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Radical Collaboration in a Linked Digital World

In an era of algorithms and highly personalized recommendations, anything that is unavailable as data is very unlikely to be found or recommended. Whereas other industries have long made their contents readily discoverable by machines, the live performance sector lags behind. Linked open data could enable performing arts organizations to catch up. Together.

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Radical Collaboration in a Linked Digital World

  1. 1. Frédéric Julien Canadian Arts Presenting Association (CAPACOA) Akoulina Connell Corporate Agility Consultant Radical Collaboration in a Linked Digital World Photo: J’aime Hydro by Christine Beaulieu. Co-produced by Porte Parole and Champ gauche. Photo credit: Pierre Antoine Lafon Simard. Unless otherwise noted, the content of these slides is provided under the CC BY 4.0 license. Ottawa November 14, 2019
  2. 2. How has the digital revolution transformed the world performing arts organizations operate in? How should performing arts organizations adapt to this shift? How has the digital revolution transformed the world performing arts organizations operate in? How should performing arts organizations adapt to this shift?
  3. 3. Business models in the digital economy A few strategic observations
  4. 4. Lessons from the digital economy Successful business models in the digital world: • Tied to distribution • Rely on scale • Create value with users’ data • Highly personalized, customer-focused recommendations The performing arts sector: • Is focused on creation/production • Does not have a scalable product • Does not have much of a data culture • Recommendations focused on the arts organization
  5. 5. Performing arts in the digital economy The performing arts sector: • Must remain focused on its core business: creation/production • Can achieve scale through digital collaboration • Needs to develop a brand new data culture • Must adopt a co-opetition mindset to recommendation
  6. 6. The Web has been changing Initially driven by a collaborative vision Now driven mainly by commercial interests
  7. 7. The Web of documents A “vague but exciting” idea… Documents coded with HyperText Markup Language (HTML) + Uniform Resource Locator (URL) + HyperText Transfer Protocole (HTTP) = Photo: The computer that Tim Berners-Lee used to invent the World Wide Web, in 1989. By Robert Scoble from Half Moon Bay, USA, CC BY 2.0.
  8. 8. The Web of data • Tim Berners-Lee also envisioned that the Web of documents would evolve into a Web of data: • Same HTTP protocol • Uniform Resource Identifier (URI) assigned to: • things/objects • and their relations Photo: Tim Berners-Lee in 2009 By Levi Clarke - Own work, CC BY-SA 4.0
  9. 9. The Web of data: from vision to reality 1994 URI working group 2001 Berners- Lee envisions “data Web” 1995 2000 2005 2010 2004 Resource Description Framework (RDF) 2006 Five-star linked open data 2007 Freebase DBpedia 2008 SPARQL query language 2010 JSON- LD encoding format Early R&D Deployment Maturity
  10. 10. The Web of linked open data The Web of data / linked open data • provides a common framework • that allows data to be shared and reused • across application, enterprise, and community boundaries. Source: W3C, Semantic Web Activity, 2001.
  11. 11. Who has data to expose as linked open data? • Who in the room publishes information about live performances on a website? • How do you do it? • Let me guess: someone copies and pastes information from some text document into a web page. • What if this data only needed to populated once? And could be reused in several listings?
  12. 12. Cross-domain • Freebase • DBpedia Geography • Names of places Life Sciences • Diseases, drugs, genes Music • Musicbrainz 95 datasets The Linked Open Data Cloud in 2009
  13. 13. The Linked Open Data Cloud in 2014 570 datasets
  14. 14. Linked open data in 2019 1240 datasets • Twice as much as in 2014! The performing arts aren’t there yet.
  15. 15. And then… Transnational tech giants also saw the potential of linked open data. • structured data vocabulary created in 2011 by Bing, Google, Yahoo!, and Yandex • Google… • Acquired Freebase • Integrated Freebase in the Google proprietary knowledge graph; • Shut down Freebase 2014 and moved the data into Wikidata.
  16. 16. From search engines to recommendation systems
  17. 17. Welcome to the recommendation era • Today, the majority of search queries are made on a small screen (or without any screen). • Search engines have therefore gradually shifted from delivering lists of search results to delivering recommendations.
