際際滷shows by User: mrm0 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: mrm0 / Tue, 22 Jun 2021 04:04:25 GMT 際際滷Share feed for 際際滷shows by User: mrm0 Data Architecture: OMG Its Made of People /slideshow/data-architecture-omg-its-made-of-people/249443077 2021-05linkedinlive-dataarchitecturev2-210622040425
Do you have data? Do you have users? Do they use that data to solve problems? Then you have a data architecture. Maybe your architecture is organic and accidental, or maybe its an accumulation of the latest practices and technologies you heard about on Stack Overflow. Spoiler: data architecture is about people and how they use data, not the latest pipeline framework or AI model. Data architecture is about enabling users to be productive, not adding the next shiny object and then blaming the users for using it wrong. What you design needs to focus on a different subject than either technology or data. Join Kevin Bogusch, Ecosystem Architect, as he talks with Mark Madsen, Fellow at the Technology Innovation Office, on the crucial elements youre missing in a successful data architecture: people and process. Find out why Mark says, dont buy one problem to solve another problem. ]]>
Do you have data? Do you have users? Do they use that data to solve problems? Then you have a data architecture. Maybe your architecture is organic and accidental, or maybe its an accumulation of the latest practices and technologies you heard about on Stack Overflow. Spoiler: data architecture is about people and how they use data, not the latest pipeline framework or AI model. Data architecture is about enabling users to be productive, not adding the next shiny object and then blaming the users for using it wrong. What you design needs to focus on a different subject than either technology or data. Join Kevin Bogusch, Ecosystem Architect, as he talks with Mark Madsen, Fellow at the Technology Innovation Office, on the crucial elements youre missing in a successful data architecture: people and process. Find out why Mark says, dont buy one problem to solve another problem. ]]> Tue, 22 Jun 2021 04:04:25 GMT /slideshow/data-architecture-omg-its-made-of-people/249443077 mrm0@slideshare.net(mrm0) Data Architecture: OMG Its Made of People mrm0 Do you have data? Do you have users? Do they use that data to solve problems? Then you have a data architecture. Maybe your architecture is organic and accidental, or maybe its an accumulation of the latest practices and technologies you heard about on Stack Overflow. Spoiler: data architecture is about people and how they use data, not the latest pipeline framework or AI model. Data architecture is about enabling users to be productive, not adding the next shiny object and then blaming the users for using it wrong. What you design needs to focus on a different subject than either technology or data. Join Kevin Bogusch, Ecosystem Architect, as he talks with Mark Madsen, Fellow at the Technology Innovation Office, on the crucial elements youre missing in a successful data architecture: people and process. Find out why Mark says, dont buy one problem to solve another problem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2021-05linkedinlive-dataarchitecturev2-210622040425-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Do you have data? Do you have users? Do they use that data to solve problems? Then you have a data architecture. Maybe your architecture is organic and accidental, or maybe its an accumulation of the latest practices and technologies you heard about on Stack Overflow. Spoiler: data architecture is about people and how they use data, not the latest pipeline framework or AI model. Data architecture is about enabling users to be productive, not adding the next shiny object and then blaming the users for using it wrong. What you design needs to focus on a different subject than either technology or data. Join Kevin Bogusch, Ecosystem Architect, as he talks with Mark Madsen, Fellow at the Technology Innovation Office, on the crucial elements youre missing in a successful data architecture: people and process. Find out why Mark says, dont buy one problem to solve another problem.
Data Architecture: OMG Its Made of People from mark madsen
]]> 280 0 https://cdn.slidesharecdn.com/ss_thumbnails/2021-05linkedinlive-dataarchitecturev2-210622040425-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Solve User Problems: Data Architecture for Humans /slideshow/solve-user-problems-data-architecture-for-humans/248655677 tdwimunichkeynotethirdnaturemadsen03-2021-210528051222
We are bombarded with stories of the latest products to hit the market products that will change everything we do. This causes us to focus on the latest technology, building IT for the sake of building IT. Meanwhile, the world still seems to run on Excel. The big innovators who have and use unimaginably large amounts of data are not the norm. Aspiring to use the same complex technologies and patterns they do leads to poor investments and tradeoffs. This is an age-old problem rooted in the over-emphasis of technology as the agent of change. Technology isnt the answer its the platform on which people build answers. To emphasize technology is to ignore the way tools change people and practices. The design focus in our market was on storing and making data accessible. If we want to make progress then we need to step back from the details and look at data from the perspective of the organization. Our design focus shifts to people learning and applying new insights, asking questions about how an organization can be more resilient, more efficient, or faster to sense and respond to changing conditions. In this talk you will learn how to put your data architecture into a human frame of reference. Drawing inspiration from the history of technology and urban planning, we will see that the services provided by the things we build are what drive success, not the latest shiny distraction. ]]>
We are bombarded with stories of the latest products to hit the market products that will change everything we do. This causes us to focus on the latest technology, building IT for the sake of building IT. Meanwhile, the world still seems to run on Excel. The big innovators who have and use unimaginably large amounts of data are not the norm. Aspiring to use the same complex technologies and patterns they do leads to poor investments and tradeoffs. This is an age-old problem rooted in the over-emphasis of technology as the agent of change. Technology isnt the answer its the platform on which people build answers. To emphasize technology is to ignore the way tools change people and practices. The design focus in our market was on storing and making data accessible. If we want to make progress then we need to step back from the details and look at data from the perspective of the organization. Our design focus shifts to people learning and applying new insights, asking questions about how an organization can be more resilient, more efficient, or faster to sense and respond to changing conditions. In this talk you will learn how to put your data architecture into a human frame of reference. Drawing inspiration from the history of technology and urban planning, we will see that the services provided by the things we build are what drive success, not the latest shiny distraction. ]]> Fri, 28 May 2021 05:12:22 GMT /slideshow/solve-user-problems-data-architecture-for-humans/248655677 mrm0@slideshare.net(mrm0) Solve User Problems: Data Architecture for Humans mrm0 We are bombarded with stories of the latest products to hit the market products that will change everything we do. This causes us to focus on the latest technology, building IT for the sake of building IT. Meanwhile, the world still seems to run on Excel. The big innovators who have and use unimaginably large amounts of data are not the norm. Aspiring to use the same complex technologies and patterns they do leads to poor investments and tradeoffs. This is an age-old problem rooted in the over-emphasis of technology as the agent of change. Technology isnt the answer its the platform on which people build answers. To emphasize technology is to ignore the way tools change people and practices. The design focus in our market was on storing and making data accessible. If we want to make progress then we need to step back from the details and look at data from the perspective of the organization. Our design focus shifts to people learning and applying new insights, asking questions about how an organization can be more resilient, more efficient, or faster to sense and respond to changing conditions. In this talk you will learn how to put your data architecture into a human frame of reference. Drawing inspiration from the history of technology and urban planning, we will see that the services provided by the things we build are what drive success, not the latest shiny distraction. