The document discusses the impact of the digital revolution on performing arts organizations, highlighting the need for adaptation to digital business models that prioritize data culture, collaboration, and shared value. It emphasizes that the performing arts sector lag in utilizing linked open data and presents various strategies for improving interoperability and discoverability of information. Ultimately, the document advocates for a collective approach among arts organizations to enhance their digital transformation and data standards.

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. 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. Business models in the digital
economy
A few strategic observations

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. 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. The Web
has been changing
Initially driven by a collaborative vision
Now driven mainly by commercial interests

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. 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. 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. 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. 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. 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. The Linked Open Data Cloud in 2014
570 datasets

14. Linked open data
in 2019
1240 datasets
• Twice as much as in 2014!
The performing arts
aren’t there yet.

15. And then…
Transnational tech giants also saw the potential of
linked open data.
• schema.org 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. From search engines to
recommendation systems

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. 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. 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. These aren’t challenges
you can tackle on your own

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. 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. Try for yourself
Search: “Movies near me” Search: “Shows near me”

24. To stand a chance, we must stand together
Anytime
Anywhere
Any device
Anytime
Anywhere
Any venue
PERFORMING ARTS

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. 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. Questions so far?

29. 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

30. 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)

31. An interoperable data model
The semantic layer

32. 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

33. 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

34. 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

35. Performing Arts Linked Data Model

36. Performing Arts Linked Data Model

37. Performing Arts Linked Data Model

38. Performing Arts Linked Data Model

39. A distributed database
The data layer

40. 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.

41. 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

42. A linked ecosystem for the
performing arts

43. 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

44. 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

45. Questions?

46. Mapping your value chain

47. 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

48. 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)

49. 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.

50. Value chain mapping exercise
Figure 3‐11: Complete Value Train Diagram for Sony Music in the MP3 Music Market (Rogers, 2014)

51. 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?)

52. What’s next?

53. 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

54. 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

55. Populating event data
Culture Creates scrapes text from web pages
and translates it into linked open data with the
Footlight technology.

56. 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

57. Research report recommendations
1. Populate a Canadian performing
arts knowledge graph.
2. Populate Wikidata.

58. What about Wikidata?
Acknowledgements (for Wikidata section): Annelise Larson and Stacy Allison-Cassin

59. What is Wikidata?
• Authority database for
Wikimedia Foundation
projects including Wikipedia

60. 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.

61. How does wikidata work?
• Contains data about all
kinds of things and their
relationships

62. 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

63. 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?
http://bit.ly/WikiArtsFacilities

64. Wrap-up

65. How do you feel?

66. Final poll
How has data to
contribute to a
linked open data
ecosystem?

67. 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

68. Learn more about a Linked Digital Future
linkeddigitalfuture.ca
• Ask for guidance from a
Digital Navigator
• Participate in the Digital
Discoverability Program
• Learn more about linked
open data
• Find resources about
Wikidata

69. 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

70. With thanks to the Linked Digital Future
collaborators and funding partners

71. Key concepts

72. 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).

73. 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.

74. 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.

75. 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.
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