1PRESENTATIONThe unifying motto for the Olympic and Paralympic
Games in Paris 2024, “Games Wide Open,” serves asa global call for Olympic scholars to convene in Parisfor an exhibition of innovation and novel experiences.
The 2024 iteration of the Olympics is posited as anembodiment of the “Games of the Future,” a concept
central to the 11th International Sport Business
Symposium’s thematic directions. Overall, theSymposium supports the IOC Agenda 2020+5 and the
newly promulgated Olympic AI Agenda—both
documents delineate mega trends and strategies toensure that the Games and Olympic Movement
remain innovative amidst the challenges of the Digital
Era. Concerning these propositions, the Symposium’s
11th edition was distinguished by hosting theinaugural presentation of booklet titled “DataStrategies for AI, Management, and Research in PE,
Sports, and Leisure”.
2This non commercial publication, authored by ateam of scholars including Bianca Pena and
Lamartine DaCosta—who also contributed to this
symposium—aligns with both the Symposium’s
themes and the progressive strides of the
Olympic Movement and Paris Olympics.
Originating from the State University of Rio deJaneiro—a longstanding collaborator withprevious Symposium editions—the booklets’themes resonate with the enduring Olympic
pursuit of excellence. We trust that thisforthcoming edition will be met with acclaim andwill embody the Olympic ethos of ‘faster, higher,
stronger,’ which inspires us all.
Prof Dr Holger Preuss
11th International Sport Business Symposium
Paris, August 8th, 2024

3

MADE POSSIBLE BY

www.emuseudoesport.com.br/us/home

WITH SUPPORT FROMTECHNICAL COOPERATION

4

Dados Internacionais de Catalogação na Publicação (CIP)(Câmara Brasileira do Livro, SP, Brasil)
Data strategies for artificial intelligence,
management and research in physical education,
sports and leisure [livro eletrônico] /author-organizer Lamartine DaCosta. -- Rio deJaneiro : Gama Assessoria Empresarial, 2024.
PDF
Vários colaboradores.
ISBN 978-65-85218-11-5
1. Educação física 2. Esportes 3. Inteligência
artificial 4. Lazer I. DaCosta, Lamartine.

24-216570

CDD-796Índices para catálogo sistemático:

1. Esportes

796

Eliane de Freitas Leite - Bibliotecária - CRB 8/8415

The eMuseum of Sport in Rio de Janeiro offers
this publication for non-commercial use. It serves
as an informative resource regarding sports andrelated topics.The booklet’s format is
unconventional and experimental. It may differ
from traditional publications, emphasizing
creativity and innovation. The contents within thebooklet are subject to change in future editions.
Readers should be aware that updates orrevisions may occur. If you wish to reproduce any
part of this publication, ensure proper attribution
by providing appropriate credits. Thisacknowledgment helps maintain transparency
and respect for intellectual contributions.The text
and images in this publication were partiallygenerated or enhanced using AI tools. The
eMuseum of Sport holds the intellectual property
rights for this publication for the year 2024.

5

Author-organizer:
Lamartine DaCosta
Author-editors:
Ana Miragaya
Bianca Gama Pena
Elizabeth Harris
Évlen Lauer (designer)

Ailton Oliveira
Cristiano Belém
Leandro Nogueira
Lucas Guesse

6

CONTENTS
8-18

Theoretical positions

19-22 Data Strategies
23-50 Strategic Case Studies
51 Hybrid AI publication
52-60 Concluding remarks

7

INTRODUCTION
Lamartine DaCosta
Author - Organizer

BACKGROUND
OF THE STUDYAND PROACTIVE
APPROACHESTO AIThis publication is a proactive and collaborative achievement
between Generative AI and human expertise, designed with aninterface that prioritizes ease of use, offering brief, clear texts
alongside visual aids. It introduces readers to a condensed
version of the contents, guiding them through the organizedknowledge and providing internet links for access to in-depth
information. This digital library is customized to fit the uniqueinterests and choices of each reader, featuring a curated
collection of Position Papers, articles, and books that contributeto the structured knowledge base. Thus, the publication
specifically addresses data collection and management within
Brazil’s Physical Education and sports sectors, presenting fivecase studies that illustrate the use of traditional analog archives
and a sixth accomplishment that introduces a digital andupdated Database model for ongoing developments.

