Our popular writers, speakers, and authors of Wow, Woo, Win: Service Design, Strategy & the Art of Customer Delight look at how customer experience and service design can enhance knowledge sharing and success in organizations. They discuss the importance of designing your organization around service and offer clear, practical strategies based on the idea that the design of services is markedly different than manufacturing. When customers have more choices than ever before, study after study reveals that it’s the experience that makes the difference. To provide great experiences that keep customers coming back, organizations or KM programs must design services with as much care as design products. Service design is proactive—it is about delivering on your promise to customers in accordance with your strategy. Our speakers share with you how to create “Aha” moments when the customer makes a positive judgment, and to avoid “Ow” moments. They provide tips on how you and customers create a bank of trust, fueled by knowledge of each other’s skills and preferences.

Everyone who engages with your organization is in search of something, whether it’s products, services, people, or support. Too much of their time is spent sifting through useless information. New advances in machine learning and AI technology, combined with contextual search, are finally bringing relevance to every interaction and are making knowledge management a key driver of real business results. See real-world examples of the impact that increased maturity has made on innovative companies. Learn actionable steps to increase the relevance of your organization and start positively impacting your bottom line.

Communities of practice are a great way to develop expertise and innovation around specific interests. By infusing intelligence into many experiences and demonstrating some recent advances in Office 365 you’ll see how to leverage tacit and explicit knowledge in different ways as well as reuse and build upon the work of others. Our speaker has extensive experience in enterprise collaboration systems and currently leads intelligent search and discovery for Microsoft 365.  Expect lots of tips & examples for improving your KM initiatives.

With the recently published book Deep Text: Using Text Analytics to Overcome Information Overload, Get Real Value From Social Media, and Add Big(ger) Text to Big Data as a guide, author Tom Reamy provides an extensive overview of the whole field of text analytics: What is text analytics, how to get started, development best practices, latest applications, and building an enterprise text analytics platform. The talk ends with a look at current and future trends that promise to dramatically enhance our ability to utilize text with new techniques and applications.

Text analytics emerged in the mid-2000s, a collection of technologies, solutions, and practices aimed at meeting a diversity of business challenges. A decade in, what’s new and promising, what’s tried-and-true, and what’s on the horizon? Sentiment, identity, personality, and intent, extracted from text: All are now part of the data science mix. How has the market evolved—both demand and supply—and how should practitioners, solution providers, business analysts, and investors stay on top of developments? Text Analytics Market Insights helps all interested parties understand what’s working and what’s next, to enable them to extract the greatest value from text analytics.

Artificial intelligence is transforming text analytics. However, most AI algorithms still lack the ability to understand text data that is different from what is encountered during training. This becomes a critical issue when algorithms encounter unfamiliar words, misspellings, acronyms, or words in a different language. The solution? Transfer learning -- the ability for an AI system to take what it has learned in one situation and apply it to new and different situations. But is transfer learning ready for business deployment, or is it still an emerging technology? How can it be used in text analytics today? Havasi will discuss how transfer learning can be applied to text analytics across multiple industries.

Some technologies such as IBM Watson are being touted as AI. In response, there are new AI offerings from the large enterprise software companies as well as many startup companies. But is this AI or automation? This talk discusses the difference and argues that these offerings use entity extraction and business rules rather than AI. However, there are real opportunities to use this new technology to automate content tagging.

Text analytics can discover and add underlying structure to content, providing some remarkable new capabilities for the enterprise. This session focuses on the discovery of relationships between data and the population of graph databases and graph search. There are now more than 30 graph databases on the market, ranging from neo4j to SQL Server 2017, and graph search has become mainstream (including Lucene 6, the Microsoft Graph and many more). Text analytics is important for these to provide value to organizations.

NLP and entity extractors make up an important part of our use cases in cognitive computing. We discuss how terminology systems and knowledge bases are used in combination with NLP and entity extractors to greatly enrich the contents of our data infrastructures.

A panel of four text analytics experts answer questions that have been gathered before the conference, during the conference, and some additional questions from the program chair and sponsors.

Using case studies of real-world client projects, Smartlogic’s CEO presents, discusses, and demonstrates how post-relational databases, text analytics, AI, semantics, and linked data are delivering rapid returns on investment in data intensive industries. Cases range from predictive analytics and financial risk assessment to compliance, superior superior customer service, and unified enterprise intelligence within industries including banking, life sciences, media, and healthcare. The talk looks at the technology, the opportunity, lessons learned, and the keys to project success.

To improve the effectiveness of information findability and usability, we are developing a new mechanism to understand users’ interests and predict the information that will be most relevant to their needs. We analyze the technical documents published by members of the workforce and build models that can be used to match user’s requests with the best available content. We utilize an existing hierarchical taxonomy as part of the clustering effort in order to provide preliminary labels for the clusters. The information retrieval environment we are building will not only support retrieval of relevant corporate information upon request, it is designed to proactively notify targeted members of the workforce when relevant information becomes available.

In this talk, Patrick Lambe takes an unconventional look at how text analytics, taxonomies and search can be used in concert to probe areas of ignorance, not just uncover and organize what is already known, via three problem cases from the areas of public health and public transport. We demonstrate how elements of the search and discovery technology stack can be used to detect patterns in the environment to address or mitigate these types of problems.

A panel of four text analytics experts answer questions that have been gathered before the conference, during the conference, and some additional questions from the program chair and sponsors.