  18. 18. Welcome to the recommendation era • In order to make recommendations, search/recommendation technologies need: Data Data on the offer User data Re- commend- ation
  19. 19. Recommendation = matching offers with behaviours and context Recommendation services take into account: • Your online behaviour history; • The online behaviour of other consumers; • Similarities between you and other consumers (“people who liked this also liked this”); • Context (time and location).OFFER
  20. 20. These aren’t challenges you can tackle on your own
  21. 21. Your real competition comes from outside of the performing arts • A performing arts venue may present up to 8 performances of the same show per week • A movie theatre screens 50+ films in various genres per week • Netflix allows you to watch any film you want, whenever you want, and on whatever device you want
  22. 22. We’re no match. And we’re behind. Movie industry • Commercial movies have a unique persistent identifier in one of several open-data knowledge bases: • International Standard Audiovisual Number (ISAN) • Entertainment Identifier Registry (EIDR) • Internet Movie Database (IMDb) Performing arts • There are no unique identifiers for performing arts productions. • There is no open knowledge base for the performing arts. • There is no standardized data model to describe the performing arts
  23. 23. Try for yourself Search: “Movies near me” Search: “Shows near me”
  24. 24. To stand a chance, we must stand together Anytime Anywhere Any device Anytime Anywhere Any venue PERFORMING ARTS
  25. 25. In summary •The Web has changed into a Web of data •Consumption is now mediated by data-hungry algorithms •The performing arts are behind •We need to catch up together
  26. 26. Solutions? Research converges in one direction: the performing arts sector needs… 1. Data standards; 2. Good quality, interoperable data published as linked open data
  27. 27. Questions so far?
  28. 28. The Linked Digital Future Initiative A multi-prong approach: • Action-Research • Deliver a shared data model • Prototyping • Translate performing arts information into linked open data • Digital literacy • Help arts organizations adapt to the digital shift & develop new digital collaboration skills Interoperability Discoverability Digital transformation Collaboration across the value chain
  29. 29. A Value Chain Approach Industry data is created at each stage of the value chain. It is however not captured and expressed as interoperable data… The Performing Arts System (adapted from Bonet & Schargorodsky 2018)
  30. 30. An interoperable data model The semantic layer
  31. 31. What kind of data are we talking about? Everyone is familiar with: • Financial data • Ticketing and donor data • Volunteer data • Marketing data • Performance measurement data In order to have meaning and value, this data needs to be connected to another type of data: • Industry data
  32. 32. Photo: J’aime Hydro by Christine Beaulieu. Co-produced by Porte Parole and Champ gauche. Photo credit: Pierre Antoine Lafond Simard. Named entity Class of similar entities Performing Arts Linked Data Model
  33. 33. Subject Predicate Object J’aime Hydo Is an instance of Performing arts production The same information can be expressed as a Resource Description Framework (RDF) triple Performing Arts Linked Data Model
  34. 34. Performing Arts Linked Data Model
  35. 35. Performing Arts Linked Data Model
  36. 36. Performing Arts Linked Data Model
  37. 37. Performing Arts Linked Data Model
  38. 38. A distributed database The data layer
  39. 39. A distributed database Imagine many databases, in different locations, connected to one another… This is made possible with: • A shared performing arts ontology (i.e., data model); • Graph databases with linked open data.
  40. 40. Relevant Base Registers / Authority Files Named Entities • Works (literary, musical, choreographic) • Editions/Translations of Works • Character Roles • Performing Arts Buildings • Organizations (presenting organizations, musical ensembles, theatre troupes, dance troupes) • Humans (writers, composers, performing arts professionals) Base registers and authority files play a key role in interlinking datasets from various sources. Some statistics (Wikidata, April 2019) • 420’000 musical works • 21’000 plays • 820 choreographic works • 11’000 character roles • 20’000 performing arts buildings • 260’000 musicians • 250’000 actors/actresses • 87’000 musical ensembles • 5’000 theatre troupes • 340 dance troupes and steadily growing... Databases • ISNI • VIAF • MusicBrainz • Discogs • IMDb • Songkick • Wikidata Slide credit: Beat Estermann
  41. 41. A linked ecosystem for the performing arts
  42. 42. The Vision: Many Stakeholders – One Knowledge Base Performing Arts Value Chain International Knowledge Base for the Performing Arts One distributed knowledge base Many Stakeholders Many applications
  43. 43. In summary • Linked open data technologies are mature. • These technologies enable: • Data exchange • Decentralized, distributed knowledge base • Radical collaboration along the performing arts value chain • To take advantage of them we must depart from our analog, closed, competitive mindsets
  44. 44. Questions?