8

The role of AI is examined in this context, highlighting its
operational dependence on data to provide meaningful
insights and functionality. Additionally, the study investigates
the application of Data Intelligence and the Small Data model
within organizations. These approaches are gaining traction
as they offer more human-centric digital interactions or allow
organizations to engage with AI on a smaller scale while
maintaining autonomy. The focus is on educatingorganizational members about data management andcreating smaller, more relevant data sets that align withspecific organizational goals.

The editorial team, comprising eight Editors and one
Organizer, was assembled to reflect gender equity. 'fhey are
responsible for crafting the booklet's format and contenl,
ensuring a balance between collective editorial guidance andindividual authorial input through Position Papers orreferences to their scholarly works.
9In short, the booklet's experimental nature is underscored by
its non­commercial distribution strategy, aimed primarily at theacademic community with a strong emphasis on research anduniversity-level education and post-graduate studies. This
proactive approach prioritizes scientific knowledge as thecornerstone in addressing AI s hybrid applications. Overall,these choices entail more guarantees in terms of intellectual
property and incentives for international collaboration.

Considering the connections of this study with Physical
Education and sports, it fias also adopted as a general
guideline the approaches of Olympic Studies and the recent
Olympic Agendas geared towards digital technology and AI.
Position paper - Background, DaCosta 2024:
https://www.sportsinbrazil.com.br/paris_2024/
paper_interview.pdf
10Bianca Gama Pena
Rio de Janeiro State University­ eMuseum of SportAGENDA 2020+5

BASIC
REFERENCE
POINTSFOR THIS
PUBLICATION

The tradition of Rio de Janeiro State
University {UERJ) in Physical Education,
sports and the Olympic Movement inparticular, is linked to innovation and the
publication of advanced knowledge. This
leads us to the present work, based on theInternational Olympic Committee's Olympic
Agenda 2020+5. With this general guidance,
we selected digital technologies as the baseof the development to be promoted by the descriptive and analyticalcontents in this booklet. Thus, we assume the role of Olympic
Movement stakeholders and entrepreneurs of the Digital Era.
Olympic Agenda 2020+5:15 recommendations(Lausanne: IOC, 2021)
11

OLYMPIC AI AGENDA
The Olympic Agenda for Artificial
Intelligence (Al) is a more recent advancemade by the International Olympic
Committee that was also incorporated bythis publication as a point of reference.
After all, Al represents the front line ofdigital technology. Thus, as digital
innovation and Olympism entrepreneurs,
the authors of the studies ahead createdanswers following the Al Agenda but
respecting their own autonomy. Ahead, we
present experimental responses to the
doubts brought on by the progress of Al.
Olympic Al Agenda (olympics.com)

12

INTELLECTUAL PROPERTY
The digital transition and the insecurity generated by Al due to thedemands made by its users has strengthened the requirement ofguarantees of originality from authors of academic works. In thiscontext, we strive to obtain recognition from international entities bydeveloping innovative projects. Through the eMuseum of Sport, we
received an award from the World Intellectual Property Organization-WIPO as a project that uses IP as a strategy and a means ofdeveloping innovation. Moreover, in the context of problems
generated by Al in various fields of intellectual production, WIPO
joined forces with UERJ's initiatives in Physical Education, sports
and Olympic themes, promoting a booklet with guidelines about
copyright law. The publication will circulate alongside the present
study and can be accessed through the link below.
https://expo3d.emuseudoesporte.com.br/downloads/
Cartilha%20Propriedade%20Intelectual%20impressao.pdf
13

INTERNATIONAL COOPERATIONThe example of the WIPO collaboration calls
attention to the recognition that an innovative
project such as this publication gains as the resultof international cooperation. The idea tointernationalize this study is based on previous
experiences. An agreement with Tsukuba
University (Japan) during the Tokyo Olympics led tojoint scientific publications, while anotheragreement, with Patras University (Greece),brought about an Erasmus+ agreement for student
and faculty mobility (2016-2022). There is also the
influence of recent cooperation initiatives with the
University of Maїnz (Germany) and the sports
museums of Athens and Barcelona, involving thelicensing of the eMuseum of Sport’s software, withthe possibility of extension to other museums in
the future.

14

Another international cooperation initiative was thesubmission of a project to a Canadian businessaccelerator, aiming to internationalize an extension
project from UERJ. Summaries of various
international experiences linked to UERJ’s Olympic
field can be found at the links below.

