Taxonomy for Personalization: January 6 Taxonomy CoP
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Preview for January 6 Taxonomy Community of Practice call, presented by Earley & Associates. Register at http://www.earley.com/webinars/enterprise-search/taxonomy-personalization
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Unrealistic expectations of LLMs as a magic solution to managing corporate content without requisite human involvement
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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
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Potential financial burden of proprietary LLMs and related enterprise software platforms
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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.
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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:
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Improve data quality
**Collaborate on decision-making and audit trails
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**Decrease reliance on spreadsheets
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Jon C. Marsella, Founder, Chairman, and CEO at Jasper Commerce Inc.
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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.
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Seth Earley, Founder & CEO of Earley Information Science, makes the case that information architecture is fundamental to a successful AI program.
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