Consider this: Data flows from every device, replacing guessing and approximations with precise information. Yet 80% of this data is unstructured; therefore, invisible to computers and of limited use to business.
Forrester Research evaluated 13 big data predictive analytics solution vendors. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing the appeal of predictive analytics for users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into software applications and business processes through APIs, web services, and other methods. The report provides an overview of the predictive analytics solutions market and evaluations of individual vendors.
Forrester Research evaluated 13 big data predictive analytics solutions providers based on 45 criteria. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing appeal for predictive analytics among users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into software applications and business processes through APIs, web services, and other methods. The report provides an overview of the predictive analytics solutions market and the evaluation criteria used to assess and score the vendors.
Forrester Research evaluated 13 big data predictive analytics solutions providers based on 45 criteria. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing appeal for users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into applications and business processes through APIs, web services, and other methods. Enterprises have many solid choices for big data predictive analytics solutions from the evaluated vendors.
Cognitive Era and Introduction to IBM WatsonSubhendu Dey
- The document introduces the cognitive era and IBM Watson. It discusses how exponential growth of data is affecting various sectors like healthcare, government, and media.
- It describes how IBM Watson is a cognitive system that uses natural language processing and builds on domain knowledge to understand language and derive answers from evidence.
- The foundational technologies behind Watson draw upon fields like big data analytics, artificial intelligence, cognitive experience, knowledge and computing infrastructure and include over 50 technologies like deep learning, machine learning, natural language processing and knowledge graphs.
Intelligent enterprise: Cognitive Business Presentation from World of WatsonNancy Pearson
How Companies Are Using Cognitive Computing to Drive Tangible Results including information from the 2016 Cognitive Advantage Report: http://www.ibm.com/cognitive/advantage-reports/
A presentation given in Denmark, introducing cognitive computing, highlighting potential benefits and early use-cases in insurance with IBM Watson. The presentation included demos.
Link to youtube video of FlexRate Insurers self-service demo: https://www.youtube.com/watch?v=8xRN9RzpVBE&spfreload=10
Link to IBM Watson white paper on Cognitive Computing in Insurance:
The document summarizes a Forrester research report on big data predictive analytics solutions. It finds that vendors must address the challenges of big data predictive analytics to help firms harness big data for predictive models to improve business outcomes. The market for big data predictive analytics is growing as more organizations seek to use these solutions. Key differentiators among vendors include their abilities to handle big data, provide easy-to-use modeling tools, and support a wide range of algorithms for structured and unstructured data.
The Intelligent Enterprise: How Companies are Using Cognitive Computing to Dr...Susanne Hupfer, Ph.D.
Presented by Susanne Hupfer and Nancy Pearson at IBM World of Watson Conference, Oct. 2016.
Wondering how and why forward-thinking businesses are already adopting cognitive computing and artificial intelligence technologies? Curious about the top business challenges organizations are tackling with cognitive computing? The "IBM Cognitive Study," which surveyed 600 leaders and decision-makers from around the world, provides answers to these questions and more. About 70% of decision-makers say that cognitive computing is extremely important to their business strategy and success. Learn how smart companies are becoming cognitive businesses, and how they're already driving tangible results and ROI.
Forrester Research evaluated 13 big data predictive analytics solution vendors. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing the appeal of predictive analytics for users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into software applications and business processes through APIs, web services, and other methods. The report provides an overview of the predictive analytics solutions market and evaluations of individual vendors.
Forrester Research evaluated 13 big data predictive analytics solutions providers based on 45 criteria. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing appeal for predictive analytics among users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into software applications and business processes through APIs, web services, and other methods. The report provides an overview of the predictive analytics solutions market and the evaluation criteria used to assess and score the vendors.
Forrester Research evaluated 13 big data predictive analytics solutions providers based on 45 criteria. They found three Leaders, eight Strong Performers, and two Contenders. Modern tools are lowering the barrier to entry and increasing appeal for users with less statistics skills. Predictive analytics has limited value unless the insights can be deployed directly into applications and business processes through APIs, web services, and other methods. Enterprises have many solid choices for big data predictive analytics solutions from the evaluated vendors.
Cognitive Era and Introduction to IBM WatsonSubhendu Dey
- The document introduces the cognitive era and IBM Watson. It discusses how exponential growth of data is affecting various sectors like healthcare, government, and media.
- It describes how IBM Watson is a cognitive system that uses natural language processing and builds on domain knowledge to understand language and derive answers from evidence.
- The foundational technologies behind Watson draw upon fields like big data analytics, artificial intelligence, cognitive experience, knowledge and computing infrastructure and include over 50 technologies like deep learning, machine learning, natural language processing and knowledge graphs.
Intelligent enterprise: Cognitive Business Presentation from World of WatsonNancy Pearson
How Companies Are Using Cognitive Computing to Drive Tangible Results including information from the 2016 Cognitive Advantage Report: http://www.ibm.com/cognitive/advantage-reports/
A presentation given in Denmark, introducing cognitive computing, highlighting potential benefits and early use-cases in insurance with IBM Watson. The presentation included demos.