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunichkeynotethirdnaturemadsen03-2021-210528051222-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We are bombarded with stories of the latest products to hit the market products that will change everything we do. This causes us to focus on the latest technology, building IT for the sake of building IT. Meanwhile, the world still seems to run on Excel. The big innovators who have and use unimaginably large amounts of data are not the norm. Aspiring to use the same complex technologies and patterns they do leads to poor investments and tradeoffs. This is an age-old problem rooted in the over-emphasis of technology as the agent of change. Technology isnt the answer its the platform on which people build answers. To emphasize technology is to ignore the way tools change people and practices. The design focus in our market was on storing and making data accessible. If we want to make progress then we need to step back from the details and look at data from the perspective of the organization. Our design focus shifts to people learning and applying new insights, asking questions about how an organization can be more resilient, more efficient, or faster to sense and respond to changing conditions. In this talk you will learn how to put your data architecture into a human frame of reference. Drawing inspiration from the history of technology and urban planning, we will see that the services provided by the things we build are what drive success, not the latest shiny distraction.
Solve User Problems: Data Architecture for Humans from mark madsen
]]> 803 0 https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunichkeynotethirdnaturemadsen03-2021-210528051222-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Black Box: Interpretability, Reproducibility, and Data Management /slideshow/the-black-box-interpretability-reproducibility-and-data-management-183143344/183143344 oreillyailondon2019-10final-191017133849
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretabilityexplaining how an analytic model worksand that you need it to deploy models. But people use many black boxes without understanding themif theyre reliable. Its when the black box becomes unreliable that people lose trust. Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibilitythe ability to get the same results given the same informationextends your view to include the environment and the data used to build and execute models. Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed. This talk will treat the black boxed of ML the way management perceives them, as black boxes. There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.]]>
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretabilityexplaining how an analytic model worksand that you need it to deploy models. But people use many black boxes without understanding themif theyre reliable. Its when the black box becomes unreliable that people lose trust. Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibilitythe ability to get the same results given the same informationextends your view to include the environment and the data used to build and execute models. Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed. This talk will treat the black boxed of ML the way management perceives them, as black boxes. There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.]]> Thu, 17 Oct 2019 13:38:49 GMT /slideshow/the-black-box-interpretability-reproducibility-and-data-management-183143344/183143344 mrm0@slideshare.net(mrm0) The Black Box: Interpretability, Reproducibility, and Data Management mrm0 The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretabilityexplaining how an analytic model worksand that you need it to deploy models. But people use many black boxes without understanding themif theyre reliable. Its when the black box becomes unreliable that people lose trust. Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibilitythe ability to get the same results given the same informationextends your view to include the environment and the data used to build and execute models. Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed. This talk will treat the black boxed of ML the way management perceives them, as black boxes. There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oreillyailondon2019-10final-191017133849-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretabilityexplaining how an analytic model worksand that you need it to deploy models. But people use many black boxes without understanding themif theyre reliable. Its when the black box becomes unreliable that people lose trust. Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibilitythe ability to get the same results given the same informationextends your view to include the environment and the data used to build and execute models. Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed. This talk will treat the black boxed of ML the way management perceives them, as black boxes. There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
The Black Box: Interpretability, Reproducibility, and Data Management from mark madsen
]]> 824 5 https://cdn.slidesharecdn.com/ss_thumbnails/oreillyailondon2019-10final-191017133849-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Operationalizing Machine Learning in the Enterprise /mrm0/operationalizing-machine-learning-in-the-enterprise tdwimunichoperatioanlizemlfinal06-22-19-190624083949
TDWI Munich 2019 What does it take to operationalize machine learning and AI in an enterprise setting? Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. Its a long way from the environment needed to build ML applications to the environment to run them in an enterprise. Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do. This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.]]>
TDWI Munich 2019 What does it take to operationalize machine learning and AI in an enterprise setting? Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. Its a long way from the environment needed to build ML applications to the environment to run them in an enterprise. Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do. This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.]]> Mon, 24 Jun 2019 08:39:48 GMT /mrm0/operationalizing-machine-learning-in-the-enterprise mrm0@slideshare.net(mrm0) Operationalizing Machine Learning in the Enterprise mrm0 TDWI Munich 2019 What does it take to operationalize machine learning and AI in an enterprise setting? Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. Its a long way from the environment needed to build ML applications to the environment to run them in an enterprise. Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do. This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunichoperatioanlizemlfinal06-22-19-190624083949-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> TDWI Munich 2019 What does it take to operationalize machine learning and AI in an enterprise setting? Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. Its a long way from the environment needed to build ML applications to the environment to run them in an enterprise. Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do. This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Operationalizing Machine Learning in the Enterprise from mark madsen
