The data economy is driving an incredible rate of innovation. New job roles are emerging, existing job roles are evolving. While much of the hype has focused on the data scientist role it's just one of many.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change.
Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.
Presented at OSCON Portland July 18, 2019
Usama Fayyad talk in South Africa: From BigData to Data ScienceUsama Fayyad
Public talk by Barclays CDO Usama Fayyad in South Africa: both at University of Pretoria (GIBS) - Johannesburg and at Workshop17 in Capetown July 14-15, 2015
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
Who is accountable for building and expanding your competitive moat?
Investing in foundational data infrastructure, training, logging, and tools
Fostering executive support for exploration and innovation, including user-facing data product and algorithm development
How to evaluate new machine intelligence projects and develop a portfolio that delivers
How AI product management differs from traditional product management
How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change.
Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.
Presented at OSCON Portland July 18, 2019
Usama Fayyad talk in South Africa: From BigData to Data ScienceUsama Fayyad
Public talk by Barclays CDO Usama Fayyad in South Africa: both at University of Pretoria (GIBS) - Johannesburg and at Workshop17 in Capetown July 14-15, 2015
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
Who is accountable for building and expanding your competitive moat?
Investing in foundational data infrastructure, training, logging, and tools
Fostering executive support for exploration and innovation, including user-facing data product and algorithm development
How to evaluate new machine intelligence projects and develop a portfolio that delivers
How AI product management differs from traditional product management
How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
In this paper the author provides a brief history of “Atelier Populaire” or Popular workshop and the situationist political movement responsible for its creation. The workshop was established to design and produce art to inspire and support situationist objectives during the Paris political protests and uprising of May 1968. The protests, resultant strikes and insurrection caused the temporary breakdown of functioning government in France.
These events came to influence many of those who were to be involved in the establishment of the “punk” movement emerging in London during the mid 1970’s including Malcolm McLaren and Jamie Ried. McLaren, allegedly, a participant in the riots and the artist Jamie Reid were aligned politically and openly sympathetic to situationist objectives and both cited the movement as being critically influential on them and the future visual aesthetic of punk.
The author suggests the influence of the work of Atelier Populair extends beyond that of 1968 and the punk aesthetic of the 1970’s evident in the posters and record sleeves designed by Reid for McLaren and the Sex Pistols, the fashion and Vivienne Westwood’s designs sold through Seditionaries and the emergent “fanzine art” reproduced in publications such as “Sniffin glue”.
Atelier Populaire, or more accurately, the artists themselves have, in a strictly anti-capitalist stance, continually resisted approaches to exhibit or reproduce their work. In contrast, Never Mind the Paradox, the artefacts of the punk continue to be commodified and exploited for commercial gain and influence politics, art, fashion and culture today.
Data Curation: Retooling the Existing WorkforceSteven Miller
My presentation given at the Symposium on Digital Curation in the Era of Big Data: Career Opportunities and Educational Requirements held at the National Academy of Sciences on July 19, 2012.
CRM is like 'Whack-A-Mole' trying to hit moving targets: customers. Longtime entrepreneur Steve Harmon shares some insights from his talk at a recent conference on how to beat the game not with bigger hammers but different tools.
Diferencias Entre Google Analytics, Urchin 6 y el nuevo Urchin 7 de GoogleGuillermo Vilarroig
Listado de diferencias entre Google Analytics y el nuevo Urchin 7 software. Valoraciones de cuándo es mejor utilizar cada uno de ellos.
Actualización septiembre 2010!!!!
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
In this paper the author provides a brief history of “Atelier Populaire” or Popular workshop and the situationist political movement responsible for its creation. The workshop was established to design and produce art to inspire and support situationist objectives during the Paris political protests and uprising of May 1968. The protests, resultant strikes and insurrection caused the temporary breakdown of functioning government in France.
These events came to influence many of those who were to be involved in the establishment of the “punk” movement emerging in London during the mid 1970’s including Malcolm McLaren and Jamie Ried. McLaren, allegedly, a participant in the riots and the artist Jamie Reid were aligned politically and openly sympathetic to situationist objectives and both cited the movement as being critically influential on them and the future visual aesthetic of punk.
The author suggests the influence of the work of Atelier Populair extends beyond that of 1968 and the punk aesthetic of the 1970’s evident in the posters and record sleeves designed by Reid for McLaren and the Sex Pistols, the fashion and Vivienne Westwood’s designs sold through Seditionaries and the emergent “fanzine art” reproduced in publications such as “Sniffin glue”.