Link to youtube video of FlexRate Insurers self-service demo: https://www.youtube.com/watch?v=8xRN9RzpVBE&spfreload=10
Link to IBM Watson white paper on Cognitive Computing in Insurance:
The document summarizes a Forrester research report on big data predictive analytics solutions. It finds that vendors must address the challenges of big data predictive analytics to help firms harness big data for predictive models to improve business outcomes. The market for big data predictive analytics is growing as more organizations seek to use these solutions. Key differentiators among vendors include their abilities to handle big data, provide easy-to-use modeling tools, and support a wide range of algorithms for structured and unstructured data.
The Intelligent Enterprise: How Companies are Using Cognitive Computing to Dr...Susanne Hupfer, Ph.D.
Presented by Susanne Hupfer and Nancy Pearson at IBM World of Watson Conference, Oct. 2016.
Wondering how and why forward-thinking businesses are already adopting cognitive computing and artificial intelligence technologies? Curious about the top business challenges organizations are tackling with cognitive computing? The "IBM Cognitive Study," which surveyed 600 leaders and decision-makers from around the world, provides answers to these questions and more. About 70% of decision-makers say that cognitive computing is extremely important to their business strategy and success. Learn how smart companies are becoming cognitive businesses, and how they're already driving tangible results and ROI.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
Intel, Cloudera and guest speaker Forrester Research, Inc. discuss the strategy of pervasive analytics and real life examples of how analytics have already been embedded into applications and workflows.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Talk presented at the Analytics Frontiers Conference in Charlotte on March 21. The presentation evaluates opportunities and risks of AI and how consumers, businesses, society and governments can mitigate some of the risks.
This document provides an overview of a research package on big data in the supply chain and logistics industry from eft. It includes:
- Interviews and presentations from industry experts on the biggest opportunities in using big data.
- A survey of over 200 supply chain executives on their use of big data.
- An article on using sensors and the "Internet of Things" to gain insights from real-time supply chain data.
The package explores how companies can leverage big data and analytics to improve visibility, flexibility, optimization, collaboration and control across their supply chains and gain competitive advantages. It highlights specific areas like risk management and predictive analytics that big data is enabling.
This document provides an overview of predictive analytics and its growing use in the insurance industry. It discusses key drivers for insurers' adoption of predictive analytics, including technological advances, data availability, seeking growth in slow-growth markets, and gaining competitive advantage. The document outlines how insurers use predictive analytics in marketing, underwriting, and claims to improve hit ratios, retention ratios, identify fraudulent claims, and prioritize claims processing. It provides details on the predictive analytic process of data mining, model development using regression and advanced models, and model validation. The advantages and disadvantages of predictive analytics for insurers are also discussed.
High Performance Computing and the Opportunity with Cognitive TechnologyIBM Watson
With the ability to reduce “time to insight” and accelerate research breakthroughs by providing immense computational power, high performance computing is becoming increasingly important in the marketplace. Meanwhile, cognitive technology has risen to prominence, similarly accelerating new insight, but through a very different approach - by analyzing previously ignored unstructured data, which accounts for 80% of new data created today.
By combining the powerful computing power of the HPC market, along with the machine learning, natural language processing, and even computer vision techniques found within cognitive technology, there is a huge opportunity to accelerate breakthroughs and enable better decision making than ever before.
Watch the replay of the webinar: https://www.youtube.com/watch?v=Hxgieboj3W0
Material for the 26 Oct 2015 lecture I held for Aalto University business students. The lecture focuses on the high level topics in analytics and Big Data that are either central to the subject or just highly visible in the media.
The main messages of the lecture are:
- The purpose of analytics and of the data analyst is to solve business problems
- Big Data brings over some very special traits to doing analytics that don't exist when working working with smaller datasets. Understanding these traits is a must for successful analytics.
- Deploying analytics is more dependent on humans than on technology
- Data and analytics are nowadays significant assets to many companies. Therefore they need their own strategy and need to be managed just like any other business critical assets.
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Staring with an brief overview of the changing role of the CIO between 2018 and 2020, then moving into the technology landscape, here are 10 use cases across the new three: AI, IoT and Blockchain (and in many cases an overlap of them)
Cristene Gonzalez-Wertz is the Leader for the IBM Institute for Business Value in Electronics as well as an alumni of IBM's Watson Group. She speaks on the intersection of technology, software, offerings, platforms and new business models.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Presentation of IBM Watson, the components of Watson, how it works and examples of where Watson is being put to use, today. Finally links and information about, how you can get to work with Watson as a software developer.
