paradigms - industries should challenge existing assumptions about how business needs to be done, as management consultants have done in questioning the purpose of prisons.
This document discusses trends towards online life insurance sales and strategies for insurance companies to embrace digital distribution. It notes consumers are increasingly researching insurance online and the average agent age is rising. Companies elsewhere like Beagle Street in the UK have achieved fully online underwriting in under 15 minutes at a fraction of traditional costs. The presenter argues the Canadian market represents opportunities for online growth, better customer experience and higher profits. He proposes a turn-key solution partnering with fraternals to provide online fulfillment, administration and specially designed digital products and underwriting.
This document discusses predictive modeling approaches for life insurance underwriting. It took a long time for predictive modeling to be applied to underwriting due to the conservative nature of life insurance and the time needed to see results. Now, more data and computing power are available. Approaches include replicating current underwriting decisions or directly modeling applicant mortality rates. Various data sources can be used, including internal, third party, and customer data. Issues in building the predictive model include how to develop and update the model over time. Companies must decide how to incorporate these approaches and start collecting relevant data.
American Fraternal Alliance Annual Meeting 2015Kevin Pledge
The document discusses the growing trend of online life insurance sales and argues that fraternal insurers should embrace online distribution to remain competitive. It notes that 85% of consumers now research insurance online and that the average agent age is 57, representing a disconnect with younger consumers. The document outlines Teachers Life's success in developing an online quoting, application, and purchase process that is faster, more scalable, and lower-cost than traditional methods. It proposes that Acceptiv and Teachers Life provide a turnkey solution for fraternals to launch online sales, including products, underwriting, administration and customer support.
2015 CIA Annual Meeting - Middle Market GapKevin Pledge
The document discusses the potential for online sales of personal life insurance policies within the next 5 years. It notes that digital disruption has changed customer expectations and behavior in other industries. Research suggests customers are open to purchasing life insurance online and find the traditional agent-based model inconvenient. The document examines approaches taken by online insurers in other markets and whether a similar model could work in North America. It proposes an online life insurance offering with streamlined quoting, application and underwriting processes to provide faster, more affordable coverage directly to customers.
Presentation for the 2015 Life and Annuity Symposium in NY.
This presentation discusses the potential for life insurance to be disrupted by online distribution.
Global Innovation - Distribution lesson from overseasKevin Pledge
There is an estimated unmet need for $1 Trillion additional life insurance in Canada. Small evolutionary changes in our market are unlikely to materially reduce this gap.
What can we learn from overseas?
It is clear that calling on customers one at a time is not working, as there is an estimated unmet need for $1T additional life insurance in Canada, and $20T in the US. The average age of an insurance advisor is 57 and it takes a shocking average of 47 days to issue life insurance. Kevin Pledge has disrupted the status quo in the companies that he worked for and has founded several companies providing innovative solutions. His latest venture aims to make life insurance accessible to the middle market. This session will discusses what innovation is and how insurance in North America can be transformed by selling online.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
7. Business Intelligence
Business intelligence (BI) is a set of theories, methodologies,
processes, architectures, and technologies that transform
raw data into meaningful and useful information for business purposes. BI can
handle large amounts of information to help identify and develop new
opportunities. Making use of new opportunities and implementing an
effective strategy can provide a competitive market advantage and long-term
stability.
BI technologies provide historical, current and predictive views of business
operations. Common functions of business intelligence technologies are
reporting, online analytical processing, analytics, data mining, process mining,
complex event processing, business performance management,
benchmarking, text mining, predictive analytics and prescriptive analytics.
