Practical Asset Reuse - Preview for DAM Jumpstart Session 3

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Preview for the third session of the Digital Asset Management webinar series, presented by Earley & Associates. Free registration: http://www.earley.com/webinars/jumpstarts/digital-asset-management

Tecnologia

Practical Asset Reuse Borrowing best practices from the savviest online media companies Diane Burley Industry SpecialistNstein Technologies

The Information Economy 24-hour information cycle Geographic trading borders blur/disappear 24-hour content on-demand Multiple channels Information Demand Information Output Intranets Social Media Mobile Email Presentations Web Colleagues Customers Partners Media Analysts Investors Round-the-Clock Knowledge Management

How Publishers Do It … A centralized system: The Hub Automatically ingests, converts to XML & enriches w/metadata Creates thumbnails. Content sent to multi channels Emails Web Blogs FTP K4 News-letters XML UGC SocialMedia HTML PDF Semantic Enrichment PPTs Enables repurposing Easily 3

Digital Asset Management + Semantic Analysis

Very Large Publisher Needed to find “blurbs” of 100 words or less from 80 year collection of information distributed over hundreds of repositories Prior to automation: 8 people/2 weeks/28 assets located After automation …. Tune in on January 28, 2 pm ET to find out! http://www.earley.com/webinars/jumpstarts/digital-asset-management

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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.

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx

Earley Information Science

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.

EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf

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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.

Reducing Returns to Increase Margin Through Better Product Data

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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.

EIS-Webinar-Most-From-LLMs-2023-08-23.pptx

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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.

EIS-Webinar-Silabs-KM-Content-Program-2023-06-07.pdf

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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

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Practical Asset Reuse - Preview for DAM Jumpstart Session 3

1. Practical Asset Reuse Borrowing best practices from the savviest online media companies Diane Burley Industry SpecialistNstein Technologies

2. The Information Economy 24-hour information cycle Geographic trading borders blur/disappear 24-hour content on-demand Multiple channels Information Demand Information Output Intranets Social Media Mobile Email Presentations Web Colleagues Customers Partners Media Analysts Investors Round-the-Clock Knowledge Management

3. How Publishers Do It … A centralized system: The Hub Automatically ingests, converts to XML & enriches w/metadata Creates thumbnails. Content sent to multi channels Emails Web Blogs FTP K4 News-letters XML UGC SocialMedia HTML PDF Semantic Enrichment PPTs Enables repurposing Easily 3

4. Digital Asset Management + Semantic Analysis

5. Semantic Enrichment

6. Very Large Publisher Needed to find “blurbs” of 100 words or less from 80 year collection of information distributed over hundreds of repositories Prior to automation: 8 people/2 weeks/28 assets located After automation …. Tune in on January 28, 2 pm ET to find out! http://www.earley.com/webinars/jumpstarts/digital-asset-management

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Practical Asset Reuse - Preview for DAM Jumpstart Session 3

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