Presentation given in te conference 'Driving IT' in Copenhagen, November 14, 2014
Decision Intelligence: a new discipline emergesLorien Pratt
This document discusses decision intelligence and how technology can help solve complex business problems through better decision making. It provides examples of different technologies that can be used for decision intelligence like predictive analytics, agent-based models, and natural language processing. The document emphasizes that decision intelligence aims to understand what data, expertise, and other assets are relevant to make decisions that help achieve desired outcomes. It also shares perspectives from industry experts on the importance of decision intelligence in today's complex world.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
Innovation and Inspiration through Cognitive Computing: IBM WatsonIBM Watson
This document provides an overview of IBM Watson and cognitive computing capabilities from a presentation given by Swami Chandrasekaran, Executive Architect for IBM Watson. It discusses how cognitive systems like Watson can understand, reason, and learn from large amounts of data to augment and scale human expertise. Examples are given of how Watson is being applied in various industries to tackle complex problems and power new applications and experiences.
This document discusses artificial intelligence (AI) in healthcare, including key challenges and best practices for implementation. Some common challenges with AI implementation include not having enough high quality data for training models and ensuring the models align with real-world problems that can change over time. It is important to have a planned strategy for AI, carefully select partners, and ensure ethical and transparent use of data that complies with regulations. When implemented properly, AI has potential to improve healthcare through applications like personalized patient experiences and optimizing operations.
Early adopters of cognitive technologies are already gaining major competitive advantages through initiatives that improve customer engagement, productivity and efficiency, and business growth. A survey of over 600 cognitive decision-makers found that IT, data analytics, and customer service are common starting points for cognitive initiatives focused on product and service innovation and automation. While challenges remain around costs, security, and skills, collaboration between IT and business is driving cognitive adoption across industries.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
AI today and its power to transform healthcareBonnie Cheuk
This document summarizes a presentation by Dr. Bonnie Cheuk on how AI can transform businesses. In 3 sentences:
Dr. Cheuk discusses how AI can help gain a better understanding of diseases, identify new drug targets, speed up drug design and development, improve clinical trial design, and enable personalized medicine. Examples are presented where AI and machine learning have been used at AstraZeneca to classify tablets, identify likely prescribers of new drugs, and review patents. In conclusion, Dr. Cheuk emphasizes that AI should be applied carefully with consideration for ethics and unintended consequences, and that humans will continue to play an important role in applying judgment.
Certus Accelerate - Building the business case for why you need to invest in ...Certus Solutions
The document discusses building a business case for investing in data by highlighting the large percentage of unstructured data growth across different industries like healthcare, government, utilities and media. It emphasizes that 80% of new data is unstructured and invisible to computers. The world is being rewritten in software code and cloud is the new platform for reimagining industries. It then discusses the need for predictive, prescriptive and cognitive systems to make sense of vast amounts of data. Investing in data integration, governance and master data management is essential to unlock insights from all data sources and provide a comprehensive view of information. Justifying such investments requires looking at the potential costs of data quality failures and benefits of avoiding rework.
Markerstudy Group Drives Growth and InnovationCloudera, Inc.
Learn how Markerstudy Group is driving growth and innovation. The general insurer uses both Cloudera Enterprise powered by Hadoop and SAS Analytics. With it's big data analytics platform, Markerstudy has achieved significant ROI including 120% increase in policy count over 18 months.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
Intel, Cloudera and guest speaker Forrester Research, Inc. discuss the strategy of pervasive analytics and real life examples of how analytics have already been embedded into applications and workflows.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Talk presented at the Analytics Frontiers Conference in Charlotte on March 21. The presentation evaluates opportunities and risks of AI and how consumers, businesses, society and governments can mitigate some of the risks.
This document provides an overview of a research package on big data in the supply chain and logistics industry from eft. It includes:
- Interviews and presentations from industry experts on the biggest opportunities in using big data.
- A survey of over 200 supply chain executives on their use of big data.
- An article on using sensors and the "Internet of Things" to gain insights from real-time supply chain data.
The package explores how companies can leverage big data and analytics to improve visibility, flexibility, optimization, collaboration and control across their supply chains and gain competitive advantages. It highlights specific areas like risk management and predictive analytics that big data is enabling.
This document provides an overview of predictive analytics and its growing use in the insurance industry. It discusses key drivers for insurers' adoption of predictive analytics, including technological advances, data availability, seeking growth in slow-growth markets, and gaining competitive advantage. The document outlines how insurers use predictive analytics in marketing, underwriting, and claims to improve hit ratios, retention ratios, identify fraudulent claims, and prioritize claims processing. It provides details on the predictive analytic process of data mining, model development using regression and advanced models, and model validation. The advantages and disadvantages of predictive analytics for insurers are also discussed.
High Performance Computing and the Opportunity with Cognitive TechnologyIBM Watson
With the ability to reduce “time to insight” and accelerate research breakthroughs by providing immense computational power, high performance computing is becoming increasingly important in the marketplace. Meanwhile, cognitive technology has risen to prominence, similarly accelerating new insight, but through a very different approach - by analyzing previously ignored unstructured data, which accounts for 80% of new data created today.
By combining the powerful computing power of the HPC market, along with the machine learning, natural language processing, and even computer vision techniques found within cognitive technology, there is a huge opportunity to accelerate breakthroughs and enable better decision making than ever before.
Watch the replay of the webinar: https://www.youtube.com/watch?v=Hxgieboj3W0
Material for the 26 Oct 2015 lecture I held for Aalto University business students. The lecture focuses on the high level topics in analytics and Big Data that are either central to the subject or just highly visible in the media.