  45. 45. Mapping your value chain
  46. 46. Value chain mapping exercise What does your current value chain currently look like? Let’s map it! To create a value chain diagram, begin by answering three questions: 1. Scope: What is the value offering (product or service) you will analyze? (write description in the heart) 2. Customer/audience/member: Who is your ultimate customer/audience/member? Draw them as a circle on the far right of your diagram and write a short description. 3. Last entity: What source does the customer receive the offering directly from? Draw this party as a square to the immediate left of the customer. 4. Prior entities: What other organizations, if any, provide unique inputs to that organization? Draw them as additional squares to the left. AUDIENCEORG 1 ORG 1 ORG 1 Slide credit: Akoulina Connell
  47. 47. DIGITAL TRANSITION: towards shared value What changes would you see in your value chain if you take the following elements into account? • Linked Open Data (networked discoverability) • Networked audience (customers) + reciprocity loops (iterative value generation) • Demographic diversity (new perspectives on shared value) • Cooperative models for competetive advantage (co-opetition with traditional competitors for a larger collective share of the attention market)
  48. 48. Value chain mapping exercise, continued… Conduct the same exercise, but now consider… Focal organization: Which organization is the focus of your analysis? (e.g. your own organization, or another whose value chain you are studying) Add an additional outline to the square around it. Value and data exchange: Between each square, add arrows in both directions. Label each arrow pointed to the right to indicate what value and/or data is being delivered to the downstream party (e.g. product, service, or creative element on them). Label each arrow pointed to the left to indicate what value is being delivered upstream / data shared. Symmetric competitors: For each square in the chain, identify the symmetric competitors (i.e. organizations that offer similar value, with a similar operating model). Add them to diagram as rectangles below the square they compete with (e.g. if the NAC was in your value chain as the final distributor, below it you would put a rectangle indicating other brick and mortar distributors). Asymmetric competitors: For each square in the chain, identify the asymmetric competitors (e.g. an alternate distribution channel, producer, or creative contributor/collaborator that can substitute for the organization, but has a different organizational model). Add them to the diagram as trapezoids above the square they could potentially serve as a substitute for.
  49. 49. Value chain mapping exercise Figure 3‐11: Complete Value Train Diagram for Sony Music in the MP3 Music Market (Rogers, 2014)
  50. 50. Mapping Resources for Your Digital Transformation What resources do you need in place to enable your organization’s digital transformation and create the conditions where new shared value can develop, evolve, and thrive over time? • Partners & Collaborators (similar organizations or organizations with complementary or divergent offerings that broaden the scope of opportunity) • Funders (financial stability will be essential • Skills & Training (where can training be sought to support internal capacity development?) • Specialists / Experts (are there gaps in your organization’s competencies? Where can you go to seek mentorship, strategic advice, or hire for specific skillsets?)
  51. 51. What’s next?
  52. 52. Linked Digital Future: state of implementation • Standard data model -  • We have a semantic layer for our linked ecosystem • A data model without data is just an empty shell • We must build the data layer, together • A data model without adoption isn’t a standard semantic layer data layer
  53. 53. Research report recommendations 1. Populate a Canadian performing arts knowledge graph. • Event data – ephemeral data • Other industry data about works, artists, venues, and organizations – “permanent” data
  54. 54. Populating event data Culture Creates scrapes text from web pages and translates it into linked open data with the Footlight technology.
