At KMWorld Connect 2021, KM industry leaders shared ideas on how to make the most of the vast wealth of information organizations collect and store to help improve customer service, improve efficiency. of employees and promote better decision-making. Access to the session archives will be available on or around November 29, 2021 for registered participants.
Here is some key information on finding and finding information from the 2021 KMWorld Connect presentations:
- The future belongs to those who can make the most of the enormous amount of information-laden content that exists in the enterprise, the tech research publisher community, and on the web. The problem of content silos is where it all begins. The content is not used because it is fragmented between these silos. This creates a nightmare scenario from the users’ point of view. —David Seuss, CEO, Northern Light
- Artificial intelligence, machine learning, and knowledge graphs are changing the way research is implemented and delivered. When the entities and relationships of an ontology are stored in a graph database and integrated with a search engine, it allows an organization to perform searches and facets based on the relationships of those entities. When searching for content, facets dynamically fill in based not only on tags, but also on defined and inferred relationships of entities. It is the foundation of natural language research. –Joseph Hilger, COO, and Neil Quinn, Senior Consultant, Technology Solutions, Software Engineer II, Enterprise Knowledge LLC
- From potential customers to empowering employees and partners, poor information retrieval capacity leads to inefficiencies and lost opportunities. Smart content is structured and has metadata with a taxonomy framework in place and can transform the user experience. Taxonomy features in content management have long been siled and limited in scope. —Chip Gettinger, Vice President of Global Solutions Consulting, RWS
- According to a survey conducted by APQC in partnership with Sinequa, 50% of those surveyed do not know where the information is stored and 45% say that there are too many disconnected systems. When workplaces are down, it is more difficult to find people and know-how, and the lack of knowledge has a direct impact on productivity. —Scott Parker, Director, Product Marketing, Sinequa
- AI and machine learning are particularly useful in three areas. One bucket is the ability to categorize knowledge assets at a speed and volume that most of the time exceeds anything humanly possible, another is continuous learning and improvement, and the third is the dissemination of information, which ranges from extracting information when users seek it to automatic delivery. to them as part of their professional activities. —Alan Pelz-Sharpe, Senior Industry Analyst, Deep Analysis
- People don’t just want to find a document; it is about acquiring factual contextual knowledge. Distributed workforces need access to centralized knowledge to do their jobs. –Doron Gower, CSO, Lighthouse KMS
- Users of enterprise search applications want one simple thing: “Make it work like Google” and, as Google search has gotten better and smarter, companies now need to master AI technologies such as machine learning, natural language processing, and knowledge graphics to deliver a similar experience. However, companies can struggle to keep up, due to lack of resources or the pace of technological change. —Kamran Khan, President and CEO, Pureinsights
- Organizations face a cascade of information. According to a 2020 survey, the majority of organizations (30%) are dissatisfied with their internal search functionality, he referenced. Many organizations say search doesn’t work and making content more accessible is a big challenge. To avoid a large tech “black box” behind research, companies must build it from the ground up, first defining business strategy and then making decisions about risk. The search strategy must respond to the information management strategy. —Martin White, Managing Director, Intranet Focus Ltd.
- Graphic Neural Networks (GNN) emerged as a mature AI approach used by companies for enriching knowledge graphics through word processing for news classification, questions and answers, organization of results research, and much more. A graph can represent a lot of things: social media networks, patient data, contracts, drug molecules, etc. GNNs improve neural network methods by processing graph data through cycles of messaging; as such, nodes know more about their own characteristics as well as neighboring nodes. This creates an even more accurate representation of the entire graphic network. —Jans Aasman, CEO, Franz
Companies and suppliers mentioned