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.
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.
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.
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
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
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.
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.
Explore 3 ways that AI can impact talent goals. Discover the full webinar series "Accelerating Talent Transformation with AI" on the Beamery resources section here: https://beamery.com/resources
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.
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.
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.
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
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
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.
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.
Explore 3 ways that AI can impact talent goals. Discover the full webinar series "Accelerating Talent Transformation with AI" on the Beamery resources section here: https://beamery.com/resources
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.
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.
Ai and Design: When, Why and How? - Morgenbooster1508 A/S
This year, A and I became the probably most used letters in the alphabet. Time to reflect upon the role we play as designers in an increasingly AI-driven landscape.
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
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
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.
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.
AI is an accelerator for Talent Transformation. It’s a layer of technology that means great talent teams can be even more powerful, but for the best results, the right foundations need to be in place.
In this first session of “Accelerating Talent Transformation with AI”, we’re cutting through the buzz and focusing on what will set talent leaders up for success when it comes to this new technology. What do today’s Talent leaders absolutely need to know about AI, and what questions should they ask when investigating new talent tech?
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.
A modern day data management platform driven by the evolved thought process and focus,
- From Data to Metadata engineering and Ontologies
- From Data Swamps to Data Products
- From Data for AI to AI for Data
- From Tech Debts to Data Monetization
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
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
Discussion 14
by
Santan Reddy Putchakayala
- Friday, 10 August 2018, 4:49 PM
In Today’s world most of the organizations are successful because of Information System. But using this information in a wrong way creates a lot of problem to organization and employees. Organization use internet and collect user data for their business. Now the main task for organizations is to secure the data. Now a days all organizations are facing issue with hackers attacks for confidential information. Information technology is facing lot of ethical issues and challenges like lack of security and privacy for confidential information. Hackers look for all possible loop holes to get the information. Cybercrime also growing so fast as information technology.
Ethics is social values and it assumes that society is working on trust factor. It is important that the technology users should make ethical decisions when anyone in the organization is engaged in unethical behavior, organization entire have to face the issue. Organizations face this issues either with internal or outside parties so companies have to ensure that their data is secure.
Most of the organizations which are in digital market are becoming victims for the cybercrime. Below are some of the ethical challenges faced by information technology.
Security: With growing technology tools like internet, it is easy for hackers to use IP address and track the user’s activity on internet. Cookies are used on browsers to collect the information. Companies use this cookies to decide what content has to show to the user. But for banking sector hackers will track the transaction and changes the transaction as they desired for their use.
Privacy Issue: Information technology is explored in such a way we can find information online. But this is also causing issues to privacy.
Prosperity: Ownership of information and ownership of channels through which the information is transmitted.
Accessibility: Amount of information accessible for organization employees. And the access they have to get information.
Respond to the above post with 150 words.
Be sure to support your work with specific citations using APA format
Read a selection of your colleagues' postings using one or more of the following ways:
• Share an insight from having read your colleagues' postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective using readings from the class materials or from your own research.
• Validate an idea with your own experience and additional research.
• Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues' postings by providing additional insights or contrasting perspectives based on readings and evidence
...
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what an ontology is and why it is important when building any AI powered application, such as a chatbot.
Artificial intelligence (AI) is getting lots of attention but one key aspect is often overlooked, understated, or underestimated: the training of the AI through the behind-the-scenes preparation of the data. Getting an AI application to actually do something useful does not happen by magic. Algorithms are in place today, but until the system is taught, it will not produce the results you are hoping for. The more research you do, the more you realize that AI only works when it has the data it needs to spot trends, identify patterns, and provide functionality. No data? No AI. And it can't use just any unstructured data. The data needs to be high-quality data. Yes, it can be messy, but it can’t be poor quality. And depending on the application, the data will require structure and curation. Attend this keynote presentation from Seth Earley, CEO of Earley Information Sciences, to learn how to train your AI, the kinds of data that are needed to make it work, and why intelligent content is vital for artificial intelligence. November 30, 2017
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
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.
