This document provides an overview of ontologies and how they can power artificial intelligence. It begins with biographies of Seth Earley, CEO and founder of Earley Information Science, who has over 20 years of experience in data science, technology, content management, and knowledge management. The document then discusses how ontologies can be used to describe domains of information and the relationships between taxonomies, thesauruses, and ontologies. It provides examples of how ontologies have been used by organizations like the Cleveland Museum of Art for traffic pattern analysis and by Allstate for semantic deconstruction. The document argues that ontologies can be applied to challenges like chatbots, question answering systems, and conversational commerce. It also discusses
Slide deck from a webinar presented by Earley Information Science on How to Use Site Search to Drive Conversions and Create Customers. Features EIS Taxonomists John Phillips and Brian Eisenberg.
Why does your organization need IOA trained professionals? What are the AIIM IOA certificate courses like? These questions and more are answered in this one hour presentation.
Search for the enterprise seems to have hit a wall. Bad search is the top complaint of users interacting with their internal data. Meanwhile, there is a seemingly never-ending flood of products, SaaS offerings and new solutions in the market all claiming and attempting to solve the problem.
In this roundtable, we will define what expectations organizations should really have about their search platforms and discuss what benefits to expect from using techniques like boosting, auto-classification, natural language processing, query expansion, entity extraction and ontologies. We will also explore what will supersede search in the enterprise.
Engaging with customers and providing an excellent customer experience depends on several capabilities:
having the right customer facing tools and technologies,
integrating internal sources of customer information to provide a clear picture of who they are,
and providing content needed to solve problems and meet customer needs in the context of their task.
The last is particularly challenging and requires that marketing organizations remove sources of friction in the content creation and management process.
In this month’s executive roundtable, we will discuss how improvements to search, content processes and data quality can all be achieved through a multi-faceted program to streamline knowledge management and collaboration and metrics that tie together seemingly disparate processes – such as customer satisfaction scores with data quality.
Intelligent Virtual Assistants, also known as Intelligent Digital Assistants, are capturing market share rapidly. As analytics and AI technologies scale, and as some standard models begin to emerge, business are starting to consider how to introduce these kinds of solutions into their customer experiences. This white paper, "Making Intelligent Virtual Assistants a Reality" attempts to demystify multiple aspects of the intelligent application ecosystem.
What kind of useful business problems can be solved by Virtual Assistants?
What are the technologies that are behind creating a Virtual Assistant, and how many new capabilities need to be integrated into the enterprise to build and deliver a Virtual Assistant?
What kind of content, knowledge representation, information architecture, assets and business processes are needed to deliver a Virtual Assistant experience?
What skills, techniques and expertise are needed of deliver a Virtual Assistant solution to the market?
Learn what is required to design and build an Intelligent Virtual Assistant, and how to deploy intelligent applications in your enterprise to achieve real business value.
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what knowledge graphs are, how they compare to ontologies, and how they can be used to power AI driven applications.
Slide deck from a webinar presented by Earley Information Science on How to Use Site Search to Drive Conversions and Create Customers. Features EIS Taxonomists John Phillips and Brian Eisenberg.
Why does your organization need IOA trained professionals? What are the AIIM IOA certificate courses like? These questions and more are answered in this one hour presentation.
Search for the enterprise seems to have hit a wall. Bad search is the top complaint of users interacting with their internal data. Meanwhile, there is a seemingly never-ending flood of products, SaaS offerings and new solutions in the market all claiming and attempting to solve the problem.
In this roundtable, we will define what expectations organizations should really have about their search platforms and discuss what benefits to expect from using techniques like boosting, auto-classification, natural language processing, query expansion, entity extraction and ontologies. We will also explore what will supersede search in the enterprise.
Engaging with customers and providing an excellent customer experience depends on several capabilities:
having the right customer facing tools and technologies,
integrating internal sources of customer information to provide a clear picture of who they are,
and providing content needed to solve problems and meet customer needs in the context of their task.
The last is particularly challenging and requires that marketing organizations remove sources of friction in the content creation and management process.
In this month’s executive roundtable, we will discuss how improvements to search, content processes and data quality can all be achieved through a multi-faceted program to streamline knowledge management and collaboration and metrics that tie together seemingly disparate processes – such as customer satisfaction scores with data quality.