The main messages of the lecture are:
- The purpose of analytics and of the data analyst is to solve business problems
- Big Data brings over some very special traits to doing analytics that don't exist when working working with smaller datasets. Understanding these traits is a must for successful analytics.
- Deploying analytics is more dependent on humans than on technology
- Data and analytics are nowadays significant assets to many companies. Therefore they need their own strategy and need to be managed just like any other business critical assets.
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Staring with an brief overview of the changing role of the CIO between 2018 and 2020, then moving into the technology landscape, here are 10 use cases across the new three: AI, IoT and Blockchain (and in many cases an overlap of them)
Cristene Gonzalez-Wertz is the Leader for the IBM Institute for Business Value in Electronics as well as an alumni of IBM's Watson Group. She speaks on the intersection of technology, software, offerings, platforms and new business models.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Presentation of IBM Watson, the components of Watson, how it works and examples of where Watson is being put to use, today. Finally links and information about, how you can get to work with Watson as a software developer.
Presentation given in te conference 'Driving IT' in Copenhagen, November 14, 2014
Decision Intelligence: a new discipline emergesLorien Pratt
This document discusses decision intelligence and how technology can help solve complex business problems through better decision making. It provides examples of different technologies that can be used for decision intelligence like predictive analytics, agent-based models, and natural language processing. The document emphasizes that decision intelligence aims to understand what data, expertise, and other assets are relevant to make decisions that help achieve desired outcomes. It also shares perspectives from industry experts on the importance of decision intelligence in today's complex world.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
Innovation and Inspiration through Cognitive Computing: IBM WatsonIBM Watson
This document provides an overview of IBM Watson and cognitive computing capabilities from a presentation given by Swami Chandrasekaran, Executive Architect for IBM Watson. It discusses how cognitive systems like Watson can understand, reason, and learn from large amounts of data to augment and scale human expertise. Examples are given of how Watson is being applied in various industries to tackle complex problems and power new applications and experiences.
This document discusses artificial intelligence (AI) in healthcare, including key challenges and best practices for implementation. Some common challenges with AI implementation include not having enough high quality data for training models and ensuring the models align with real-world problems that can change over time. It is important to have a planned strategy for AI, carefully select partners, and ensure ethical and transparent use of data that complies with regulations. When implemented properly, AI has potential to improve healthcare through applications like personalized patient experiences and optimizing operations.
Early adopters of cognitive technologies are already gaining major competitive advantages through initiatives that improve customer engagement, productivity and efficiency, and business growth. A survey of over 600 cognitive decision-makers found that IT, data analytics, and customer service are common starting points for cognitive initiatives focused on product and service innovation and automation. While challenges remain around costs, security, and skills, collaboration between IT and business is driving cognitive adoption across industries.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
AI today and its power to transform healthcareBonnie Cheuk
This document summarizes a presentation by Dr. Bonnie Cheuk on how AI can transform businesses. In 3 sentences:
Dr. Cheuk discusses how AI can help gain a better understanding of diseases, identify new drug targets, speed up drug design and development, improve clinical trial design, and enable personalized medicine. Examples are presented where AI and machine learning have been used at AstraZeneca to classify tablets, identify likely prescribers of new drugs, and review patents. In conclusion, Dr. Cheuk emphasizes that AI should be applied carefully with consideration for ethics and unintended consequences, and that humans will continue to play an important role in applying judgment.
Certus Accelerate - Building the business case for why you need to invest in ...Certus Solutions
The document discusses building a business case for investing in data by highlighting the large percentage of unstructured data growth across different industries like healthcare, government, utilities and media. It emphasizes that 80% of new data is unstructured and invisible to computers. The world is being rewritten in software code and cloud is the new platform for reimagining industries. It then discusses the need for predictive, prescriptive and cognitive systems to make sense of vast amounts of data. Investing in data integration, governance and master data management is essential to unlock insights from all data sources and provide a comprehensive view of information. Justifying such investments requires looking at the potential costs of data quality failures and benefits of avoiding rework.
Markerstudy Group Drives Growth and InnovationCloudera, Inc.
Learn how Markerstudy Group is driving growth and innovation. The general insurer uses both Cloudera Enterprise powered by Hadoop and SAS Analytics. With it's big data analytics platform, Markerstudy has achieved significant ROI including 120% increase in policy count over 18 months.
This document introduces IBM's Watson and cognitive computing capabilities. It discusses how Watson uses technologies like natural language processing, machine learning, and deep learning to understand language, learn from interactions, and provide answers to questions. The document outlines IBM's vision of a "cognitive era" where systems can automate complex tasks by understanding, learning, and reasoning like humans. It promotes Watson and IBM's cognitive APIs and services as tools to help organizations gain insights from data and transform their business operations and customer experiences for the cognitive era.
From the conference Future Tech in Insurance at Forsikringsakademiet, nov 15 2016. Defining cognitive and how that is relevant for insurance companies.