]]> 770 6 https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunichoperatioanlizemlfinal06-22-19-190624083949-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Building a Data Platform Strata SF 2019 /slideshow/building-a-data-platform-strata-sf-2019/138336811 buildadataplatformdataarchstratasf2019-190326224058
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. [This is a new, changed version of the presentations of the same title from last year's Strata]]]>
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. [This is a new, changed version of the presentations of the same title from last year's Strata]]]> Tue, 26 Mar 2019 22:40:58 GMT /slideshow/building-a-data-platform-strata-sf-2019/138336811 mrm0@slideshare.net(mrm0) Building a Data Platform Strata SF 2019 mrm0 Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. [This is a new, changed version of the presentations of the same title from last year's Strata] <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/buildadataplatformdataarchstratasf2019-190326224058-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. [This is a new, changed version of the presentations of the same title from last year&#39;s Strata]
Building a Data Platform Strata SF 2019 from mark madsen
]]> 3322 16 https://cdn.slidesharecdn.com/ss_thumbnails/buildadataplatformdataarchstratasf2019-190326224058-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Architecting a Data Platform For Enterprise Use (Strata NY 2018) /slideshow/architecting-a-data-platform-for-enterprise-use-strata-ny-2018/113886805 stratanyanalyticsarchfinal09-11-18-180911060633
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. Long: The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Data architecture * Functional architecture * Technology planning assumptions and guidance ]]>
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. Long: The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Data architecture * Functional architecture * Technology planning assumptions and guidance ]]> Tue, 11 Sep 2018 06:06:33 GMT /slideshow/architecting-a-data-platform-for-enterprise-use-strata-ny-2018/113886805 mrm0@slideshare.net(mrm0) Architecting a Data Platform For Enterprise Use (Strata NY 2018) mrm0 Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. Long: The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Data architecture * Functional architecture * Technology planning assumptions and guidance <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratanyanalyticsarchfinal09-11-18-180911060633-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT. Long: The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization&#39;s data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Data architecture * Functional architecture * Technology planning assumptions and guidance
Architecting a Data Platform For Enterprise Use (Strata NY 2018) from mark madsen
]]> 1117 9 https://cdn.slidesharecdn.com/ss_thumbnails/stratanyanalyticsarchfinal09-11-18-180911060633-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Architecting a Platform for Enterprise Use - Strata London 2018 /slideshow/strata-london-analytics-arch-v2-05-2018/98035643 stratalondonanalyticsarchv205-20-18-180522075913
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Analytic workload characteristics and constraints * Data architecture * Functional architecture * Tradeoffs between different classes of technology * Technology planning assumptions and guidance #strataconf]]>
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Analytic workload characteristics and constraints * Data architecture * Functional architecture * Tradeoffs between different classes of technology * Technology planning assumptions and guidance #strataconf]]> Tue, 22 May 2018 07:59:13 GMT /slideshow/strata-london-analytics-arch-v2-05-2018/98035643 mrm0@slideshare.net(mrm0) Architecting a Platform for Enterprise Use - Strata London 2018 mrm0 The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Analytic workload characteristics and constraints * Data architecture * Functional architecture * Tradeoffs between different classes of technology * Technology planning assumptions and guidance #strataconf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratalondonanalyticsarchv205-20-18-180522075913-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure. The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization&#39;s data management practices? This tutorial will help you answer these questions. Topics covered: * A brief history of data infrastructure and past design assumptions * Categories of data and data use in organizations * Analytic workload characteristics and constraints * Data architecture * Functional architecture * Tradeoffs between different classes of technology * Technology planning assumptions and guidance #strataconf
Architecting a Platform for Enterprise Use - Strata London 2018 from mark madsen
]]> 717 13 https://cdn.slidesharecdn.com/ss_thumbnails/stratalondonanalyticsarchv205-20-18-180522075913-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range /mrm0/a-brief-tour-through-the-geology-endemic-botany-of-the-klamathsiskiyou-range npsosouthernorbotanyfinal02-2018-180319024710
A hotspot of diversity for rare plants, butterflies and birds, the Klamath-Siskiyou region of southern Oregon is a scientist's (and naturalist's) paradise. This is transverse range running from the Cascades range to the Pacific Ocean, creating an east-west corridor between the coast and the volcanic Cascades range. Mark Madsens love of biology while living in the area for 15 years sparked an interest in botanical taxonomy in the world of serpentine soils and the plant communities thriving in the region, including remnant species from the last ice age.]]>
A hotspot of diversity for rare plants, butterflies and birds, the Klamath-Siskiyou region of southern Oregon is a scientist's (and naturalist's) paradise. This is transverse range running from the Cascades range to the Pacific Ocean, creating an east-west corridor between the coast and the volcanic Cascades range. Mark Madsens love of biology while living in the area for 15 years sparked an interest in botanical taxonomy in the world of serpentine soils and the plant communities thriving in the region, including remnant species from the last ice age.]]> Mon, 19 Mar 2018 02:47:10 GMT /mrm0/a-brief-tour-through-the-geology-endemic-botany-of-the-klamathsiskiyou-range mrm0@slideshare.net(mrm0) A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range mrm0 A hotspot of diversity for rare plants, butterflies and birds, the Klamath-Siskiyou region of southern Oregon is a scientist's (and naturalist's) paradise. This is transverse range running from the Cascades range to the Pacific Ocean, creating an east-west corridor between the coast and the volcanic Cascades range. Mark Madsens love of biology while living in the area for 15 years sparked an interest in botanical taxonomy in the world of serpentine soils and the plant communities thriving in the region, including remnant species from the last ice age. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/npsosouthernorbotanyfinal02-2018-180319024710-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A hotspot of diversity for rare plants, butterflies and birds, the Klamath-Siskiyou region of southern Oregon is a scientist&#39;s (and naturalist&#39;s) paradise. This is transverse range running from the Cascades range to the Pacific Ocean, creating an east-west corridor between the coast and the volcanic Cascades range. Mark Madsens love of biology while living in the area for 15 years sparked an interest in botanical taxonomy in the world of serpentine soils and the plant communities thriving in the region, including remnant species from the last ice age.