Intelligent Virtual Assistants, also known as Intelligent Digital Assistants, are capturing market share rapidly. As analytics and AI technologies scale, and as some standard models begin to emerge, business are starting to consider how to introduce these kinds of solutions into their customer experiences. This white paper, "Making Intelligent Virtual Assistants a Reality" attempts to demystify multiple aspects of the intelligent application ecosystem.
What kind of useful business problems can be solved by Virtual Assistants?
What are the technologies that are behind creating a Virtual Assistant, and how many new capabilities need to be integrated into the enterprise to build and deliver a Virtual Assistant?
What kind of content, knowledge representation, information architecture, assets and business processes are needed to deliver a Virtual Assistant experience?
What skills, techniques and expertise are needed of deliver a Virtual Assistant solution to the market?
Learn what is required to design and build an Intelligent Virtual Assistant, and how to deploy intelligent applications in your enterprise to achieve real business value.
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what knowledge graphs are, how they compare to ontologies, and how they can be used to power AI driven applications.
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains how advanced concepts in information architecture, such as ontologies and knowledge engineering, are the basis for streamlined content workflows.
In this session Seth Earley, author of the AI Powered Enterprise, discusses how to harness the power of artificial intelligence to drive extraordinary competitive advantage.
Meaningful Metrics - Aligning Operational Metrics with Marketing & Customer E...Earley Information Science
Analytics and big data are the buzzwords de jour. But what is meaningful and how can success be measured in a tangible way? Marketing campaign dashboards, user behavior BI reports and on site clickstream data need to be correlated and interpreted in an actionable way, otherwise business owners will be quickly overwhelmed with data without deriving insights that can guide action.
This roundtable will revisit the topic of analytics and discuss practices for closing the data=>insight=>action loop.
Earley Executive Roundtable on Data Analytics - Session 1 - The Business Pote...Earley Information Science
Business Potential of Machine Learning and Cognitive Computing. Speakers include:
– Bruce Daley Principal Analyst, Tractica (@brucedaley)
– Olly Downs, Chief Scientist/CTO, Globys (@globysinc )
– Mitchell Shuster, Data Scientist, Knowledgent (@Knowledgent)
– Patrick Heffernan, Practice Manager, TBR (@TBR_PatrickH)
Governance is the glue that holds various content, knowledge and data management initiatives together. It is increasingly necessary as a component of customer experience and marketing automation and integration initiatives. The challenge is that governance is not an exciting topic and it is difficult to get participation and buy in at the correct levels of the organization. How do you retain interest in these kinds of necessary programs? The answer is to tie governance to measurement of program and project progress, success and operations. Once governance is aligned with objectives and clearly defined measurement, the organization will focus the correct level of attention and governance will be successful.
This webinar will cover the challenges associated with data governance and the business impact of poor data quality on digital marketing programs and knowledge management systems. Expert panel members will discuss real-world examples of data governance best practices, how to avoid the common pitfalls and how to put a framework for a successful metrics-driven governance process in place.
Earley Executive Roundtable for May 2016. Topic: Predictive Analytics, AI and the Promise of Personalization. Panelists are Seth Earley, EIS; Julie Penzott, Amplero; Adam Pease, Articulate Software. Host: Dino Eliopulos, EIS
Tagging isn’t new - it’s been around for a dog’s age in internet years. But in the past few years some fresh ideas and tools have reinvigorated the social tagging world. These new approaches include an attempt to improve findability through a bit of structure and control. While the idea of adding control to folksonomy seems like going against the whole selling point of social tagging (flexibility, openness), it is bringing the tagging to a new level, making it more viable for practical use in enterprises. This session will present hybrid approaches to formal taxonomies and social tagging. How can they be used in the corporate environment? What type of content is appropriate for social tagging? What kind of software is available for the enterprise? Learn how social tagging is not necessarily anathema to corporate taxonomy programs and how this hybrid approach can bring the best of both worlds: a fresh, up to date taxonomy with the structure needed to improve information findability.
Key Takeaways:
Folksonomy and taxonomy defined
Drawbacks of pure social tagging
Social tagging in the enterprise
Hybrid taxonomy & folksonomy approaches: Four models
Earley Executive Roundtable - Building a Digital Transformation Roadmap
Panelists:
Seth Earley, CEO, Earley Information Science (@sethearley)
Paul Wlodarczyk, VP, Client ServicesEarley Information Science (@twitcontentguy)
Information Driven Enterprise Architecture - Connected Brains 2018LoQutus
In this session we will show you how to deliver business
improvements with enterprise and information architecture. During this session you will get new insights to increase value to the organisation. We also explain our LEAF framework, an integrated EA framework leveraging information to build architectures more quickly.