Leveraging Applied AI to Accelerate Digital Transformation and Maximize Busin...Apttus
Enterprise business is taking a significant leap forward in its ability to maximize business outcomes using Applied AI – conversational and cognitive technologies. Leading organizations are accelerating digital transformation through machine learning, artificial intelligence and virtual assistants designed to streamline and accelerate revenue generation processes.
In this presentation for executives and decision makers, we’ll share insights from the soon-to-be-published Harvard Business Review study on using Applied AI to accelerate B2B Quote-to-Cash processes and commerce. We’ll examine Applied AI emerging trends, best practices, and barriers to adoption.
Keynote presentation from IBM Solutions Connect 2013 covering topics such as changing business world today and how technologies can help organisations cope with this change and move forward.
Ibm cognitive business_strategy_presentationdiannepatricia
IBM Cognitive Business Strategy presentation. Presented by Dianne Fodell and Jim Spohrer at the Cognitive Systems Institute Group Speaker Series call on October 8, 2015.
Assure Patient and Clinician Digital Experiences with ThousandEyes for Health...ThousandEyes
This document discusses how ThousandEyes can help healthcare organizations assure patient and clinician digital experiences. It provides an overview of the current healthcare landscape including challenges around digital transformation, hybrid workforces, and growing complexity. The document then outlines how ThousandEyes offers digital experience monitoring and internet visibility capabilities to help healthcare organizations improve patient experiences, ensure network assurance, enhance employee productivity, and reduce clinician frustration. Use cases are presented for optimizing applications, infrastructure, and supporting remote workforces.
This document summarizes a continuing education conference for accounting, finance, and human resources professionals on technology updates for 2011. The conference will cover topics including cloud computing, security best practices, disaster recovery plans, and how to effectively use social media for business. The presenter will discuss what cloud computing really means, current security threats facing businesses, how to safeguard mission critical data through disaster recovery plans, and how to establish social media policies for business collaboration. The conference aims to bring professionals up to date on important technology topics and best practices.
Join our webinar to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS.
.
Webinar attendees will learn:
- Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights.
- How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS.
- How easy it is to get started with machine learning solutions for data wrangling on the cloud.
This document provides an agenda for a presentation on AI and machine learning for financial professionals. The presentation will be given by Sri Krishnamurthy, founder and CEO of QuantUniversity. The agenda includes introductions of the speaker and an overview of QuantUniversity. It then covers key trends in AI/ML, the basics of machine learning in 30 minutes, building a machine learning application in 10 steps, and case studies of how AI/ML are used in finance from companies like Bank of America, Ravenpack, and Northfield.
Transforming Insurance Analytics with Big Data and Automated Machine Learning Cloudera, Inc.
This document discusses how machine learning and big data analytics can transform the insurance industry. It provides an overview of how automated machine learning works and its benefits for insurers, including higher returns on investment. Specific use cases discussed include underwriting triage, pricing, claims management, and fraud prevention. The document also addresses key data challenges for insurers and how a unified data platform can help bring different data sources together for machine learning. It promotes the idea that automated machine learning solutions can make machine learning more accessible, affordable and inclusive for organizations.
This presentation was made on May 13, 2020 and the video recording of it can be viewed here: https://youtu.be/QAgYASr1SHA
Description:
Are AI and AutoML overhyped or the answer to our problems?
Beyond the hyperbole, what are AutoML and AI?
How are they helpful, and when are they not?
Why are they more relevant and valuable than ever?
Our world is changing rapidly, and that implies many organizations will need to adapt quickly. AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business. AI empowers data teams to scale and deliver trusted, production-ready models in an easier, faster, more cost-effective way than traditional machine learning approaches.
AI and AutoML are not magic but it can be transformative, find out how at this virtual meetup. Get practical tips and see AutoML in action with a real-world example. We’ll demonstrate how AutoML can augment your Data Scientists, supercharging your team and giving your organization the AI edge in record time.
Speakers' Bio:
James Orton: He has over a decade of experience in analytics and data science across a number of industries. He has managed data science teams and large scale projects, before more recently launching his own startup. His vision for AI and that of H2O.ai were so closely aligned, it was a fortuitous opportunity for James to join H2O.ai in the Australia and New Zealand region.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
Democratization - New Wave of Data Science (홍운표 상무, DataRobot) :: AWS Techfor...Amazon Web Services Korea
This document discusses the democratization of data science and machine learning using automated machine learning tools. It provides examples of how DataRobot has helped customers in various industries build predictive models faster and with less coding than traditional approaches. Specifically, it summarizes how DataRobot has helped customers in banking, insurance, retail, and other industries with use cases like predictive maintenance, sales forecasting, fraud detection, customer churn prediction, and insurance underwriting.