  55. 55. Populating a Canadian knowledge graph for the performing arts Individual arts organizations Existing databases, events listings, etc. Event data harvesting (Footlight) Platforms and information systems Slide credit: inspired by Culture Creates
  56. 56. Research report recommendations 1. Populate a Canadian performing arts knowledge graph. 2. Populate Wikidata.
  57. 57. What about Wikidata? Acknowledgements (for Wikidata section): Annelise Larson and Stacy Allison-Cassin
  58. 58. What is Wikidata? • Authority database for Wikimedia Foundation projects including Wikipedia
  59. 59. What is Wikidata? • Authority database for Wikimedia Foundation projects including Wikipedia • Anyone – humans and machines – can read, add & edit data & use it for free • Multilingual by design • Can be interlinked to other open data sets on the web of data.
  60. 60. How does wikidata work? • Contains data about all kinds of things and their relationships
  61. 61. How does wikidata work? Firehall Arts Centre 49°17'8" N, 123°5'48" W 150 Downtown Eastside Vancouver • Contains data about all kinds of things and their relationships • Follows linked data principles: it forms a graph (a linked network) of relationships • Indirect relationships can be inferred and queried • Provides meaning and context
  62. 62. What can we use Wikidata for? Any industry data of a “permanent” nature: • Performing arts buildings • Organizations • Recurring festivals • Artists, bands Want to learn more?
  63. 63. Wrap-up
  64. 64. How do you feel?
  65. 65. Final poll How has data to contribute to a linked open data ecosystem?
  66. 66. INFORMATION Connecting the dots… from the Web of documents to linked open data WEB PAGES KNOWLEDGE WWW Schema Semantic SEO Linked Open Data Recommendation systems + Cdn knowledge graph + Wikidata + Data reuse + + + Traditional SEO
  67. 67. Learn more about a Linked Digital Future • Ask for guidance from a Digital Navigator • Participate in the Digital Discoverability Program • Learn more about linked open data • Find resources about Wikidata
  68. 68. Acknowledgements Advisory Committee • Jean-Robert Bisaillon, President and Founder, iconoclaste musique inc. - metaD - TGiT • Clément Laberge, independent consultant, education, culture and technology • Margaret Lam, Founder, BeMused Network • Tammy Lee, CEO, Culture Creates • Mariel Marshall, Co-Founder, StagePage • Marie-Pier Pilote, Responsable des projets et du développement numérique, RIDEAU Key contributors • Beat Estermann, Bern University of Applied Sciences • Gregory Saumier-Finch, CTO, Culture Creates • Adrian Gschwend, Zazuko GmbH • Stacy Allison-Cassin, York University • And many, many more contributors
  69. 69. With thanks to the Linked Digital Future collaborators and funding partners
  70. 70. Key concepts
  71. 71. Interoperability Interoperability is the ability of a system or an application to work (connect, exchange information, make use of information) with other systems or applications, at the current time and in the future. • For example, systems that use the same Linked Open Data standards are interoperable semantically and technically: they can understand one another’s information, and they can exchange it without even needing to connect through an intermediary such as an application programming interface (API).
  72. 72. Discoverability Discoverability is the ability of information: • to be easily found when specifically searched for; • to be recommended when search for; • to be readily available when not specifically searched for; • and to be explored in more details. Currently, much information about the performing arts in Canada is not even findable by traditional search engines or by voice-enabled personal assistants.
  73. 73. Value chain A value chain or production chain (which is referred to as 'creative chain' in the Conceptual Framework for Culture Statistics) has been described as a sequence of activities during which value is added to a new product or service as it makes its way from invention to final distribution. The stages of the creative value chain are: creation, production, dissemination and use. If Linked Open Data is a technology intended for end users, it depends and quality data and metadata being generated in the production and dissemination stages.
  74. 74. Knowledge Graph Even experts disagree as to what a “knowledge graph” actually is. In simple terms, one could say that a knowledge graph is the combination of two things: 1. A data model (a conceptual model for representing information as data, with formal ontologies providing a set of rules about how knowledge must be organized within a given knowledge domain); and, 2. The actual data, stored in a graph database. Read more