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
Artificial intelligence (AI) is getting lots of attention but one key aspect is often overlooked, understated, or underestimated: the quality of “training” information and the structure of that information – the Information Architecture or “IA”. AI only works when it has the data it needs to spot trends, identify patterns and provide functionality – especially when it comes to chatbots and other so called “cognitive” technologies. While many recent high profile attempts at chatbots have failed, they are getting better and one day will be indispensable. Organizations need to do certain things to prepare for a future of bots and AI-driven processes. This session will outline what that looks like and how organizations can solve problems today while preparing themselves for a future where businesses will succeed or fail based on the power of their bots.
In the rapidly evolving world of ChatGPT and Large Language Models (LLMs), businesses are understandably apprehensive. Numerous potential hazards and hurdles exist such as:
Unrealistic expectations of LLMs as a magic solution to managing corporate content without requisite human involvement
Difficulty distinguishing between creative outputs and fabricated responses (hallucinations)
Decisions around training models: balancing usefulness with the threat of exposing trade secrets or other proprietary knowledge
Absence of clear audit trails and citation sources
The risk of generating responses misaligned with company policies or brand image
Potential financial burden of proprietary LLMs and related enterprise software platforms
In this webinar, we will examine a structured approach to harvest, utilize, and protect corporate knowledge resources. We will explore how both commercial and open-source large language models can be leveraged to deliver precise conversational responses without jeopardizing intellectual property.
Learn how your organization can effectively use LLM based applications for competitive advantage. Using a general LLM will provide efficiency, but through standardization. Differentiation using your corporate terminology and knowledge will allow for competitive advantage. You don’t have to deploy ChatGPT to benefit from these approaches. They will improve the information metabolism of the enterprise and pave the way for advanced AI applications.
A knowledge graph is a type of data representation that utilizes a network of interconnected nodes to represent real-world entities and the relationships between them. This makes it an ideal tool for data discovery, compliance, and governance tasks, as it allows users to easily navigate and understand complex data sets.
In this webinar, we will demystify knowledge graphs and explore their various applications in data discovery, compliance, and governance. We will begin by discussing the basics of knowledge graphs and how they differ from other data representation methods. Next, we will delve into specific use cases for knowledge graphs in data discovery, such as for exploring and understanding large and complex datasets or for identifying hidden patterns and relationships in data.
We will also discuss how knowledge graphs can be used in compliance and governance tasks, such as for tracking changes to data over time or for auditing data to ensure compliance with regulations. Throughout the webinar, we will provide practical examples and case studies to illustrate the benefits of using knowledge graphs in these contexts.
Finally, we will cover best practices for implementing and maintaining a knowledge graph, including tips for choosing the right technology and data sources, and strategies for ensuring the accuracy and reliability of the data within the graph.
Overall, this webinar will provide an executive level overview of knowledge graphs and their applications in data discovery, compliance, and governance, and will equip attendees with the tools and knowledge they need to successfully implement and utilize knowledge graphs in their own organizations.
*Thanks to ChatGPT for help writing this abstract.
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains how advanced concepts in information architecture, such as ontologies and knowledge engineering, are the basis for streamlined content workflows.
In this session Seth Earley, author of the AI Powered Enterprise, discusses how to harness the power of artificial intelligence to drive extraordinary competitive advantage.
Meaningful Metrics - Aligning Operational Metrics with Marketing & Customer E...Earley Information Science
Analytics and big data are the buzzwords de jour. But what is meaningful and how can success be measured in a tangible way? Marketing campaign dashboards, user behavior BI reports and on site clickstream data need to be correlated and interpreted in an actionable way, otherwise business owners will be quickly overwhelmed with data without deriving insights that can guide action.
This roundtable will revisit the topic of analytics and discuss practices for closing the data=>insight=>action loop.