Disruptive Insurance Product Innovation Using IoT in HealthcareAmazon Web Services
Potential for consumer healthcare
1. No more non-value apps – consumers want insights
2. Lifestyle and Wellness platforms will win
3. Data INTENSITY = New OPPORTUNITY
4. Real Time is the NORM
5. Machines learn to IMPROVE our lives
Speakers:
Gaurav Sharma, Senior Industry Principal and Lead for Finacle on Cloud business, Infosys Finacle
&
Michael Braendle, Principal Cloud Architect, Professional Services, AWS
Captricity at Corinium Chief Data Officer Forum Keynote - Brian Cox Captricity
Chief Data Officer Forum, Insurance
September 15, 2016
How Insurers are Leveraging Modern Technology for Improved Customer Experiences
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Intro to Artificial Intelligence w/ Target's Director of PMProduct School
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Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Looks at the different AI approaches and provides some practical categorisation and case studies. Then talks about the data fabric you need to put in place to improve model accuracy and deployment. Covers: supervised, unsupervised, machine learning, deep learning, RPA, etc. Finishes with how to create successful AI projects.
Similar to Certus Accelerate - Why You Need to Invest in Your Data by Vincent McBurney (20)
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Sydney: Certus Data 2.0 Vault Meetup with Snowflake - Data Vault In The Cloud Certus Solutions
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Data Vault 2.0 is a unique system of Data Warehousing created from the ground up to deal with real-world data challenges. Data Vault 2.0 delivers improved total cost of ownership, greatly enhanced operational agility and traceable data governance.
While Bitcoin is often considered the first real use case for blockchain, the technology has come a long way since those early days. Blockchain has been rebuilt from the ground up and is a driving force for new business models and regulated industries. Blockchain is a shared, immutable ledger for recording the history of transactions.
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Accelerate 2017_Brand experience and context_Craig ParnhamCertus Solutions
Using location services to change brand experiences
How beacons and location services are transforming everyday experiences
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Accelerate 2017_Navigating Digital Disruption_James SlezakCertus Solutions
Keynote: How top organisations like The New York Times are navigating digital disruption
When The New York Times digital edition launched in January of 1996, it was a wholly separate operation, staffed out of a building on the other side of town and updated once a day. Like everything then, it was free.
Twenty years later, digital is at the core of the operation. Despite powerful voices of opposition and difficult economic headwinds, the digital operation is a source of rapid growth.
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The document describes the Certus Accelerate app. It provides the following key information:
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- To download the app, the user will receive a text with a link to tap and follow instructions.
- New content and features can be added after the event concludes, and users will continue to receive updates.
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To compete in a consumer-empowered economy, it is increasingly clear that financial services firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more.
This document discusses cognitive computing and analytics technologies. It provides examples of how cognitive systems can be applied, such as a toy that learns from child interactions. The document outlines a cognitive strategy and foundation that includes collecting and analyzing both structured and unstructured data. It also discusses the importance of cloud services, infrastructure, and security for cognitive systems. Finally, the document describes some of the cognitive computing APIs available from IBM Watson and how the set of APIs has expanded over time.
This document discusses how data, devices, and design are changing rapidly and must be navigated strategically. Key points include:
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
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Fueling AI with Great Data with Airbyte WebinarZilliz
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
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This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
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Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
2. HEALTHCARE DATA GOVERNMENT & EDUCATION DATA
99% 88% 94% 84%
Healthcare data comes from sources
such as:
Government & education data comes
from sources such as:
Patient
Sensors
Electronic
Medical
Records
Test
Results
Vehicle Fleet
Sensors
Traffic
Sensors
Student
Evaluations
UTILITIES DATA MEDIA DATA
93% 84% 97% 82%
Utilities data comes from sources
such as:
Media data comes from sources
such as:
Utility
Sensors
Employee
Sensors
Location
Data
Video
and Film
Images Audio
growth by 2017 unstructured growth by 2017 unstructured
growth by 2017 unstructured growth by 2017 unstructured
Data flows from every device, replacing
guessing and approximations with precise
information. Yet 80% of this data is
unstructured; therefore, invisible to
computers and of limited use to business.
By 2020,
of new information will be created
every second for every human
being on the planet.
1.7 MB
Consider:
3. The world is being rewritten in software
code, and cloud is the platform on which
the new digital builders—from developers
to business professionals—are
reimagining everything from banking to
retail to healthcare.
100,000,000
lines of code
in a new car
5,000,000
lines of code
in smart appliances
1,200,000
lines of code
in a smartphone
80,000
lines of code
in a pacemaker
Consider:
of B2B
collaboration
will take place
through web
APIs next year.
50%
Smart TVs
represented 27% of
all TV sales in 2012;
by 2018, they will
represent 82%.
Smart LED lighting
will grow from 6M
units in 2015 to 570M
units in 2020, used for
safety communication,
health, pollution and
personalized services.
By 2017, there will be
1B connected things
in smart homes,
including appliances,
smoke detectors and
cameras.
Sensors for industrial asset
monitoring and management will
grow from just over 15M units in
2014 to over 40M units in 2018
Smart traffic sensors
and other devices
installed in smart
cities will grow from
237M units in 2015 to
371M in 2017.
Revenues for
smart grid sensors
will grow ten-fold from
2014 to 2021.
Code Tools
Analytics
Data
APIs
By 2020, there will be
925M smart meters
installed worldwide,
more than double the
400M in 2014.