International Cooperation – eMuseum
Sport 2020:
https://expo3d.emuseudoesporte.com.br/
downloads/eBook%20%20TecnologiaInovacaoStartups%202020.pdf
Video Cooperation Summit 2024 – Portuguese
and English: https://youtu.be/oE9lG03rN20?
si=HmeUQO2PI2dWQyQM
Olympic Studies - International Cooperation:
https://youtu.be/mXnRRI-rePQ?
si=yuRTBs9TDNKADkTe

15

SUMMARY OF PROPOSALSAND PRELIMINARY DIRECTIONS
Elizabeth Harris
Review Editor

SCOPE OFWORK IN
EXPERIENCE(1) Define Data Intelligence as themain focus of publication in terms of
conducted by institutions or
individuals when identifying demands,
collecting, and structuring data of
local interest or transactions resulting
from entrepreneurial activities.
Consequently, an internal or groupknow-how is created for data analysis
and use of AI tools, seeking user
independence as well as better
results.
16

(2) Describe, through summarized syntheses,
past and present examples of data strategy
considering approaches to public transparency,
management, research, and AI use.
(3) Establish an experimental awareness forboth the contents and format of the book.
(4) Create and analyze innovative and hybrid
relationships – i.e., with human sharing – forthe use of generative AI in light of the data
strategies reported and analyzed in the new
publication.
(5) Follow current Olympic Agendas regarding
Digital Technologies and Artificial Intelligencein view of ethical recommendations from
Olympism.
17

(6) Preserve the practical and didactic
meaning of book approaches so as to meetgreater demands from Physical Education
professionals aimed at technical and scientificknowledge, mainly those from autonomous
activities on a smaller scale.
(7) Produce summarized syntheses andimages based on a human-machine hybrid
composition including in each approach ofmajor importance a link with text from an
identified author or formally referenced work.
(8) Prioritize intellectual property andnational/international collaboration in
definitions and actions recommended for DataIntelligence-related activities.

18

Lamartine DaCosta
Author - Organizer“Data” refers to quantitative expressions, such as numbers,
degrees, and volumes. They can be presented in various ways,
including tables, graphs, textual descriptions, images, orsounds. When analyzed, these data acquire practical meaning
and become operational information.
The present study focuses on data as a tool for generatingknowledge, managing organizations, and conducting
research. Six cases were selected to exemplify strategies forhandling data in Brazil and abroad, spanning the period from1971 to 2024. Four cases (1971, 1999, 2002, and 2005)
constitute analog repositories originally published as
research in books and are now retrievable as static PDF files.

DATA
STRATEGIES

19

These cases form accessible collections via
the Internet, especially through the VirtualSports Center (Centro Esportivo Virtual cev.org.br). Two of these cases (2002 and2005) are reinterpreted here from theperspective of Data Banks.
Due to their analog nature, these collections
have not been updated, although they remain
as sources of reference and are suitable forparticipating in archives accessible togenerative AI. The fifth case under
examination (2015) emerged as a project
involving data modeled from studies andresearch conducted by various academic
entities in Brazil and abroad. These data are
retrievable as works published in digital
media.

20

• Repertories:• Analog: 1, 2, 3, and 4• Mixed file: 5• Digital file: 62
3

1• Data bank:• 3 and 4

Plataforma Educação Física em Dados
Módulo de Consulta Pública•Database:•6•Static:• 1, 2, 3, and 4•Dynamic:•6•International:• 3 and 5.

4
6
5
21

In turn, the sixth case under examination is the Confef Data
Platform, launched in 2024 as a digital technology project. It
consists of multiple dynamic files and functions as a databasewith programmed operational procedures. Unlike the genericand openly queryable Data Bank model, the Confef Platform
is more structured and updatable, capable of operating withvarious connected repositories.
In summary, data strategy involves actions such as
identification (demand, nature, availability, quality, quantity,
and cost), collection, aggregation, structuring (accessible via
references), organization (v.g. Data Bank and Database), andusage management. It’s important to note that unstructured
data is unable to circulate in digital media.
https://www.linkedin.com/pulse/understanding-datadefinition-importance-its-four-key-manas-jain
Journal of Medical Internet Research - Data Work:
Meaning-Making in the Era of Data-Rich Medicine(jmir.org)

22

Lamartine DaCosta
Federal University of Rio de Janeiro 1971

DEMAND IDENTIFICATION
AND DATA COLLECTION

CASE STUDYNATIONAL PE
AND SPORTDIAGNOSIS 1971

Between 1969 and 1971, the Institute ofApplied Economic Research (IPEA), a
federal government agency, led thedevelopment of the DIAGNÓSTICO as thefirst data survey about physical activities in
Brazil. The project was detailed andcoordinated in its execution by Lamartine
DaCosta. The team assembled to collectthe data consisted of 80 individuals who
identified, mapped, and visited 426
respondent institutions across the country.

