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Navigating the modern world of content management

Regardless of the industry, content management is likely at the heart of any enterprise. The handling of endless amounts of text documents, spreadsheets, emails, PDFs, and the like is a crucial component of knowledge management. Yet, as traditional methods and modern technologies clash, how can organizations successfully overcome content management complexities?

Content experts joined KMWorld’s webinar, Intelligent Content Management: Game-Changing Technologies and Strategies, to examine new solutions and strategies—often revolving around automation, integration, and AI—that catapult content management into the digital age.

Jamie Greenstein, senior marketing manager at Access, argued that integrated information management is a 2024 essential. Ideally, an integrated information management system consists of the following components:

  • Streamlined and centralized data access
  • Seamless integration of compliance requirements into information management processes
  • A unified, cohesive governance framework across the organization
  • Automated classification with AI-driven tools
  • Retention automation to ensure regulatory compliance and resource optimization
  • Enhanced information security to safeguard sensitive data and maintain data integrity

This is, however, the ideal scenario; though fruitful, digitizing information is fraught with challenges, noted Greenstein. From complex costs to delayed timelines, labor availability, user acceptance, change management, and budget alignment, modernizing an enterprise’s wealth of information is no simple task.

Greenstein suggested that AI-powered indexing is the key to successful digitization, defined by efficient and quick access, accurate classification, mitigated risk, and cost-effective solutions. With AI, organizations can optimize file-level indexing—ultimately increasing productivity through content democratization—while improving decision making, compliance, and reducing costs through greater efficiency.

Teodora Petkova, semantic content and metadata expert at Ontotext, stated that taming content complexity is possible through the utilization of knowledge graphs. Introducing Ontotext—the company working at the intersection of data and knowledge management to maximize the value of data with AI, knowledge graph, and semantic technologies—and its core product—Ontotext GraphDB—Petkova explained that crafting and evolving knowledge graphs by interlinking open and proprietary data while applying text analysis is at the center of Ontotext’s focus.

Though, like many enterprises, Ontotext faced the all-too-common challenge of content management complexity. Their solution was to put their technology and business strategy to the test, building a knowledge graph for their marketing content.

Echoing the previous speakers, James Morris, principal sales engineer at Semaphore/Progress, agreed that content alone isn’t enough to create a successful business. Citing a 2023 statistic from Gartner, 80% of data will be unstructured by 2025, but more than 90% of decisions are based on structured data. Furthermore, knowledge workers spend about 2.5 hours per day, or roughly 30% of a workday, searching for information, yet 50% of information retrieved is not relevant, according to 2023 findings from IDC and Accenture.

Morris noted that through the power of context and metadata, enterprises can overcome the complexity of today’s data ecosystem by feeding the right content to the right people at the right time.

This system requires knowledge modeling, a data governance framework that acts as a source of truth for organizational domains. Importantly, these models should be adaptable to change as organizations, content, and users change. To manage that transformation over time, Morris argued that you must collaboratively leverage the joint intelligence of your organization.

Companies don’t need another platform, they need understanding, according to Morris.

Juanita Olguin, senior director of product marketing at Coveo, focused their presentation on how content search and knowledge discovery can be augmented by AI and generative AI (GenAI).

They prefaced their discussion by citing a statement from Gartner, explaining that “generative AI, in isolation, is neither an alternative to, nor replacement for, current search technologies.” What must support GenAI-based content management, Olguin offered, is data, knowledge, search, and automation.

Knowledge management is critical, yet cannot be done effectively, at scale, without search and automation, according to Olguin. Humans are limited in their cognitive loads, ultimately faced with thousands of systems, in diverse formats, that fail to adhere to traditional methods of data centralization and curating.

Olguin noted that, while popular, generative answering is only one of several search techniques. However, its implementation can be widespread, applicable to websites, service portals, service management or CRM apps, chatbots, etc.

Ultimately, search and AI can be leveraged to amplify KM initiatives in a multitude of ways, ranging from advanced access security to automated content and data source refresh, behavioral AI that learns from user search activity, personalization through real-time and historical behavioral data, enhanced business controls, and more.

For the full discussion of content management strategies and solutions, you can view an archived version of the webinar here.

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