4. So where do we want to go from here?
Predictive Prescriptive Cognitive
5. Cognitive systems can understand the world through sensing and
interaction, reason using hypotheses and arguments and learn from experts
and through data. Watson is the most advanced such system.
Today, businesses in
countries across.
There are
Watson ecosystem partner
companies, with
78%
of business and IT executives
believe that successful business will
manage employees alongside
intelligent machines.
On average there are
36
17
industries are applying cognitive
technologies.
350+
100
of those have taken their product to market.
1.3B
Watson API calls a month
and growing.
Among C-Suite executives familiar
with cognitive computing:
96%
84%
94%
89%
in insurance intend to invest in cognitive
capabilities.
in healthcare believe it will play a
disruptive role in the industry, and 60%
believe they lack the skilled professionals
and technical experience to achieve it.
in retail intend to invest in cognitive
capabilities.
in telecommunications believe
it will have a critical impact on the future of
their business.
Consider:
6. Watson Winning and Jeopardy
4 Letter Word for
a Vantage Point
or a belief
“and anytime you feel the
pain, hey” this guy “refrain,
don’t carry the world upon
your shoulders”
Watson defeated two all
time Jeopardy champions
over two nights on US TV
You’re just a little stiff! You
don’t have this painful
mosquito-borne joint illness
with a Swahili name
8. Asset Data is Rich
Asset Management Modules
• Assets
• Condition Monitoring
• Failure Codes
• Locations
• Meters and Meter Groups
Work Management Modules
• Assignment Manager
• Job Plans
• Lock-Out/Tag-Out
• Labor Tracking
• PreventiveMaintenanceMaster PM
• Qualifications
• Safety
• Service Items and Requests
• Tools/Crafts
Inventory Management Modules
•Item Master
•Storerooms
•Lot Management
•Kitting
•Issues & Transfers
•Condition Codes
•Stocked Tools
•Service Items
Procurement Management Modules
•Desktop Requisitions
•Purchase Orders
•Purchase Requisitions
•Receiving & Receiving Inspections
•Request for Quotation
Contract Management Modules
•Labor Rate Contracts
•Lease/Rental Contracts
•Master Contracts
•Payment Schedules
•Purchase Contracts
•Warranty Contracts
Service Management Modules
•Service Catalogs
•Service Level Agreement (SLA)
•Incidents
•Problems
•Changes
•Releases
•Solutions
Maximo is a treasure trove of unstructured and semi structured notes, guides, messages,
instructions etc.
9. Important Information can be Hidden
Maximo attachment example – an external document that helps
complete work orders.
1.0 GENERAL INSTRUCTIONS
1.1 Worksite Access and Egress
Access to the worksite shall be in accordance with Worksite Access and Egress.
Thunderstorm risk must be checked prior to access.
1.2 Instructions prior to commencing work
Either Maintenance Control or the Traffic Control Room should be notified via O & M radio
prior to the commencement of any work, and again at the end of the works. If any alarms
are to be activated, the Traffic Control room should also be informed.
1.3 Defects
All Defects found are to be reported to the Traffic Control Room or the Leading Hand and
listed on the defect register
1.4 Qualifications / Licences
This procedure should be carried out by BOCS personnel experienced in VR sensor
certification procedures and familiar with the operation of Kapsch Tolling Equipment.
1.5 Overview
VR sensor replacement is carried out prior to the certification process. Before replacement
of the sensor, the certification entity (SGS) needs to place seals on the VR sensor and
take pictures of the VR sensor to record serial number and MAC address of the
replacement sensor.
Certification information and Images are forwarded to the certification entity and kept by
them for their records.
10. Watson on Diagnosis
Watson at work
After Jeopardy IBM developed Watson to become a business application – some of the
first use cases were helping health care workers diagnosis.
"On Jeopardy it was not
necessarily critical to know
how Watson arrived at its
answer,” says Eric Brown,
IBM Research Director of
Watson Technologies. "But
doctors or domain experts in
any field will want to
understand what information
sources Watson consulted,
what logic it applied and what
inferences it made in arriving
at a recommendation.”
11. How Watson Works
Answer
Scoring
Models
Responses with
Confidence
Inquiry
Evidence
Sources
Models
Models
Models
Models
ModelsPrimary
Search
Candidate
Answer
Generation
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Final Confidence
Merging & Ranking
Synthesis
Answer
Sources
Inquiry/Topic
Analysis
Evidence
Retrieval
Deep
Evidence
Scoring
Learned Models
help combine and
weigh the Evidence
Hypothesis
Generation
Hypothesis and Evidence
Scoring
Inquiry
Decomposition
1000’s of
Pieces of Evidence
Multiple
Interpretations
of a question
100,000’s Scores from
many Deep Analysis
Algorithms
100’s
sources
100’s Possible
Answers
Balance
& Combine
How Watson works: DeepQA architecture
12. So where are we all right now?
Structured
Semi-
structured
Unstructured
13. • Unfortunately, 40% to 50% of data warehouse initiatives end in costly failure.