Though the term business intelligence is sometimes a synonym for
competitive intelligence (because they both support decision making), BI uses
technologies, processes, and applications to analyze mostly internal,
structured data and business processes while competitive intelligence
gathers, analyzes and disseminates information with a topical focus on
company competitors. If understood broadly, business intelligence can
include the subset of competitive intelligence
9. Data
Warehouse
Extraction, Transformation
and Loading (ETL)
Metadata
(data about data) Online Analytical
Processing (OLAP)
Source Systems
End User
(there are other forms)
Typical BI StructureTypical BI Architecture
12. Business Intelligence
you don’t know what you don’t know
there may not be a single version of the truth
retrospective changes messy if possible at all
designed seriatim aggregations
hard to keep up to date
15. Big Data
Big data is a collection of data sets so large and complex that it becomes difficult
to process using on-hand database management tools or traditional data processing
applications. The challenges include capture, curation, storage, search, sharing, transfer,
analysis, and visualization. The trend to larger data sets is due to the additional information derivable from
analysis of a single large set of related data, as compared to separate smaller sets with the same total
amount of data, allowing correlations to be found to "spot business trends, determine quality of research,
prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.
As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on
the order of exabytes of data.[ Scientists regularly encounter limitations due to large data sets in many areas,
including meteorology, genomics, connectomics, complex physics simulations, and biological and
environmental research. The limitations also affect Internet search, finance and business informatics. Data
sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing
mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-
frequency identification readers, and wireless sensor networks. The world's technological per-capita capacity
to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day
2.5 quintillion (2.5×1018) bytes of data were created. The challenge for large enterprises is determining who
should own big data initiatives that straddle the entire organization.
Big data is difficult to work with using most relational database management systems and desktop statistics
and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even
thousands of servers”. What is considered "big data" varies depending on the capabilities of the organization
managing the set, and on the capabilities of the applications that are traditionally used to process and
analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first
time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of
terabytes before data size becomes a significant consideration."
19. Descriptive Title
Quantitative
Sophistication/Numeracy
Sample Roles
Data Scientist or
Quantitative Analyst
Advanced Math/Stat
Internal expert in statistical and
mathematical modelling and
development, with solid business
domain knowledge.
Business Intelligence /
Operational Analytics
Good business domain,
background in statistics
optional
Running and managing analytical
models. Application of traditional
methods such as experience studies.
Business Intelligence/ Reporting
Data and numbers oriented,
but no special advanced
statistical skills
Reporting, dashboard, OLAP and
visualization, some design, posterior
analysis of results from quantitative
methods. Spreadsheets, “business
discovery tools”
Analytic Types
Types of Analysis
Type V
20. Data Scientist Job Description
• Passion for “playing” with tons of data and supporting scientific experiments to
validate the performance of algorithms
• Advanced degree in Statistics or related area
• Experience with traditional as well as modern statistical learning techniques,
including: Support Vector Machines; Regularization Techniques; Boosting, Random
Forests, and other Ensemble Methods.
• Strong computer science skills with high-level languages, such as R, Python, Perl,
Ruby, Scala or similar scripting languages.
• Experience with Hadoop and working with multi-terabyte systems.
• Extensive hands on experience working with very large data sets, including
statistical analyses, data visualization, data mining, and data
cleansing/transformation.
• Business expertise and entrepreneurial inclination to discover novel opportunities
for applying analytical techniques to business/scientific problems across the
company.
• Good communication ability
24. Opportunities
Actuaries already have:
Most the statistical skills
Some computer skills
Business expertise
Communication skills
Significant job growth in analytics is predicted
Reputation for actuaries in analytics can be enhanced
However… we have competition
25. U.S. Department of Labor
occupations forecasted for growth in analytics
Job Titles Expected
Growth by
2018
Total #
Expected
Projected
Median
Income
Top 10%
Income
Librarians 8% 172,400 $52,530 $81,130
Accountants/Auditors 22% 1,570,000 $59,430 $102,380
Statisticians 13% 25,500 $72,610 $117,190
Ops Research Analysts 22% 76,900 $69,000 $118,130
Management Analysts 24% 925,200 $73,570 $133,850
Actuaries 21% 23,900 $84,810 >$160,780
27. “Required” Skills/Techniques
Traditional Statistical Techniques
Ordinary Least Squares
Logistic Regression
Generalized Linear Model
Time Series
Methods That Group/Organize
Trees/Clustering
Prep for Analysis
Model Validation
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Alternatives
This is an oxymoron, but an early driver of BI was to establish a single set of data that would enable analytics.