Earley Executive Roundtable on Data Analytics - Session 1 - The Business Pote...Earley Information Science
Business Potential of Machine Learning and Cognitive Computing. Speakers include:
– Bruce Daley Principal Analyst, Tractica (@brucedaley)
– Olly Downs, Chief Scientist/CTO, Globys (@globysinc )
– Mitchell Shuster, Data Scientist, Knowledgent (@Knowledgent)
– Patrick Heffernan, Practice Manager, TBR (@TBR_PatrickH)
Governance is the glue that holds various content, knowledge and data management initiatives together. It is increasingly necessary as a component of customer experience and marketing automation and integration initiatives. The challenge is that governance is not an exciting topic and it is difficult to get participation and buy in at the correct levels of the organization. How do you retain interest in these kinds of necessary programs? The answer is to tie governance to measurement of program and project progress, success and operations. Once governance is aligned with objectives and clearly defined measurement, the organization will focus the correct level of attention and governance will be successful.
This webinar will cover the challenges associated with data governance and the business impact of poor data quality on digital marketing programs and knowledge management systems. Expert panel members will discuss real-world examples of data governance best practices, how to avoid the common pitfalls and how to put a framework for a successful metrics-driven governance process in place.
Earley Executive Roundtable for May 2016. Topic: Predictive Analytics, AI and the Promise of Personalization. Panelists are Seth Earley, EIS; Julie Penzott, Amplero; Adam Pease, Articulate Software. Host: Dino Eliopulos, EIS
Tagging isn’t new - it’s been around for a dog’s age in internet years. But in the past few years some fresh ideas and tools have reinvigorated the social tagging world. These new approaches include an attempt to improve findability through a bit of structure and control. While the idea of adding control to folksonomy seems like going against the whole selling point of social tagging (flexibility, openness), it is bringing the tagging to a new level, making it more viable for practical use in enterprises. This session will present hybrid approaches to formal taxonomies and social tagging. How can they be used in the corporate environment? What type of content is appropriate for social tagging? What kind of software is available for the enterprise? Learn how social tagging is not necessarily anathema to corporate taxonomy programs and how this hybrid approach can bring the best of both worlds: a fresh, up to date taxonomy with the structure needed to improve information findability.
Key Takeaways:
Folksonomy and taxonomy defined
Drawbacks of pure social tagging
Social tagging in the enterprise
Hybrid taxonomy & folksonomy approaches: Four models
Earley Executive Roundtable - Building a Digital Transformation Roadmap
Panelists:
Seth Earley, CEO, Earley Information Science (@sethearley)
Paul Wlodarczyk, VP, Client ServicesEarley Information Science (@twitcontentguy)
Information Driven Enterprise Architecture - Connected Brains 2018LoQutus
In this session we will show you how to deliver business
improvements with enterprise and information architecture. During this session you will get new insights to increase value to the organisation. We also explain our LEAF framework, an integrated EA framework leveraging information to build architectures more quickly.
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
Artificial intelligence (AI) is getting lots of attention but one key aspect is often overlooked, understated, or underestimated: the quality of “training” information and the structure of that information – the Information Architecture or “IA”. AI only works when it has the data it needs to spot trends, identify patterns and provide functionality – especially when it comes to chatbots and other so called “cognitive” technologies. While many recent high profile attempts at chatbots have failed, they are getting better and one day will be indispensable. Organizations need to do certain things to prepare for a future of bots and AI-driven processes. This session will outline what that looks like and how organizations can solve problems today while preparing themselves for a future where businesses will succeed or fail based on the power of their bots.
In the rapidly evolving world of ChatGPT and Large Language Models (LLMs), businesses are understandably apprehensive. Numerous potential hazards and hurdles exist such as:
Unrealistic expectations of LLMs as a magic solution to managing corporate content without requisite human involvement
Difficulty distinguishing between creative outputs and fabricated responses (hallucinations)
Decisions around training models: balancing usefulness with the threat of exposing trade secrets or other proprietary knowledge
Absence of clear audit trails and citation sources
The risk of generating responses misaligned with company policies or brand image
Potential financial burden of proprietary LLMs and related enterprise software platforms
In this webinar, we will examine a structured approach to harvest, utilize, and protect corporate knowledge resources. We will explore how both commercial and open-source large language models can be leveraged to deliver precise conversational responses without jeopardizing intellectual property.
Learn how your organization can effectively use LLM based applications for competitive advantage. Using a general LLM will provide efficiency, but through standardization. Differentiation using your corporate terminology and knowledge will allow for competitive advantage. You don’t have to deploy ChatGPT to benefit from these approaches. They will improve the information metabolism of the enterprise and pave the way for advanced AI applications.