International Association for Computer Information Systems
• Gartner estimates in 2012 that by 2014, fewer than 30% of business intelligence
(BI) initiatives will align analytics completely with enterprise business drivers,
despite alignment being the foremost BI challenge
• LinkedIn poll in 2013 asked BI experts how much of a BI project was spent in just
getting the data right. The answer came back as around 75–80%.
• Data warehouses are replaced on average every 3–5 years!
The issues with a traditional approach
14. What if we could increase and improve each of these?
Structured
Semi-
structured
Unstructured
15. So we need a new architecture
and a new approach
16. Systems Security
On premise, Cloud, As a service
Storage
IBM Watson Foundations
IBM Big Data & Analytics Infrastructure
New/Enhanced
Applications
All Data
Real-time
analytics
zone
Enterprise
warehouse
data mart
and analytic
appliances
zone
Information governance zone
Exploration,
landing
and
archive zone
Information
ingestion
and
operational
information
zone
What could
happen?
Predictive analytics
and modeling
What action
should I take?
Decision
management
What is
happening?
Discovery and
exploration
Why did it
happen?
Reporting, analysis,
content analytics
Cognitive
Fabric
Different approaches require different systems:
17. • Capturing any type of information, fast/slow, big/small, structured,
unstructured, text/images etc
• The ability to visualise, report. predict and action
BUT to make this a reality we need to apply:
• Retained auditability/integrity
• Flexible access
• Appropriate security
• Single view of an entity
• Just enough governance
• Just enough modelling
• Automation
• Avoiding re-engineering
New tools & methods are providing new capabilities to:
18. Operational Data Store
Bring data together
in real time.
Create an alternative
to querying operational
databases.
Make operational data easier
to access and use.
Time Machine
Go back to any point in time
and re-run a query.
Troubleshoot old decisions
made on old report.
Analyse change over time.
Semantic Layer
Store data so that reporting
and analytic tools can use it.
Model and name information
so it can be understood
and used safely.
19. • Seamlessly allow SQL and noSQL data integration
• Only 3 ELT patterns to load ALL data into a warehouse
• Avoids all re-engineering
• Enables full automation of changes into data warehouse
• Enables true agile reporting and analytics
• Specific constructs to deal with business rules
• Retains auditability and security
• Linear scaling in process and development
Methods like Data Vaulting that:
20. New/Enhanced
Applications
All Data
Information Integration and Governance
Information Governance and compliance reporting
Data
Quality
Entity
Management
Information
Lifecycle
Management
Information
Integration
On-Premises, As-A-Service (IBM DataWorks)
and Cloud (SoftLayer)
Unlocks key insights, by providing trust and comprehensive view
Information integration and governance
23. Implementing a Total Information Quality Management System across People,
Processes and Tools to create a continuous data quality improvement culture.
Certus Data Quality Framework
24. Data Profiling and Assessment
A lot of 1977 and 1970
in Installation Date
Some legacy values in
Allow Work Order field
27. Data Quality Management is essential
Pass Fail rate Potential Cost of Failures Cost to fix errors
Errors by
data domain
and quality
category
Errors by
criticality
28. A Stewardship Function is essential
Business Process Management
for manual and automated
workflows required to implement
information governance and
manage data quality.
29. So back to the business case:
Governance
• Make Stewards more
efficient
• Spend money wisely
• Minimise Data as a
cause of accidents
• Forecast and budget
more accurately
• Ensure crews can
access sites
Analytics
• Predict replacement
and maintenance
dates with greater
accuracy
• Anticipate equipment
failure
• Improve the
relationship with the
customer
• Optimise the use of
resources
Cognitive
• Ensure all personnel
know how to react to
a condition code
• Use the corpus for
better actions and
resolutions
• Make it easier to get
answers from data
30. Why an MDM?
31
Maximize 1:1 consumer
relationships
Deliver personalized offers or
discounts aligned to unique
behaviors, needs and desires
Brand reputation
Right message every time in market
and consistent communication with
customers
Marketing productivity
Increased breadth of digital
channels, emphasis on cross-sell /
up-sell / right-sell opportunities,
understanding and embracing ROMI
Deliver value across all
touch points
Build opportunity for revenue
growth throughout marketing
value chain
360 Degree View of the Customer
Understanding, responding and maximizing each
unique customer relationship
Optimize marketing mix
Model and plan balancing needs of
channels, probability of ROI success
and resource constraints
Customer growth and
retention
Demanding customers, commoditized
products and crowded competitive
marketplace
31. Becoming an analytically driven or
cognitive business is a journey.
Businesses will be able to rapidly capitalise
on new opportunities if they have invested
in the foundations of their information
management systems.
32. Install The Certus Accelerate
Shareable App
The Certus Accelerate App will give you access to a host of event
related content including:
Videos
Infographics
Blogs
How to download: A text will come through saying, “Certus app wants
to share the Accelerate app with you.” Simply Tap the link and follow
the instructions to download the app.
Fresh ideas: New content and features can be added to the app so the
experience won’t end once the event concludes, we will continue to
update you with new content.