A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range from mark madsen
]]> 699 6 https://cdn.slidesharecdn.com/ss_thumbnails/npsosouthernorbotanyfinal02-2018-180319024710-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 How to understand trends in the data & software market /slideshow/a-retrospective-of-the-future-how-to-understand-trends-in-the-data-industry/86937245 futureretrospectivejuly2017-180130212048
The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand whats really happening in the data technology market a whole lot of reinvention and some innovation, but not where you expect it. We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities. We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes well have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself. At that point, however, it will have ceased to matter because well be dealing with fundamentally new problems/shortcomings/intractabilities. ]]>
The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand whats really happening in the data technology market a whole lot of reinvention and some innovation, but not where you expect it. We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities. We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes well have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself. At that point, however, it will have ceased to matter because well be dealing with fundamentally new problems/shortcomings/intractabilities. ]]> Tue, 30 Jan 2018 21:20:48 GMT /slideshow/a-retrospective-of-the-future-how-to-understand-trends-in-the-data-industry/86937245 mrm0@slideshare.net(mrm0) How to understand trends in the data & software market mrm0 The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand whats really happening in the data technology market a whole lot of reinvention and some innovation, but not where you expect it. We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities. We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes well have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself. At that point, however, it will have ceased to matter because well be dealing with fundamentally new problems/shortcomings/intractabilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/futureretrospectivejuly2017-180130212048-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand whats really happening in the data technology market a whole lot of reinvention and some innovation, but not where you expect it. We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities. We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes well have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself. At that point, however, it will have ceased to matter because well be dealing with fundamentally new problems/shortcomings/intractabilities.
How to understand trends in the data & software market from mark madsen
]]> 506 3 https://cdn.slidesharecdn.com/ss_thumbnails/futureretrospectivejuly2017-180130212048-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Pay no attention to the man behind the curtain - the unseen work behind data science /slideshow/pay-no-attention-to-the-man-behind-the-curtain-the-unseen-work-behind-data-science/80954111 accelerateunseenworkdatasciencefinal10-18-17-171018190456
Goal: explain the nature of the work of an analytics team to a manager, and enable people on those teams to explain what a data science team needs to a manager. It seems as if every organization wants to enable analytical-decision making and embed analytics into operational processes. What can you do with analytics? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Why is this? Vendors promise easy-to-use analytics tools and services but they rarely deliver. The products may be easy but the work is still hard. Using analytics to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Figuring out what to do and how to do it is a lot harder. Despite this, fancy new tools get all the attention and budget. People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data for analytics is a nuanced, detail-oriented and seemingly dull task left to back-office IT. If your goal is to build a repeatable analytics capability rather than a one-off analytics project then you will need to address the parts that are rarely mentioned. This talk will explain some of the unseen and little-discussed aspects involved when building and deploying analytics.]]>
Goal: explain the nature of the work of an analytics team to a manager, and enable people on those teams to explain what a data science team needs to a manager. It seems as if every organization wants to enable analytical-decision making and embed analytics into operational processes. What can you do with analytics? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Why is this? Vendors promise easy-to-use analytics tools and services but they rarely deliver. The products may be easy but the work is still hard. Using analytics to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Figuring out what to do and how to do it is a lot harder. Despite this, fancy new tools get all the attention and budget. People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data for analytics is a nuanced, detail-oriented and seemingly dull task left to back-office IT. If your goal is to build a repeatable analytics capability rather than a one-off analytics project then you will need to address the parts that are rarely mentioned. This talk will explain some of the unseen and little-discussed aspects involved when building and deploying analytics.]]> Wed, 18 Oct 2017 19:04:55 GMT /slideshow/pay-no-attention-to-the-man-behind-the-curtain-the-unseen-work-behind-data-science/80954111 mrm0@slideshare.net(mrm0) Pay no attention to the man behind the curtain - the unseen work behind data science mrm0 Goal: explain the nature of the work of an analytics team to a manager, and enable people on those teams to explain what a data science team needs to a manager. It seems as if every organization wants to enable analytical-decision making and embed analytics into operational processes. What can you do with analytics? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Why is this? Vendors promise easy-to-use analytics tools and services but they rarely deliver. The products may be easy but the work is still hard. Using analytics to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Figuring out what to do and how to do it is a lot harder. Despite this, fancy new tools get all the attention and budget. People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data for analytics is a nuanced, detail-oriented and seemingly dull task left to back-office IT. If your goal is to build a repeatable analytics capability rather than a one-off analytics project then you will need to address the parts that are rarely mentioned. This talk will explain some of the unseen and little-discussed aspects involved when building and deploying analytics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/accelerateunseenworkdatasciencefinal10-18-17-171018190456-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Goal: explain the nature of the work of an analytics team to a manager, and enable people on those teams to explain what a data science team needs to a manager. It seems as if every organization wants to enable analytical-decision making and embed analytics into operational processes. What can you do with analytics? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Why is this? Vendors promise easy-to-use analytics tools and services but they rarely deliver. The products may be easy but the work is still hard. Using analytics to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Figuring out what to do and how to do it is a lot harder. Despite this, fancy new tools get all the attention and budget. People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data for analytics is a nuanced, detail-oriented and seemingly dull task left to back-office IT. If your goal is to build a repeatable analytics capability rather than a one-off analytics project then you will need to address the parts that are rarely mentioned. This talk will explain some of the unseen and little-discussed aspects involved when building and deploying analytics.