Atelier Populaire, or more accurately, the artists themselves have, in a strictly anti-capitalist stance, continually resisted approaches to exhibit or reproduce their work. In contrast, Never Mind the Paradox, the artefacts of the punk continue to be commodified and exploited for commercial gain and influence politics, art, fashion and culture today.
Data Curation: Retooling the Existing WorkforceSteven Miller
My presentation given at the Symposium on Digital Curation in the Era of Big Data: Career Opportunities and Educational Requirements held at the National Academy of Sciences on July 19, 2012.
CRM is like 'Whack-A-Mole' trying to hit moving targets: customers. Longtime entrepreneur Steve Harmon shares some insights from his talk at a recent conference on how to beat the game not with bigger hammers but different tools.
Diferencias Entre Google Analytics, Urchin 6 y el nuevo Urchin 7 de GoogleGuillermo Vilarroig
Listado de diferencias entre Google Analytics y el nuevo Urchin 7 software. Valoraciones de cuándo es mejor utilizar cada uno de ellos.
Actualización septiembre 2010!!!!
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find more Data-Ed webinars here: www.datablueprint.com
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Conceptual vs. Logical vs. Physical Data ModelingDATAVERSITY
A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
DataEd Webinar: Reference & Master Data Management - Unlocking Business ValueDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Every organization produces and consumes data. Because data is so important to day to day operations, data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, NoSQL, data scientist, etc., to seek solutions for their fundamental issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data effort. It is a vital activity that supports the solutions driving your business.
This webinar will address fundamental data modeling methodologies, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Learning Objectives:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Want to know more about Common Data Model and Service? You need to understant what's the difference between CDS for Apps and Analytics? Feel free to use these slides and send me your feed backs.
Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges can often trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from reoccurring.
Learning objectives:
-Help you understand foundational Data Quality concepts for improving Data Quality at your organization
-Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
-Share case studies illustrating the hallmarks and benefits of Data Quality success
Align Business Data & Analytics for Digital TransformationPerficient, Inc.
Your success in the digital world relies primarily on how well you manage and analyze the data coming from disparate internal systems and external channels. You need to understand how to innovate and leverage digital data to drive sales and productivity.
Existing principles driving traditional data architecture are inadequate to support the volume, variety, and velocity of this new data ecosystem. In these scenarios, information governance (master data management, metadata, data quality and data governance) becomes highly critical in terms of providing the context for operational, competitive and advanced analytics.
Companies require a data architecture and strategy that can support efficient digital transformation by unlocking the value in all data sources to provide mission-critical insights and informed decision-making.
Our experts covered:
-Five information management pillars necessary for digital transformation
-Stages of digital information maturity, reflecting the typical path of an organization implementing this new data ecosystem
-Issues, challenges, and approaches to governing this new architecture
Data-Ed Online Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is data architecture that organizes data assets so they can be used in your business strategy to create real business value. Even though this is important, data architectures are still being used ineffectively. The various uses of data architecture are referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecture to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will maximize your organization’s competitive advantage.
Takeaways:
•How to utilize data architecture to address a broad variety of organizational challenges and how to utilize data architectures in support of business strategy
•Understanding foundational data architecture concepts based on the DAMA DMBOK
•Data architecture guiding principles & best practices
Preparing the next generation for the cognitive era - NFAIS KeynoteSteven Miller
Keynote address at NFAIS 2016 in Philadelphia PA on February 21st 2016 focused on how the Cogntive Era is transforming our lives, creating new careers, and inspiring innovation.