More Related Content
Similar to EIS-Webinar- Generative-AI-KM-2023-04-19.pdf
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.
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.
Ai and Design: When, Why and How? - Morgenbooster1508 A/S
This year, A and I became the probably most used letters in the alphabet. Time to reflect upon the role we play as designers in an increasingly AI-driven landscape.
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
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
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.
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.
AI is an accelerator for Talent Transformation. It’s a layer of technology that means great talent teams can be even more powerful, but for the best results, the right foundations need to be in place.
In this first session of “Accelerating Talent Transformation with AI”, we’re cutting through the buzz and focusing on what will set talent leaders up for success when it comes to this new technology. What do today’s Talent leaders absolutely need to know about AI, and what questions should they ask when investigating new talent tech?
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.
A modern day data management platform driven by the evolved thought process and focus,
- From Data to Metadata engineering and Ontologies
- From Data Swamps to Data Products
- From Data for AI to AI for Data
- From Tech Debts to Data Monetization
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
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
Discussion 14
by
Santan Reddy Putchakayala
- Friday, 10 August 2018, 4:49 PM
In Today’s world most of the organizations are successful because of Information System. But using this information in a wrong way creates a lot of problem to organization and employees. Organization use internet and collect user data for their business. Now the main task for organizations is to secure the data. Now a days all organizations are facing issue with hackers attacks for confidential information. Information technology is facing lot of ethical issues and challenges like lack of security and privacy for confidential information. Hackers look for all possible loop holes to get the information. Cybercrime also growing so fast as information technology.
Ethics is social values and it assumes that society is working on trust factor. It is important that the technology users should make ethical decisions when anyone in the organization is engaged in unethical behavior, organization entire have to face the issue. Organizations face this issues either with internal or outside parties so companies have to ensure that their data is secure.
Most of the organizations which are in digital market are becoming victims for the cybercrime. Below are some of the ethical challenges faced by information technology.
Security: With growing technology tools like internet, it is easy for hackers to use IP address and track the user’s activity on internet. Cookies are used on browsers to collect the information. Companies use this cookies to decide what content has to show to the user. But for banking sector hackers will track the transaction and changes the transaction as they desired for their use.
Privacy Issue: Information technology is explored in such a way we can find information online. But this is also causing issues to privacy.
Prosperity: Ownership of information and ownership of channels through which the information is transmitted.
Accessibility: Amount of information accessible for organization employees. And the access they have to get information.
Respond to the above post with 150 words.
Be sure to support your work with specific citations using APA format
Read a selection of your colleagues' postings using one or more of the following ways:
• Share an insight from having read your colleagues' postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective using readings from the class materials or from your own research.
• Validate an idea with your own experience and additional research.
• Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues' postings by providing additional insights or contrasting perspectives based on readings and evidence
...
Seth Earley, Founder & CEO of Earley Information Science and author of the award winning book, "The AI Powered Enterprise" explains what an ontology is and why it is important when building any AI powered application, such as a chatbot.
Artificial intelligence (AI) is getting lots of attention but one key aspect is often overlooked, understated, or underestimated: the training of the AI through the behind-the-scenes preparation of the data. Getting an AI application to actually do something useful does not happen by magic. Algorithms are in place today, but until the system is taught, it will not produce the results you are hoping for. The more research you do, the more you realize that AI only works when it has the data it needs to spot trends, identify patterns, and provide functionality. No data? No AI. And it can't use just any unstructured data. The data needs to be high-quality data. Yes, it can be messy, but it can’t be poor quality. And depending on the application, the data will require structure and curation. Attend this keynote presentation from Seth Earley, CEO of Earley Information Sciences, to learn how to train your AI, the kinds of data that are needed to make it work, and why intelligent content is vital for artificial intelligence. November 30, 2017
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
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.
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.
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.
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
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
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.
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.
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.
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.
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 - Building a Digital Transformation Roadmap
Panelists:
Seth Earley, CEO, Earley Information Science (@sethearley)
Paul Wlodarczyk, VP, Client ServicesEarley Information Science (@twitcontentguy)
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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.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.