Forty five minute time slot dedicated to digging deeper into a specific topic
The first generation of IAs are still out there, many of us now in leadership roles, often on product teams, or coaching up-and-coming professionals. In this panel, three veterans will reflect on what happens once you've "made it" and whether it's possible to find purposeful satisfying stable work without an overhanging sense that just one more raise, promotion, or opportunity will finally get us "there."
Digital media, and software in particular, is a very verbose, very precise incantation of how we want an information system to behave. The highest level of detail, however, is also the most perishable, with material trading off precision for durability.
The Specificity Gradient is a framework for thinking about all of the precursor material: the research, prototypes, documents, that go into product development, as well as a general process for not only prioritizing work but organizing deliverables by level of detail, so the more durable parts can be reused. Expect as well a set of demonstrations, tools and infrastructure that implement the Gradient, as this is much more than a framework for organizing thought, but actual running systems.
Are Information Architects a thing of the past? Or the only folks out there who are going to make sense of this mess? And if not them, who is best positioned to shepherd the structure of our shared information spaces into a better state? How do we ‘future proof' our discipline?
In this debate, we'll pit industry experts against each other to determine answers to these questions and more, once and for all. Or, at least, unpack some of the situations and contexts that really require dedicated IA expertise… and what we might need to ‘share' with our colleagues for great impact in the future.
Join us for this spirited live debate, hosted by Optimal Workshop.
There is a growing interest in knowledge graphs to organize information and make it findable in organizations with large amounts of data and content. Unlike other data technologies, a knowledge graph has a structure that is typically based on a taxonomy and ontology, and thus should involve information architects. Knowledge graphs also have more benefits than information findability, including discovery, analysis, and recommendation. Knowledge graphs bring together content and data.
An enterprise knowledge graph involves a change in thinking about information and its access. Instead of designing information architecture in individual applications, an intranet, or website, a knowledge graph extracts data and links to content that exists in multiple different applications and repositories, linking them in a web or graph-like structure by means of customized, semantic relationships. This session explains what a knowledge graph is and how it is built, with a focus on ontologies. It also presents examples and standards.
Can a company that has ignored previous knowledge management systems and chooses to store key information on sticky notes, peoples' heads or on personal computers ever be motivated to change? Can people learn to trust digital content?
Our small team built a holistic and collaborative solution for knowledge management. We created a 10-step process to ensure that every pain point and possible stumbling block could be addressed. Here we share this process, which includes our information architecture, content and communication strategies.
While our journey has had challenges (like highly change-resistant departments), the overwhelming sentiment has been one of gratitude. One employee, who in a pre-survey said that she felt ‘lost in a jungle', declared that she had been ‘found after being lost in the jungle' in a post-solution survey. Our team have been buoyed by the meaning and purpose of doing this work. In this talk, I will take you through our process and lessons learned along the way.