can’t handle stochastic modelsInflexibleand it’s still just internal data(and just reporting)
Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
Data from GartnerBig data this year will account for US$28 billion of IT spending worldwide, which will increase to US$34 billion in 2013, according to Gartner.In a report released Wednesday, the market research firm said much of 2012 expenditure will be in adapting traditional tools to address issues related to the big data phenomenon such as machine data, social data, and the large variety and velocity of data. In contrast, only US$4.3 billion will be focused on new big data functionalities.Specifically, social network and content analysis had the most impact on big data budgets, and projected to account for 45 percent of new IT spending each year, Gartner said. Application infrastructure and middleware would account for 10 percent of yearly spend.Big data is not a distinct, standalone market, said Mark Beyer, research vice president at Gartner. Rather it represents an industry-wide market force addressed in products, practices and solution delivery, he explained.In 2011, big data was the new driver in almost every category of IT spending. Through to 2018, however, big data requirements will gradually evolve from being a differentiator to "table stakes" in information management, Beyer said.Elaborating, he said by 2020 big data features and functionalities will be non-differentiating and routinely expected from traditional enterprise vendors as part of their product offerings.By the end of 2015, Gartner said it expects leading organizations to begin using their big data knowledge in "an almost embedded form in their architectures and practices". And around the start of 2018, the distinction--and advantage--new big data products had over traditional offerings that provide additional functions to handle big data, will decrease.Skills, tools and practices leading companies acquired over the years to handle big data would eventually become routine flexibility, it added.Beyer said: "Because big data's effects are pervasive, big data will evolve to become a standardized requirement in leading information architectural practices, forcing older practices and technologies into early obsolescence."In other words, big data will end up as "just data" once again by 2020, and approaches toward architecture, infrastructure, hardware, and software that do not adapt to this "new normal" will be retired, he said.
How do they move up? Type Shifting: How Analysts can slip up to higher types and what organizations need to do to facilitate itThe go-to BI analysts are ready to move up to Type IIIType III analysts can be trained to be Type IIInternal training and mentoringExternal certification programs such as one being offered by the Society of Actuaries
Term invented by YahooWho is this data scientist. There is some confusion over the term. Some define it as just skilled in statistics' and programming, others include ability to communicate with the business as a result of having domain expertise. For the most part, the “data scientist” will probably be more a collaborative group than an individual. Exception: actuaries, who have always been data scientists, but many rise to senior position in the business. Domain expertise a prerequisite for fellowship. The training of actuaries is a good model for data scientists. The value in big data is analytics. Because, as we said, The data doesn’t speak for itself. But lets take a closer look at analyticsUsed to be called them quantsFew and far between
Team Members: Lisa Tourville (Chair) Joan Barrett Guillaume Briere-Giroux Jack Bruner Kara Clark Ian Duncan Kim DwornickAlice Kroll John Lloyd David McleroyKevin Pledge Jacques RiouxChris Stehno
Significant job growth in analytics is predicted, including management and leadership roles.
Meg – why we see opportunities for actuaries…The US Department of Labor predicts healthy growth in occupations that work in analytics. In addition, McKinsey (May 2011) predicts 1.5 million managers and advanced analysts needed by 2018. A recent review of open actuarial positions with a recruiter showed 15% of all openings had a preference for job seekers with proficiencies in one or more aspects of advanced business analytics, most often predictive modeling.But a survey (Fall 2011) of 55 life insurance companies by the SOA indicated life insurance companies were not using predictive modeling techniques in any widespread way but planned to in the near future…40% considering using predictive modeling to enhance sales and marketing50% considering using for underwriting1 company currently using predictive modeling for claimsKey Points:Actuaries are included in the job titles associated with job growth in analytics. There are many job titles associated with this growth (IT jobs, too).According to this chart, actuaries tend to be more highly compensated than our fellow professionals. Our stories from actual hiring managers conflict on this. Some employers pay non actuaries at the same rate. Some employers say they are hiring economists and “ninja librarians” because they cost less.
Actuaries as leaders, not the people running the models.
Many other skills and techniques were identified as part of our study but these made the top of the list. (Others – Survival/failure Time Analysis, Factor Analysis,
Technical sites like stack overflow
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.