Preparing the next generation for the cognitive eraSteven Miller
Short version of my latest presentation used during a panel session at the ASA Research Symposium at Southern Illinois University Carbondale on November 21st 2015
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
The Building Blocks of QuestDB, a Time Series Database
Big Data Careers
1. @ 2014 IBM Corporation
It’s all about the data
April 2014
How All Data is Changing Everything
Career Perspectives
2. @ 2014 IBM Corporation@ 2014 IBM Corporation
Internet of Things
Mobile
Social
Cloud
E-commerce
E-content
Trends Shaping the future
3. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data makes everything possible…
4. @ 2014 IBM Corporation@ 2014 IBM Corporation
Monetization
Brokering
Differentiation
R. “Ray” Wang
http://blogs.hbr.org/2012/12/what-a-big-data-business-model/
Trends Shaping the future of Data
5. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
6. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://www.cutter.com/bia/fulltext/reports/2013/02/index.html
http://ibmdatamag.com/2013/02/i-am-an-information-strategist
7. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data Policy is fast growing too
http://jobs.walmart.com/us/united-states/legal,-compliance-and-
ethics/jobid4414643-senior-manager-business-analytics-%EF%B9%A0-strategy-ethics
8. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data skills needed elsewhere too
Law & Government
– Data law
– Open Data
Business
– Business schools are aggressively tackling analytics, but building skills to address larger
implications of data is often a gap:
• MBA programs, business analytics programs, and information systems programs (which
often reside within business schools) typically have minimal curriculum spanning the larger
issues of data
Literacy for all (students & professionals)
– Data literacy
– Analytics literacy
Field specific competency
– Bio/health informatics is just the beginning
http://bit.ly/XZcji7
http://bit.ly/15A1PIE
9. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data Scientists
Data Engineers
Chief Data Officers
Data Policy Roles
Big Data Architects
Visual Analysts
Big Data Developers
Business Analysts
Emerging & evolving Big Data job roles
10. @ 2014 IBM Corporation@ 2014 IBM Corporation
Today’s reality
– It’s early days – anyone who wants to call themselves a data scientist – can and does
– No consensus
• Is someone who applies ‘data science’ skill a data scientist?
• Or is a data scientist something more?
What is a data scientist?
11. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data Scientists are curious: exploring, asking questions, doing “what if” analysis,
questioning existing assumptions and processes
Discipline incorporates skills across:
• Data Strategy, Governance, Ethics
• Analytics (descriptive, predictive, prescriptive, decisive)
• Data Visualization, Storytelling
• Computer Programming
• Data Mining
• Data Engineering
• Machine Learning
Example Programs:
Common titles:
• Data Scientist
• Decision Scientist
• Analytics Specialist
• Predictive Modeler
• Data Visualization Specialist
• Machine Learning Engineer
• Computational Scientist
Master of Science in AnalyticsData Science Certificate
12. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data Engineers can select, engineer, and deploy the data layer for web-scale
and big data analytics solutions collaborating with full-stack developers, IT
architects, and data scientists.
Discipline incorporates skills across:
• Computer Programming
• Data Engineering
• Data Architecture
• Data Integration
• Data Modeling
• Data Quality
Example Programs:
Common titles:
• Data Engineer
• Data Integration Engineer
• Data Infrastructure Engineer
• Data Layer Developer
• Data Modeler
• Database Designer
• Integration Developer
MS in Computer
Information Systems
13. @ 2014 IBM Corporation@ 2014 IBM Corporation
Chief Data Officers understand how to use data to create strategic
opportunities; responsible for organizational data strategy & governance
Discipline incorporates skills across:
• Data Strategy
• Data Policy, Governance, Law, Ethics
• Enterprise & Cloud Data Architecture
• Data Engineering
• Data Warehousing
• Information Integration
• Data Science & Analytics
Example Programs:
Where will the CDO come from?
• Data Architect
• Data Engineer
• Data Warehouse / Business Intelligence
Architect
• Information Architect
• Information Strategist
• Director/VP Data Management
14. @ 2014 IBM Corporation@ 2014 IBM Corporation
Data Policy Pros strategically manage data across the enterprise, while ensuring
high levels of data quality, integrity, availability, security, and privacy.
Discipline incorporates skills across:
• Data Architecture
• Data Lifecycle / Data Supply Chain
• Data Privacy & Security
• Data Quality
• Master Data Management
• Understanding & compliance with
business rules; laws & regulations
Example Programs:
Common titles:
• Data Curator / Steward
• Data Governance Specialist / Lead
• Data Architect - Governance
• Data Privacy Manager
• Compliance Manager - Data
Management
• Master Data Management Architect
Master of Information
Management
MS in Information
Management
15. @ 2014 IBM Corporation@ 2014 IBM Corporation
Big Data Architects create enduring data blueprints to enable an organization to
take strategic advantage of all data, big or small. Ensures the data layer
architecture provides enterprise scale services.