Pay no attention to the man behind the curtain - the unseen work behind data science from mark madsen
]]> 842 13 https://cdn.slidesharecdn.com/ss_thumbnails/accelerateunseenworkdatasciencefinal10-18-17-171018190456-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Assumptions about Data and Analysis: Briefing room webcast slides /slideshow/briefing-room-datanative-analysis-architecture/75402628 briefingroomarcadiadataupload04-25-17-170425192725
In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small. ]]>
In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small. ]]> Tue, 25 Apr 2017 19:27:25 GMT /slideshow/briefing-room-datanative-analysis-architecture/75402628 mrm0@slideshare.net(mrm0) Assumptions about Data and Analysis: Briefing room webcast slides mrm0 In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefingroomarcadiadataupload04-25-17-170425192725-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In many ways, moving data is like moving furniture: it&#39;s an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He&#39;ll be briefed by Steve Wooledge of Arcadia Data who will showcase his company&#39;s technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small.
Assumptions about Data and Analysis: Briefing room webcast slides from mark madsen
]]> 539 7 https://cdn.slidesharecdn.com/ss_thumbnails/briefingroomarcadiadataupload04-25-17-170425192725-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Everything Has Changed Except Us: Modernizing the Data Warehouse /slideshow/everything-has-changed-except-us-modernizing-the-data-warehouse/70321811 tdwimunich2016dataarchkeynotethirdnaturefinal-161221001434
Keynote, Munich, June 2016 The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.]]>
Keynote, Munich, June 2016 The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.]]> Wed, 21 Dec 2016 00:14:34 GMT /slideshow/everything-has-changed-except-us-modernizing-the-data-warehouse/70321811 mrm0@slideshare.net(mrm0) Everything Has Changed Except Us: Modernizing the Data Warehouse mrm0 Keynote, Munich, June 2016 The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunich2016dataarchkeynotethirdnaturefinal-161221001434-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Keynote, Munich, June 2016 The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
Everything Has Changed Except Us: Modernizing the Data Warehouse from mark madsen
]]> 1213 4 https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunich2016dataarchkeynotethirdnaturefinal-161221001434-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Pragmatic Approach to Analyzing Customers /slideshow/a-pragmatic-approach-to-analyzing-customers/61689978 customeranalysistdwiexecsummitsandiego2015-160505004313
The business market is different today than it was 20 years ago when BI got started. We're just beginning to grasp how to work within the new economic and communication models. Companies can't rely solely on financial and operational metrics any more, and need to analyze customer behaviors in more detail. The big change in analysis is a move from mass market metrics to individualized data, no longer analyzing or managing by averages. The stream of events and observations available from applications today combined with new platforms for collecting and processing data enables (relatively) easy analysis. Despite this, many companies struggle to analyze customer data. This talk will describe a handful of customer metrics and models that are (relatively) easy to do, yet are often not done. It's often easier to succeed by stringing together a handful of simple techniques rather than applying advanced techniques. Expect to come away from this session with: - a little history of customer data use by marketing and how that has changed in the last 10 years. - the most common behavioral data sources you have available. - some of the basic questions that often go unanswered, and data that is not assessed in the proper context. - some basic analyses you can perform.]]>
The business market is different today than it was 20 years ago when BI got started. We're just beginning to grasp how to work within the new economic and communication models. Companies can't rely solely on financial and operational metrics any more, and need to analyze customer behaviors in more detail. The big change in analysis is a move from mass market metrics to individualized data, no longer analyzing or managing by averages. The stream of events and observations available from applications today combined with new platforms for collecting and processing data enables (relatively) easy analysis. Despite this, many companies struggle to analyze customer data. This talk will describe a handful of customer metrics and models that are (relatively) easy to do, yet are often not done. It's often easier to succeed by stringing together a handful of simple techniques rather than applying advanced techniques. Expect to come away from this session with: - a little history of customer data use by marketing and how that has changed in the last 10 years. - the most common behavioral data sources you have available. - some of the basic questions that often go unanswered, and data that is not assessed in the proper context. - some basic analyses you can perform.]]> Thu, 05 May 2016 00:43:13 GMT /slideshow/a-pragmatic-approach-to-analyzing-customers/61689978 mrm0@slideshare.net(mrm0) A Pragmatic Approach to Analyzing Customers mrm0 The business market is different today than it was 20 years ago when BI got started. We're just beginning to grasp how to work within the new economic and communication models. Companies can't rely solely on financial and operational metrics any more, and need to analyze customer behaviors in more detail. The big change in analysis is a move from mass market metrics to individualized data, no longer analyzing or managing by averages. The stream of events and observations available from applications today combined with new platforms for collecting and processing data enables (relatively) easy analysis. Despite this, many companies struggle to analyze customer data. This talk will describe a handful of customer metrics and models that are (relatively) easy to do, yet are often not done. It's often easier to succeed by stringing together a handful of simple techniques rather than applying advanced techniques. Expect to come away from this session with: - a little history of customer data use by marketing and how that has changed in the last 10 years. - the most common behavioral data sources you have available. - some of the basic questions that often go unanswered, and data that is not assessed in the proper context. - some basic analyses you can perform. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/customeranalysistdwiexecsummitsandiego2015-160505004313-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The business market is different today than it was 20 years ago when BI got started. We&#39;re just beginning to grasp how to work within the new economic and communication models. Companies can&#39;t rely solely on financial and operational metrics any more, and need to analyze customer behaviors in more detail. The big change in analysis is a move from mass market metrics to individualized data, no longer analyzing or managing by averages. The stream of events and observations available from applications today combined with new platforms for collecting and processing data enables (relatively) easy analysis. Despite this, many companies struggle to analyze customer data. This talk will describe a handful of customer metrics and models that are (relatively) easy to do, yet are often not done. It&#39;s often easier to succeed by stringing together a handful of simple techniques rather than applying advanced techniques. Expect to come away from this session with: - a little history of customer data use by marketing and how that has changed in the last 10 years. - the most common behavioral data sources you have available. - some of the basic questions that often go unanswered, and data that is not assessed in the proper context. - some basic analyses you can perform.