23

Data structuring and access
The collected information resulted in a 392page book, including 112 tables withquantitative data and analytical texts. The
publication was made available for free tofacilitate public consultations
PE and Sport National Diagnosis 1971
(Portuguese):https://cev.org.br/biblioteca/diagnosticoeducacao-fisica-desportos-brasil

24

25

The methodology used in the 1971 Diagnosis for datacollection, organization, and interpretation was based on Data
Science, which was taking its initial steps at the time. From thistheoretical choice, practical methodological paths were derived
from the emerging field of System Dynamics for data
management, research, and governance. This approach was
adopted because there were no statistics available on physicalactivities in Brazil, and System Dynamics emerged naturally asa solution. In summation, this procedure operated withselected data aggregations, identifying trends and patterns anddefining points of process changes (feedback loops). These
aggregations allowed for data description and analysis. Today,
variations of this methodology are still used within the field ofData Science, which has become predominant in the digital era.
System Dynamics updated (2019):
https://www.sciencedirect.com/topics/engineering/systems-dynamics

System Dynamics, J. Forrester in McKinsey Quartely, 1995:
The beginning of system dynamics | McKinsey

26

Leandro Nogueira
Federal University of Rio de Janeiro 1999

DEMAND IDENTIFICATION
AND DATA COLLECTION

CASE STUDY OF
PROFESSIONALQUALIFICATIONIN PE 1999

The survey on professional education in
Physical Education in Brazil was carriedout in 1999 with the segment of Physical
Education colleges due to its increasingprominence in expansion, originallycaptured in the 1971 Diagnosis. Data
collection was conducted by master’s anddoctoral students in Physical Education
who participated in a research project now
redefined as an update of the 1971 data.
The field collection phase involved on-site
visits to the responding institutions. The researcher responsible
for the study and the main author of the book was Lamartine
DaCosta.
27

STRUCTURING AND ACCESS
The data structure was organized similarly
to 1971, creating quantitative tables withadditional text, but with more refined
analyses and historical comparisons withother countries. Aggregation and accesswere facilitated through a commerciallyavailable book with national circulation.
Currently, this data repository is freely
accessible in the Virtual Sports Center(Centro Esportivo Virtual – CEV), Brazil.

28

The final organization of the data published in 1999 followed
the tradition of scientific research, structuring tables based on
quantitative values, similarly to what occurred in the 1971
Diagnosis. From this analog format, analyses andinterpretations were generated, completing the data utilization
cycle. This procedure continues today in digital archives,offering greater possibilities for management and updates, as
will be evident in the subsequent approaches. Notably, in termsof advancements from 1999 compared to 1971, more detailed
analyses of the most recent case are highlighted.
Survey on Professional qualification 1999 (Portuguese):https://cev.org.br/media/biblioteca/4064697.pdf

29

Ana Miragaya
Estácio University
Petrópolis

DEMAND IDENTIFICATION
AND DATA COLLECTIONDATA BANKSURVEY SPORTFOR ALL 2002

Between 1998 and 2002, Lamartine DaCosta and Ana Miragaya
led a survey on the situation of Sport for All (SfA) in 36
countries, promoted by UNESCO and TAFISA, bringing together
87 informants, 50% of whom worked in universities. The data
demand in this case was to diagnose non-formal practices ofPhysical Activities according to target groups interventions,practice locations, promotion among the population, socialchanges, marketing and sponsorship strategy, and financial
resources. For data collection, a booklet was printed in English
for the training of the respondent authors and thecorresponding aggregation by country.
30

The organization of the data followed the criteria oftables with numbers to allow analysis according totrends or patterns. Under these conditions, the study
was explicitly presented as a Data Bank, thus
constituting an advance in relation to the 1971 and1999 surveys.