A knowledge graph is a type of data representation that utilizes a network of interconnected nodes to represent real-world entities and the relationships between them. This makes it an ideal tool for data discovery, compliance, and governance tasks, as it allows users to easily navigate and understand complex data sets.
In this webinar, we will demystify knowledge graphs and explore their various applications in data discovery, compliance, and governance. We will begin by discussing the basics of knowledge graphs and how they differ from other data representation methods. Next, we will delve into specific use cases for knowledge graphs in data discovery, such as for exploring and understanding large and complex datasets or for identifying hidden patterns and relationships in data.
We will also discuss how knowledge graphs can be used in compliance and governance tasks, such as for tracking changes to data over time or for auditing data to ensure compliance with regulations. Throughout the webinar, we will provide practical examples and case studies to illustrate the benefits of using knowledge graphs in these contexts.
Finally, we will cover best practices for implementing and maintaining a knowledge graph, including tips for choosing the right technology and data sources, and strategies for ensuring the accuracy and reliability of the data within the graph.
Overall, this webinar will provide an executive level overview of knowledge graphs and their applications in data discovery, compliance, and governance, and will equip attendees with the tools and knowledge they need to successfully implement and utilize knowledge graphs in their own organizations.
*Thanks to ChatGPT for help writing this abstract.
In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today)
Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers.
Core Insights Include:
Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base.
Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes.
Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies.
Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively.
This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024.
Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights.
Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer.
Learn about:
- The essence and purpose of taxonomies and ontologies in information and knowledge management;
- Advantages of semantic layers leveraging organizational taxonomies; and
- Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies
Generative AI is getting all the attention, headlines, and industry hype. Organizations are looking at how it can be used to create better employee and customer experiences by unlocking the potential stored in the vast troves of unstructured data that house knowledge assets.
We will begin by providing an overview of the fundamental concepts and advances in generative AI, followed by an in-depth examination of the importance of knowledge management in developing, implementing, and improving these systems.
We’ll discuss knowledge management approaches for the organization and retrieval of information, how retrieval fits in with content generation, and the challenges and opportunities it presents for the enterprise.
In this session we will be discussing the challenges the organization faced in content usability, traceability, and findability, hindering their internal training workflows and access to critical knowledge assets.
We will also discuss what’s next on the content and information horizon, including the role of machine learning and why these approaches are needed for AI-Powered applications, including LLMs and ChatGPT types of information access.
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA.
In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability.
In this session, participants gained insights about the following:
Most common types of AI categories and use cases;
Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives;
Taxonomy and ontology design considerations and best practices;
Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and
Tools, roles, and skills to design and implement AI-powered search solutions.
Understand the key steps to set up your next data discovery initiative for success using the latest methodology and technologies with Earley Information Science. In this webinar we partner with Expert.AI, a recognized leader in document-oriented text analytics platforms to explain the technical and methodological advances that enable better data discovery.
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
With the explosive popularity of ChatGPT, organizations are throwing massive budgets and executive attention at the implementation of AI technologies. Making these solutions work for the enterprise can deliver competitive advantage and open up new solutions and business opportunities that were never before possible. However, without the right Information Architecture (IA) foundations, these projects are bound to fail. In this presentation, Marino and Galdamez provided practical, actionable steps around IA that organizations can take in preparation for future AI solutions.
In this session, attendees:
- Reviewed key elements of IA and discovered how their successful design and implementation can lay the foundations for AI;
- Learned basic terminology surrounding AI, as well as different techniques and applications of AI in enterprise environments;
- Gained a deeper understanding of the feedback loops between IA and AI and the corresponding implications on user experience; and
- Received practical advice on IA design to facilitate its implementation and the success of AI efforts.
Seth Earley, CEO & Founder of Earley Information Science and Peter Crocker, CEO & Co-founder of Oxford Semantic Technologies discuss powering personalized search with knowledge graphs to transform legacy faceted search into personalized product discovery.
The Increasing Criticality of MDM for Personalization for Customers and Employees
Master data management seems to be one of those perennial, evergreen programs that organizations continue to struggle with.
Every couple of years people say, “we're going to get a handle on our master data” and then spend hundreds of thousands to millions and tens of millions of dollars working toward a solution.
The challenge is that many of these solutions are not really getting to the root cause of the problem. They start with technology and begin by looking at specific data elements rather than looking at the business concepts that are important to the organization.