Discipline incorporates skills across:
• Enterprise IT / Data Architecture
• Database Engineering / Performance
Optimization
• Data Governance
• Data Warehousing
• Information Integration
• Data Science & Analytics
Example Programs:
Common titles:
• Data Architect
• Data Warehouse Architect
• Business Intelligence Architect
• Data Engineer
• Information Architect
• Information Strategist
• Director/VP Data Management
Masters in Information
Technology & Management
MS in Information Systems
16. @ 2014 IBM Corporation@ 2014 IBM Corporation
Visual Analysts promote the effective use of interactive visual information
systems in dealing with complex and data-rich problems in organizations & society
Discipline incorporates skills across:
• Visual Information Systems
• Data Storytelling / Narrative
• Human Cognition
• Statistical Modeling
• Analytical Reasoning
• Data Management
• Information Privacy
Example Programs:
Common titles:
• Visualization Developer / Engineer
• Visualization Data / Information Specialist
• Visual Designer
• Director, Visual Analysis
• Visual Analytics Researcher
• Data / Digital Storyteller
Certificate in Visual Analytics
17. @ 2014 IBM Corporation@ 2014 IBM Corporation
Big Data Developers combine skills in software engineering, statistics & machine
learning, and complex systems architectures to manage data at extreme scale.
Discipline incorporates skills across:
• Computer Science
• Computational Analytics
• Machine Learning
• Database Engineering
• Systems Engineering & Architecture
Example Programs:
Common titles:
• NoSQL* software engineer / architect
• Machine Learning software engineer /
architect
• Algorithms engineer / designer
• BI / analytics developer / architect
MS in Computer Science
18. @ 2014 IBM Corporation@ 2014 IBM Corporation
Business Analysts combine deep analytics skills with strong communication skills
and strategic mind set to transform data into a competitive asset.
Discipline incorporates skills across:
• Data Driven Business Strategy
• Industry specific experience
• Critical & Analytical Methods
• Analytics Driven Decision Making
• Communicate Analytical Findings
• Understanding of legal & ethical
implications of data analysis
Example Programs:
Common titles:
• Business Analyst
• Data Analyst
• Market Analyst
• Financial Analyst
• Supply Chain Analyst
• Business Planning
• Business Strategist
Msc in Business AnalyticsMS in Business Analytics
MS in Marketing Analytics
19. @ 2014 IBM Corporation@ 2014 IBM Corporation
Big Data University
IBMdatamag
Academic Initiative
Faculty Grants
Big Data & Analytics EdCon 2013
Analytics Zone
Visualizations
Select IBM big data & analytics resources
20. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://www.ibm.com/developerworks/bigdata/
21. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://bigdatauniversity.com
22. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://IBMdatamag.com
23. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://ibm.com/ibm/university/academic/pub/page/academic_initiative
24. @ 2014 IBM Corporation@ 2014 IBM Corporation
IBM University Programs
Academic Awards for Information & Analytics Curriculum Development
2014 Program is now open. Seeking
proposals for new curricula addressing
areas of interest:
Internet of Everything, anything imaginable can be sensed,
measured, controlled, analyzed, …Open Data
Context aware computing
Analytics for everything imaginable
Data Journalism
Evidence-based decision making
Shift from Systems of Record to Systems of Engagement
Data Policy: Both literacy and deep talent with privacy,
security, and ethics skill
Web-scale systems to planet-scale systems
Natural Language being used in all forms of engagement
with systems that can understand, optimize and respond
2013 program
92 proposals from
– 75 universities in 23 countries
16 Winners from 8 countries
– Czechoslovakia, France, Mexico, Netherlands,
Russia, Turkey, United Kingdom, United States
http://www-03.ibm.com/press/us/en/pressrelease/41733.wss
Goal: Foster collaboration between faculty at leading universities worldwide and IBM to
develop talent and skilled resources through advanced curriculum development.
To learn more, contact your IBM
university liaison or Jeffrey Brody
brody@us.ibm.com
25. @ 2014 IBM Corporation@ 2014 IBM Corporation
Big Data & Analytics EdCon
http://edcon2013.ischool.syr.edu/
Talks from 2013 :
https://www.youtube.com/channel/UCdZVIx65ufDnYJSooTNuUeA
2014 call for papers & details will be announced soon
26. @ 2014 IBM Corporation@ 2014 IBM Corporation
https://www.analyticszone.com
27. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://vottrave.ottawa.ibm.com/visualizations.html
28. @ 2014 IBM Corporation@ 2014 IBM Corporation
http://www-958.ibm.com/software/analytics/labs/manyeyes/
29. @ 2014 IBM Corporation@ 2014 IBM Corporation
@brandsteve
Editor's Notes
http://www.viva-viva.cahttp://students.sfu.ca/calendar/interactive-arts-technology/visual-analytics-grad-cert.htmlNeed to verify if Middlesex is partnered with IBM in any way.