MCN 2024

Since 2020, MoMA no longer offers print materials such as maps and brochures to onsite visitors. In their place, the Digital Product team at MoMA developed a Digital Visitor Guide to provide these and other onsite resources, such as audio, to visitors. The first iteration of the guide was put together quickly in the early days of the pandemic, and was a simple collection of links to existing online content and digitized versions of print materials.

Since then, user research and visitor feedback has highlighted some ongoing challenges that visitors face during their onsite experience and the ways MoMA’s digital materials fell short of alleviating them. PDF resources are difficult to use on a mobile device, and tedious to update internally. Exhibition listings are insufficient for visitors who are either not sure where to start, or looking for specific artists or works on view. The first version of the guide was also not built with accessibility standards in mind.

With these learnings, the Digital Produce team embarked on a process of designing, testing, and iterating on a number of new features and enhancements, such as an interactive map, floor-by-floor previews of exhibitions, galleries, artists and amenities, a streamlined audio experience, self-guided tours that cater to different interests, and an improved search experience for various types of onsite content.

This talk will cover the initial research that exposed these challenges, walk through the product design process of developing these new features, discuss insights and adjustments arising from rapid onsite testing that followed each iteration, and share quantitative metrics that outline the impact of all these changes. We’ll also discuss ideas for future features and improvements for the guide, as well as thornier challenges that we’re still exploring solutions for.

Though our discussion will not focus heavily on the technical aspects of our solution, we will briefly touch upon some important points such as geolocation, building a web-based onsite map without relying on an app or third-party software, and connecting the guide to our content management system to ensure exhibition, location, and artwork data is automatically kept up-to-date.

Artificial Intelligence has rapidly emerged as a transformative force in the museum community. As museum professionals, understanding the basics of AI and its implications to our industry is crucial. This presentation provides an overview of AI and delves into the various opportunities, challenges, and serious concerns that we must face together.

AI can offer the museum community tools from enhancing the visitor experience to improving how we do our business. AI-driven efforts can revolutionize how we do our work and foster innovation. However, AI in museums raises ethical concerns, especially regarding visitor data privacy and information biases. Equally important, over-reliance on AI might diminish the human touch, risking the loss of authentic, trusted connections museums aim to foster. There are real concerns about AI as it can distort, misrepresent, or oversimplify complex historical and cultural narratives. If not properly trained or contextualized, AI will perpetuate biases or misunderstandings. As museum professionals, it's our responsibility to approach AI with a balanced perspective, harnessing its potential while being mindful of its implications. Collaboration, continuous learning, and open dialogue will be key as we navigate this intersection of technology, innovation, and culture.

What is Digital Content? How do we create policy and strategy for digital without common definitions and a shared framework for managing digital content? How do we prioritize and resource digital content to support our institution’s mission and strategic goals?

To help answer those questions, cross-departmental teams at the Smithsonian set out to research and inventory digital content types, connected systems, and workflows. The project differentiates between digital content and digital assets, emphasizing that not all digital content holds long-term institutional value. Digital assets are specifically identified for their ongoing usefulness and significance to the SI. The project involved extensive stakeholder engagement, including interviews and surveys with 102 staff members across 32 units, covering diverse roles such as collections, conservation, research, content production, and data analysis.

The gap analysis section addresses the challenges in harmonizing collections data, managing born-digital collections, and ensuring effective digital stewardship. Recommendations include developing systems for aggregate collections data, providing sustained funding for essential digital systems, and creating tools and training for content transfer and management.

This session will highlight how we went about researching and communicating findings, as well as prompt attendees to consider the types of content they engage with in their day-to-day work, ensuring that valuable digital assets are effectively preserved, accessed, and utilized across one’s organization.

30min: presentation
Background on the project, process, and findings.
Links of resources, final report, and supplemental materials will be made available to attendees.
15min: group exercise and Q+A
We will invite users to contribute questions and add their own digital content types via QR code form to display real-time submissions.
We may also invite guests into our Systems Diagram Miro board.