MDM programs are also difficult to anchor on a specific business value proposition such as improving the top line. Many initiatives are so deep in the weeds and so far upstream that executives lose interest and they lose faith in the business value that the project promises. Meanwhile frustrated data analysts, data architects and technology organizations feel cut off at the knees because they can't get the funding, support and attention that they need to be successful.
We've seen this time after time and until senior executives recognize the value and envision where the organization can go with control over its data across domains, this will continue to happen over and over again. Executives all nod their heads and say “Yes! Data is important, really important!” But when they see the price tag they say, “Whoa hold on there, it's not that important”.
Well, actually, it is that important.
We can't forget that under all of the systems, processes and shiny new technologies such as artificial intelligence and machine learning lies data. And that data is more important than the algorithm. If you have bad data your AI is not going to be able to fix it. Yes there are data remediation applications and there are mechanisms to harmonize or normalize certain data elements. But looking at this holistically requires human judgment: understanding business processes, understanding data flows, understanding dependencies and understanding of the entire customer experience ecosystem and the role of upstream tools, technologies and processes that enable that customer experience.
Until we take that holistic approach and connect it to business value these things are not going to get the time, attention and resources that they need.
Seth Earley, Founder & CEO, Earley Information Science
Dan O'Connor, Senior Product Manager at inriver
Semantic AI Making Great Data and Making Data GreatSmartlogic
As humans, understanding everyday language and the meanings of words is easy. Transferring these same capabilities to a machine – not simple. Technologies like natural language processing (NLP), AI, and machine learning (ML) combined with Semantic Web technologies derive context and meaning from information in a consistent and reusable way.
Jeremy Bentley, CEO and Founder, and Scott Henninger, Senior Solution Architect from Smartlogic to learn how organizations use Semantic AI platforms like Semaphore to augment existing systems to deliver perceptive Insight Engines, enriched process automation, agile predictive analytics, and exceptional knowledge management.
Square Pegs In Round Holes: Rethinking Data Availability in the Age of Automa...Denodo
Watch full webinar here: https://bit.ly/43qJKwn
Data-led transformations are becoming more prevalent in recent years, across numerous industries. More and more senior leaders are looking for data to drive their business decisions and impact their bottom line. One key challenge facing such businesses is the ability to pivot to new technologies while maintaining investments in legacy systems they have grown to rely on. In an age where automation, internet-scale search, and advanced analytics are driving many new advances, it is important to understand that this is not only a pivot in terms of technologies, it is a pivot in terms of how we think about and utilize data of different types. Traditional systems since the 1970’s have been built around database concepts where data is physically pipelined, mapped together, statically modeled, and locked away in vaults. The types of vaults have evolved over time from basic databases, to data warehouses, to data lakes, to lake houses, and so on.
The fundamental premise remains: data is placed into sealed containers, such that the critical approach is around storage, instead of being aimed at retrieval. Reversing this approach can, instead, lead to understanding data as transient, on-demand, and immediately available to end users within a certain context. This talk will discuss certain contemporary concepts that are expanding the notion of data storage devices and, instead, are moving to loosely connected data retrieval devices, or in some cases, data generation devices. We will examine this shift in approach and what it means for designing and deploying new types of technologies that can be more flexible and provide improved business value for clients in the fast-paced evolving world of Artificial Intelligence.
This is an article about Generative AI. It discusses what it is and the different techniques used to create it. It also goes into the potential uses of Generative AI. Some of the important points from this article are that Generative AI is still in its early stages but has already shown promising results. It is also important to note that Generative AI can be used to create fake data that is indistinguishable from real data.
https://www.ltimindtree.com/wp-content/uploads/2023/01/DeepPoV-Generative-AI.pdf
Modernizing your information architecture with aiModusOptimum
How an AI database can transform your organization with advanced workloads and intelligent data management
https://event.on24.com/wcc/r/2001350/88F16755FE0146440C7857390A93B309?partnerref=On-Demand
4 Critical Requirements for Building Truly Intelligent AI ModelsInnodata, Inc
Did you know 85% of AI projects will fail because of a lack of training data?
Before investing time and money in machine learning, discover 4 critical requirements every company needs to employ in order to build effective machine learning applications and bring intelligence to artificial intelligence.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Many Organizations are struggling with the best way to govern and manage the use of Generative AI in the enterprise. There are many dimensions to this challenge ranging from ethical issues, data architecture and quality, legal and copywrite, operational and more.