A Pragmatic Approach to Analyzing Customers from mark madsen
]]> 1510 11 https://cdn.slidesharecdn.com/ss_thumbnails/customeranalysistdwiexecsummitsandiego2015-160505004313-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Disruptive Innovation: how do you use these theories to manage your IT? /slideshow/disruptive-innovation-how-do-you-use-these-theories-to-manage-your-it/57903954 thirdnaturedisruptiveinnovationtdwi2016-160205010420
The term disruptive innovation was popularized by Harvard professor Clayton Christensen in his 1997 book The Innovators Dilemma. Nearly 20 years later Disrupt! is a popular leadership mantra that is more frequently uttered than experienced. You can't productize it. You can't always control it at least what effects it has in practice. You aren't necessarily going to like every product of innovation. So are you sure you want it? If so, how do you promote a culture in which innovation can flower and, potentially, thrive? Because that's probably the best that you can do. Perhaps there's a better framing for innovation than just "disruption. This session is an overview of commmoditization and innovation theories followed by basic things you can do to apply that theory to your daily job architecting, choosing and managing a data environment in your company. ]]>
The term disruptive innovation was popularized by Harvard professor Clayton Christensen in his 1997 book The Innovators Dilemma. Nearly 20 years later Disrupt! is a popular leadership mantra that is more frequently uttered than experienced. You can't productize it. You can't always control it at least what effects it has in practice. You aren't necessarily going to like every product of innovation. So are you sure you want it? If so, how do you promote a culture in which innovation can flower and, potentially, thrive? Because that's probably the best that you can do. Perhaps there's a better framing for innovation than just "disruption. This session is an overview of commmoditization and innovation theories followed by basic things you can do to apply that theory to your daily job architecting, choosing and managing a data environment in your company. ]]> Fri, 05 Feb 2016 01:04:20 GMT /slideshow/disruptive-innovation-how-do-you-use-these-theories-to-manage-your-it/57903954 mrm0@slideshare.net(mrm0) Disruptive Innovation: how do you use these theories to manage your IT? mrm0 The term disruptive innovation was popularized by Harvard professor Clayton Christensen in his 1997 book The Innovators Dilemma. Nearly 20 years later Disrupt! is a popular leadership mantra that is more frequently uttered than experienced. You can't productize it. You can't always control it at least what effects it has in practice. You aren't necessarily going to like every product of innovation. So are you sure you want it? If so, how do you promote a culture in which innovation can flower and, potentially, thrive? Because that's probably the best that you can do. Perhaps there's a better framing for innovation than just "disruption. This session is an overview of commmoditization and innovation theories followed by basic things you can do to apply that theory to your daily job architecting, choosing and managing a data environment in your company. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thirdnaturedisruptiveinnovationtdwi2016-160205010420-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The term disruptive innovation was popularized by Harvard professor Clayton Christensen in his 1997 book The Innovators Dilemma. Nearly 20 years later Disrupt! is a popular leadership mantra that is more frequently uttered than experienced. You can&#39;t productize it. You can&#39;t always control it at least what effects it has in practice. You aren&#39;t necessarily going to like every product of innovation. So are you sure you want it? If so, how do you promote a culture in which innovation can flower and, potentially, thrive? Because that&#39;s probably the best that you can do. Perhaps there&#39;s a better framing for innovation than just &quot;disruption. This session is an overview of commmoditization and innovation theories followed by basic things you can do to apply that theory to your daily job architecting, choosing and managing a data environment in your company.