Every museum has a digital strategy, whether it is formalized or ad-hoc. The challenge is to make that strategy useful and meaningful. This session presents three case studies that showcase approaches to making digital strategy operational and useful as museums look for new digital initiatives and innovation. The Spencer Museum of Art focused on the creation of a digital plan that operationalizes well thought-out institutional goals, and incorporated museum- and campus-wide input, and sought guidance from digital leaders across the museum sector. The Arizona State University Art Museum started from a broad assessment of where they were, and what peer institutions had done, in order to plan how to proceed. The Yale University Art Gallery approached planning after completing a major digital project, reflecting on what the project’s twists and turns said about the organization as a whole, and exploring how they might better plan for digital initiatives in the future. At Yale, digital strategy is an ongoing activity, managed by a cross-departmental team. In each case, digital planning is operational. It is focused on doing: where to start, what to do next, and how to measure and learn from doing. As such, digital planning bridges the visionary strategy with the nuts-and-bolts and day-to-day.

One of the challenges that digital technologies have made manifest in cultural heritage organizations is that there is more stuff than most staff have the time, attention or ability to devote to. In the past, before networked computers and compendiums of information like Wikipedia (or Amazon) changed people's expectations of what can and should be taken for granted, the cultural heritage sector dealt with this problem by hiding it in a flurry of words and never letting anyone see all the things stacked on shelves in the backroom. But people's expectations have changed and coupled with the calls for open access around our collections suddenly all that stuff, much of it uncataloged and unresearched, is seeing the light of day without much context. This session will discuss the organization and technical challenges that these new realities present emphasizing the questions surrounding the use of generative and artificial intelligence systems to address the cultural heritage sector's perennial "content deficit" and what many of the concerns about their use say about the sector itself.

With the approaching end-of-life of Drupal 7, our ongoing challenges of managing separate websites for museum visitors and online collections, the construction of a new physical museum, and an impending rebranding, it was clear that we needed to reevaluate the overall architecture of Gilcrease's online ecosystem. In this session, we will present a case study and share the lessons we've learned from the decoupled migration of the Gilcrease Museums Online Collections.

You might be wondering, what exactly is a decoupled website? What benefits, drawbacks, and compromises did we encounter during the transition to a new website architecture? What are our plans post-launch? We will address these questions and many more.

We'll share relatable experiences and obstacles that small to medium-sized museums often face, demonstrating the strategies we used to “lift and shift” the website migration while balancing innovation with budget constraints and timelines. Throughout this process, we aimed to minimize technical debt and adhere to a reasonable timeline, despite the temptation to add new features. Sometimes, we resisted; other times, we had to compromise.

We will also share key technical highlights such as:
- A brief high-level overview of the Gilcrease collections systems architecture including TMS, Library, and ArchivesSpace custom integration with DAMS and collections website.
- Overview of the Piction to Drupal integration and delta sync.
- Building, serving, and presenting IIIF manifests with Drupal and Clover IIIF.
- Enhancing performance and SEO with decoupled architecture.

Exploring AI and machine learning in art museums often feels like an exercise in separating hype, achievable near-term value, and potential long-term game-changers. In this presentation, the National Gallery of Art will share how that pursuit is playing out after 18 months of pilots with cross-functional teams in two priority use cases, with lessons learned to date and plans for the way ahead. First: after initial work to scan, run optical character recognition (OCR), and analyze exhibition response wall cards and visitor comments, the team found that an AI-powered chatbot built within the network helped quickly find insights among thousands of comments, unlocking new value from qualitative data. Second: as a part of ongoing transformation in exhibition planning and operations, machine learning helped mine a decade of data to predict attendance curves and gauge what drives audience engagement. The data science team will present data visualizations, predictive modeling techniques, and methods for natural language processing and chatbot development, while members of visitor experience and evaluation will share findings, time savings, and future plans from these two initiatives. Recognizing that the value of analytics projects is measured by the decisions and outcomes they inform, the session will address how the results are used and future plans for plugging into business processes, with relevance to any museum and an invitation to participate in ongoing analysis, benchmarking, and collaborative data culture across museums.