This is why a governance framework needs to be carefully designed and put into place so the business can make the most use of this truly revolutionary technology, reduce and mitigate risks, control costs, maintain a positive employee and customer experience and most importantly, find competitive advantage in the marketplace.
Improving product data quality will inevitably increase your sales. However, there are other benefits (beyond improved revenue) from investing in product data to sustain your margins while lowering costs.
One poorly understood benefit of having complete, accurate, consistent product data is the reduction in costs of product returns. Managing logistics and resources needed to process returns, as well as the reduction in margins based on the costs of re-packaging or disposing of returned products, are getting more attention and analysis than in previous years.
This is a B2C and a B2B issue, and keeping more of your already-sold product in your customer’s hands will lower costs and increase margins at a fraction of the cost of building new market share.
This webinar will discuss how EIS can assist in all aspects of product data including increasing revenue and reducing the costs of returns. We will discuss how to frame the data problems and solutions tied to product returns, and ways to implement scalable and durable changes to improve margins and increase revenue.
Some product information management (PIM) tools make it difficult to change core data models once they have been set up in the system. To avoid costly rework, you can utilize a “pre-PIM” design tool as a PIM accelerator. This class of software allows you to:
**Iterate on designs before committing to a PIM architecture
Improve data quality
**Collaborate on decision-making and audit trails
**Set up metrics around product data and attribute structure
**Correlate performance measures with metrics – product data and hierarchy improvements are correlated with user behaviors and outcomes
**Integrate governance content prior to PIM load
**Decrease reliance on spreadsheets
While some PIM tools include a subset of these functions, they are often lacking in flexibility, functionality, and integration capabilities, especially around product data model and hierarchy design changes.
In this webinar our PIM experts introduce a pre-PIM software solution that enables fluid design changes while ensuring data integrity, reducing risk, increasing stakeholder engagement, and showing clear ROI on investments in product data.
If you want to deliver a truly personalized product experience and strengthen customer loyalty, a Product Information Management System (PIM) is a must. PIM systems ensure clean, complete, and consistent data to enhance both the customer and employee experience. With intuitive management of complex product information, PIM unites internal teams with better visibility and reporting.
In this session our experts in enterprise information architecture and PIM technology explain ways you can:
--Streamline the complexity of supply chain information
--Publish consistent product information across all channels
--Adapt quickly to market changes and bring products to market faster
--Increase the total performance and profitability your Ecommerce business
Speakers:
Chantal Schweizer, Director of Solution Delivery at Earley Information Science
Jon C. Marsella, Founder, Chairman, and CEO at Jasper Commerce Inc.
How Large Enterprises are Saving Millions in Operational Costs and Improving the Employee Experience.
In this session, Earley Information Science, with partner PeopleReign, will show how these programs can rapidly produce measurable results in weeks rather than months and years. While large-scale knowledge problems cannot be solved overnight, by focusing on narrow AI with clearly defined processes and curated knowledge, organizations can see ROI in as little as 30 days.
In today's world everyone, including your B2B customers, expect personalized buying experiences. Unless you have the right information architecture in place to power your digital experience tools you will not be able to scale and retain trust with your customers.
In this webinar, B2B ecommerce experts Allison Brown with Earley Information Science and Jason Hein with Bloomreach walk through the reasons why you must invest in information architecture foundations in order to compete.
In this webinar Seth Earley establishes the formula for AI success, demystifies the topic for executives and provides actionable advice for data strategists.
Key Takeaways:
**AI-Powered solutions begin with a focus on business goals
**Successful AI requires a semantic data layer built on a solid enterprise information architecture.
**Instrumenting measuring ROI should be part of every AI program
Enterprises are increasingly recognizing the critical need for knowledge management (KM) to power cognitive AI. In fact, KM and AI are two sides of the same coin. Training a chatbot requires the same organized information that we use to train a human. When you engineer knowledge correctly, you serve the needs of people today and prepare for greater automation in the future. In fact, the long term success of the organization will depend on doing just that – especially when the competition builds high functionality bots that will produce lower costs and better customer service. Those without the capability will not be competitive.
In this panel discussion, our experts discuss examples and approaches that show how KM supports AI and how to ensure the success of your KM initiative.
Knowledge management and AI
People and cultural considerations
Business justification for long term investment
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/