Disruptive Innovation: how do you use these theories to manage your IT? from mark madsen
]]> 2781 11 https://cdn.slidesharecdn.com/ss_thumbnails/thirdnaturedisruptiveinnovationtdwi2016-160205010420-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Briefing room: An alternative for streaming data collection /slideshow/briefing-room-an-alternative-for-streaming-data-collection/56216751 briefingroom-thirdnatureextrahop-streamingdata2015-151216194859
Knowing whats happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. Hell be briefed by Erik Giesa of ExtraHop, who will showcase his companys stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. Hell share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis. ]]>
Knowing whats happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. Hell be briefed by Erik Giesa of ExtraHop, who will showcase his companys stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. Hell share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis. ]]> Wed, 16 Dec 2015 19:48:59 GMT /slideshow/briefing-room-an-alternative-for-streaming-data-collection/56216751 mrm0@slideshare.net(mrm0) Briefing room: An alternative for streaming data collection mrm0 Knowing whats happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. Hell be briefed by Erik Giesa of ExtraHop, who will showcase his companys stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. Hell share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-thirdnatureextrahop-streamingdata2015-151216194859-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Knowing whats happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. Hell be briefed by Erik Giesa of ExtraHop, who will showcase his companys stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. Hell share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Briefing room: An alternative for streaming data collection from mark madsen
]]> 838 6 https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-thirdnatureextrahop-streamingdata2015-151216194859-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Building the Enterprise Data Lake: A look at architecture /mrm0/building-the-enterprise-data-lake-a-look-at-architecture thirdnaturesnaplogicdatalakefinal2015-12-08-151208194050-lva1-app6892
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover: Why dumping data into Hadoop and letting users get it out doesn't work The difference between a Hadoop application and a Data Lake Why new ideas about data architecture are a key element An Enterprise Data Lake reference architecture to frame what must be built ]]>
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover: Why dumping data into Hadoop and letting users get it out doesn't work The difference between a Hadoop application and a Data Lake Why new ideas about data architecture are a key element An Enterprise Data Lake reference architecture to frame what must be built ]]> Tue, 08 Dec 2015 19:40:50 GMT /mrm0/building-the-enterprise-data-lake-a-look-at-architecture mrm0@slideshare.net(mrm0) Building the Enterprise Data Lake: A look at architecture mrm0 The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover: Why dumping data into Hadoop and letting users get it out doesn't work The difference between a Hadoop application and a Data Lake Why new ideas about data architecture are a key element An Enterprise Data Lake reference architecture to frame what must be built <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thirdnaturesnaplogicdatalakefinal2015-12-08-151208194050-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover: Why dumping data into Hadoop and letting users get it out doesn&#39;t work The difference between a Hadoop application and a Data Lake Why new ideas about data architecture are a key element An Enterprise Data Lake reference architecture to frame what must be built
Building the Enterprise Data Lake: A look at architecture from mark madsen
]]> 4667 8 https://cdn.slidesharecdn.com/ss_thumbnails/thirdnaturesnaplogicdatalakefinal2015-12-08-151208194050-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Briefing Room analyst comments - streaming analytics /slideshow/briefing-room-analyst-comments-streaming-analytics/54973623 briefingroom-striimthirdnaturestreaminganalytics2015-151110222237-lva1-app6892
際際滷s for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 ) Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battle field by designing on-ramps to the future of streaming analytics. Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He'll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds at a fraction of the cost and complexity of legacy systems. Several case studies will be shared.]]>
際際滷s for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 ) Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battle field by designing on-ramps to the future of streaming analytics. Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He'll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds at a fraction of the cost and complexity of legacy systems. Several case studies will be shared.]]> Tue, 10 Nov 2015 22:22:37 GMT /slideshow/briefing-room-analyst-comments-streaming-analytics/54973623 mrm0@slideshare.net(mrm0) Briefing Room analyst comments - streaming analytics mrm0 際際滷s for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 ) Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battle field by designing on-ramps to the future of streaming analytics. 鐃緒申Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He'll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds at a fraction of the cost and complexity of legacy systems. Several case studies will be shared. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-striimthirdnaturestreaminganalytics2015-151110222237-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 ) Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battle field by designing on-ramps to the future of streaming analytics. 鐃緒申Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He&#39;ll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds at a fraction of the cost and complexity of legacy systems. Several case studies will be shared.
Briefing Room analyst comments - streaming analytics from mark madsen
]]> 893 7 https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-striimthirdnaturestreaminganalytics2015-151110222237-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Everything has changed except us /slideshow/everything-has-changed-except-us/53991890 tdwilasvegas2015dataarchkeynotethirdnaturefinal-151015200209-lva1-app6891
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.]]>
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.]]> Thu, 15 Oct 2015 20:02:09 GMT /slideshow/everything-has-changed-except-us/53991890 mrm0@slideshare.net(mrm0) Everything has changed except us mrm0 The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tdwilasvegas2015dataarchkeynotethirdnaturefinal-151015200209-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years. The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes. The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We dont need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors wont solve the problem. The data we want to use is challenging. We cant model and clean and maintain it fast enough. We dont need more data modeling to solve this problem. We need less modeling and more metadata. And lastly, a change in scale has occurred. It isnt a simple problem of big. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses. In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
Everything has changed except us from mark madsen
]]> 1596 6 https://cdn.slidesharecdn.com/ss_thumbnails/tdwilasvegas2015dataarchkeynotethirdnaturefinal-151015200209-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Bi isn't big data and big data isn't BI (updated) /slideshow/bi-isnt-big-data-and-vice-versa-updated/52902387 biisntbigdataandviceversatdwibostonchapter09-15-15-150917172259-lva1-app6892
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level. Yes, its about the data normally called big, but its not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I wont be teaching you to write MapReduce jobs or anything of the sort. The first part will be an overview of the types, formats and structures of data that arent normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture. The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area. ]]>
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level. Yes, its about the data normally called big, but its not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I wont be teaching you to write MapReduce jobs or anything of the sort. The first part will be an overview of the types, formats and structures of data that arent normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture. The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area. ]]> Thu, 17 Sep 2015 17:22:59 GMT /slideshow/bi-isnt-big-data-and-vice-versa-updated/52902387 mrm0@slideshare.net(mrm0) Bi isn't big data and big data isn't BI (updated) mrm0 Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level. Yes, its about the data normally called big, but its not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I wont be teaching you to write MapReduce jobs or anything of the sort. The first part will be an overview of the types, formats and structures of data that arent normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture. The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/biisntbigdataandviceversatdwibostonchapter09-15-15-150917172259-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Big data is hyped, but isn&#39;t hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn&#39;t big data, and big data isn&#39;t BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level. Yes, its about the data normally called big, but its not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I wont be teaching you to write MapReduce jobs or anything of the sort. The first part will be an overview of the types, formats and structures of data that arent normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture. The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area.
