9 Key takeaways about search and content management from KMWorld Connect 2021
At KMWorld Connect 2021, KM industry leaders shared insights on how to get more from vast wealth of information that organizations are collecting and storing to help improve customer service, enhance employee efficiency, and drive better decision making. Access to session archives will be available on or about November 29, 2021 for registered attendees.
Here are some key insights about search and information findability from the 2021 KMWorld Connect presentations:
- The future belongs to those who can make the most effective use of the enormous amount of insight loaded content that exists in the business, the technology research publishing community, and on the web. The problem of content silos is where it all begins. Content is not used because it is fragmented between these silos. This creates a nightmare scenario from the users’ perspective. —David Seuss, CEO, Northern Light
- AI, machine learning, and knowledge graphs are changing how search is implemented and delivered. When an ontology’s entities and relationships are stored in a graph database and integrated with a search engine, it enables an organization to search and facet based on the relationships of those entities. When querying for content, facets dynamically populate based not only on the tags but also the defined and inferred relationships of entities. This is the foundation of natural language searching. —Joseph Hilger, COO, and Neil Quinn, senior consultant, technology solutions, software engineer II, Enterprise Knowledge LLC
- Whether for prospective customers or employee and partner enablement, poor information findability results in inefficiencies and lost opportunities. Intelligent content is structured and has metadata with a framework of taxonomy in place and can be transformational to user experience. Taxonomy features in content management have long been siloed and limited in scope. —Chip Gettinger, VP global solutions consulting, RWS
- According to a survey fielded by APQC in partnership with Sinequa, 50% of respondents have confusion over where information is stored, and 45% say there are too many disconnected systems. When workplaces don’t work, it’s harder to find people and know-how, and a lack of knowledge directly impacts productivity. —Scott Parker, director, product marketing, Sinequa
- AI and machine learning are particularly useful in three areas. One bucket is the ability to classify knowledge assets at a speed and volume that mostly exceeds anything humanly possible, another is continuous learning and improvement, and the third is insight delivery, which is evolving from pulling information when users search for it to pushing it automatically to them in the context of their work activities. —Alan Pelz-Sharpe, principal industry analyst, Deep Analysis
- People don’t want to just find a document; it’s about getting factual contextual knowledge. Distributed workforces need to be able to access centralized knowledge to do their jobs. —Doron Gower, CSO, KMS Lighthouse
- Users of enterprise search applications want one simple thing: “Just make it work like Google,” and, as Google search has gotten better and smarter, there is now a requirement for companies to master AI technologies such as machine learning, natural language processing, and knowledge graphs to deliver a similar experience. However, companies may struggle to keep up due to the lack of resources or the pace of change in technology. —Kamran Khan, president & CEO, Pureinsights
- Organizations are coping with an information waterfall. According to a 2020 survey, the majority of organizations (30%) are dissatisfied with their internal search functionality, he referenced. Many organizations say that search is not working and that making content more findable is a big challenge. To avoid a big “black box” of technology behind search, companies need to build it from the ground up, starting with defining the business strategy, then making decisions on risks. The search strategy has to meet the information management strategy. —Martin White, managing director, Intranet Focus Ltd.
- Graph neural networks (GNNs) have emerged as a mature AI approach used by companies for knowledge graph enrichment via text processing for news classification, question and answer, search result organization, and much more. A graph can represent many things—social media networks, patient data, contracts, drug molecules, etc. GNNs enhance neural network methods by processing the graph data through rounds of message passing; as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. —Jans Aasman, CEO, Franz
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