Bi isn't big data and big data isn't BI (updated) from mark madsen
]]> 1415 4 https://cdn.slidesharecdn.com/ss_thumbnails/biisntbigdataandviceversatdwibostonchapter09-15-15-150917172259-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 On the edge: analytics for the modern enterprise (analyst comments) /slideshow/on-the-edge-analytics-for-the-modern-enterprise-analyst-comments/51743586 briefingroom-cisco-thirdnaturestreaminganalytics2015-150818011613-lva1-app6892
On the Edge: Analytics for the Modern Enterprise [these are the analyst comments on enterprise data architecture and streaming] Webcast description: The speed of business today requires new approaches to generating and leveraging analytics. Latencies of a day, an hour or even minutes no longer suffice in many situations. For these use cases, organizations must embrace analytics at the edge: a process that involves targeted number-crunching at the fringe of the enterprise. When designed properly, these systems give companies a leg up on their competitors.Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a new era of information architectures is now unfolding, paving the way to much more responsive and agile business models. He'll be briefed by Kim Macpherson of the Cisco Data and Analytics Business Unit, who will explain how her company's platform is uniquely suited for this new, federated analytic paradigm. She'll demonstrate how edge analytics can help companies address opportunities quickly and effectively.]]>
On the Edge: Analytics for the Modern Enterprise [these are the analyst comments on enterprise data architecture and streaming] Webcast description: The speed of business today requires new approaches to generating and leveraging analytics. Latencies of a day, an hour or even minutes no longer suffice in many situations. For these use cases, organizations must embrace analytics at the edge: a process that involves targeted number-crunching at the fringe of the enterprise. When designed properly, these systems give companies a leg up on their competitors.Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a new era of information architectures is now unfolding, paving the way to much more responsive and agile business models. He'll be briefed by Kim Macpherson of the Cisco Data and Analytics Business Unit, who will explain how her company's platform is uniquely suited for this new, federated analytic paradigm. She'll demonstrate how edge analytics can help companies address opportunities quickly and effectively.]]> Tue, 18 Aug 2015 01:16:13 GMT /slideshow/on-the-edge-analytics-for-the-modern-enterprise-analyst-comments/51743586 mrm0@slideshare.net(mrm0) On the edge: analytics for the modern enterprise (analyst comments) mrm0 On the Edge: Analytics for the Modern Enterprise [these are the analyst comments on enterprise data architecture and streaming] Webcast description: The speed of business today requires new approaches to generating and leveraging analytics. Latencies of a day, an hour or even minutes no longer suffice in many situations. For these use cases, organizations must embrace analytics at the edge: a process that involves targeted number-crunching at the fringe of the enterprise. When designed properly, these systems give companies a leg up on their competitors.鐃緒申Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a new era of information architectures is now unfolding, paving the way to much more responsive and agile business models. He'll be briefed by Kim Macpherson of the Cisco Data and Analytics Business Unit, who will explain how her company's platform is uniquely suited for this new, federated analytic paradigm. She'll demonstrate how edge analytics can help companies address opportunities quickly and effectively. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-cisco-thirdnaturestreaminganalytics2015-150818011613-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> On the Edge: Analytics for the Modern Enterprise [these are the analyst comments on enterprise data architecture and streaming] Webcast description: The speed of business today requires new approaches to generating and leveraging analytics. Latencies of a day, an hour or even minutes no longer suffice in many situations. For these use cases, organizations must embrace analytics at the edge: a process that involves targeted number-crunching at the fringe of the enterprise. When designed properly, these systems give companies a leg up on their competitors.鐃緒申Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a new era of information architectures is now unfolding, paving the way to much more responsive and agile business models. He&#39;ll be briefed by Kim Macpherson of the Cisco Data and Analytics Business Unit, who will explain how her company&#39;s platform is uniquely suited for this new, federated analytic paradigm. She&#39;ll demonstrate how edge analytics can help companies address opportunities quickly and effectively.
On the edge: analytics for the modern enterprise (analyst comments) from mark madsen
]]> 999 8 https://cdn.slidesharecdn.com/ss_thumbnails/briefingroom-cisco-thirdnaturestreaminganalytics2015-150818011613-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-mrm0-48x48.jpg?cb=1633653373 President, Third Nature, Inc. I help companies create strategies and designs for analytics and data. My work covers all aspects of information delivery and data management. The primary focus of the company is new and emerging technology and practices in these areas. My experience covers a broad range of roles in vendors and business, with a focus on information management and analytics. I've received awards for technology and business innovation, including Smithsonian/Computerworld, APQC, International Benchmark Clearinghouse, and the Data Warehousing Institute. Twitter: @markmadsen thirdnature.net https://cdn.slidesharecdn.com/ss_thumbnails/2021-05linkedinlive-dataarchitecturev2-210622040425-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-architecture-omg-its-made-of-people/249443077 Data Architecture: OMG... https://cdn.slidesharecdn.com/ss_thumbnails/tdwimunichkeynotethirdnaturemadsen03-2021-210528051222-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/solve-user-problems-data-architecture-for-humans/248655677 Solve User Problems: D... https://cdn.slidesharecdn.com/ss_thumbnails/oreillyailondon2019-10final-191017133849-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-black-box-interpretability-reproducibility-and-data-management-183143344/183143344 The Black Box: Interpr...