STRUCTURING AND ACCESS
The data were structured using a 792-page book,marketed by an international publisher, providing
relevant access to the content of the work. It isessential to mention that this research sought toexamine the SfA for its meanings of inclusion andsocial change, and that it was a pioneer ininternational cooperation as it set up a Data Bank in
the area of sports.

31

The difference between the repertoire organized in 2002 andthe other cases examined here is that the complete cycle ofdata collection, organization and analysis was carried out in
the SfA book. There was then a successful case of setting up
a Data bank with data analysis as a fidelity test of collection
and structuring of data. Another pioneering aspect of the2002 project consisted in the preliminary training of the 87
authors in the sense of organizing the data collected andstructured in their respective chapters. A standard framework
had been developed for the chapters with data management
instructions and respective descriptions and interpretations.
The support for this orientation consisted of a "call for
authors" style booklet delivered to the contributors of theinternational survey. It can also be said that the Sport for All
initiative consisted of a preliminary action in terms of DataIntelligence in its recent proposals. A similar procedure wasused in the 2005 Atlas of Sports in Brazil, which trained its
410 authors with didactic texts delivered via Internet.
32

Access to the book Sport for
All - 792 pp (2002):
https://books.google.com.br/books?
id=EZrVlXazCnEC&printsec=frontcover&hl=pt-BR&source=gbs ge summary
r&cad=O#v=onepage&q&f=false
Booklet Tafisa & Unesco
for authors Sport for All:https://www.sportsinbrazil.eom.br/paris 2024/book tafisa and unesco.pdf
Sport for All Data bank analytics:
https://www.sportsinbrazil.com.br/paris_2024/sfa_article.pdf
33

DEMAND IDENTIFICATION
AND DATA COLLECTION
Lamartine DaCosta
Gama Filho University Rio deJaneiro 2005

CASE STUDYATLAS OFSPORT 2005

The 2005 Atlas of Sports in
Brazil project had a larger-scale
Data bank development than its
predecessors between 1971 and2002 because it was organized
into 300 segments. These data
focuses corresponded to Physical
Activity (PA) events, institutions,and knowledge production in Physical Education, sports, andleisure. With Lamartine DaCosta as the organizer, this survey
was the result of a team of 17 editors and 410 volunteer authors
supported by the Federal Council of Physical Education.

34

The data in this case consisted of descriptive texts andquantitative expressions in historical and geographical order foreach segment. The collection strategy involved connecting eachfocus – later transformed into a chapter in the published Atlas –with authors specialized in the adopted approach. The progress of
this work was monitored by the editors through a standard set of
written instructions and Internet-based communication with eachindividual author. In summary, the Atlas survey represented an
improvement over previous data strategies, with greater authorial
involvement from volunteer contributors.
Structuring and Access
The collected data was structured in an editorial arrangement
within a 950-page double-sized book, comprising 300 chapterswith texts in Portuguese and abstracts in English. As an
additional feature, the Atlas included a collection of photos andscenarios related to quantitative data across the entire country.
The chosen format also had a digital PDF version, accessible
online as a non-commercial work up to the present date.
35

Rationalities of data
structuring in the 2005 Atlas(a) Mapping: Memory and current conditions of physicalactivities in Brazil, constituting a set of spatial data (maps andfigures) and quantitative data (tables, charts, and graphs) as
well as qualitative data (descriptive and analytical texts), with
regional and national interpretations.
(b) Database: A collection of expressions focused on trends,
patterns, proportions, and samples related to development,
expansion, stability, insufficiency, or regression.
(C) Support for National Statistics: Qualitative andquantitative data representing trends and patterns in
geographical, institutional, or population segments that cancomplement or provide context for surveys conducted by theBrazilian Institute of Geography and Statistics (IBGE) on the
topic of physical activities with economic and socio-cultural
importance.
36(d) Continuous Improvement: Organizingcollected information to enable its
replacement with increasingly reliable or
statistically valid data.
(e) Texts and Figures: Formatting
presentations to complement quantitative datawith written information and images, creating
meaningful blocks while adhering to minimum
database standards.

Atlas methodology (DaCosta, 2005):
Centro Esportivo Virtual | CEV | Metodologia doAtlas e Apresentação dos Capítulos

37

Organization of data in scenarios - Atlas of Sport 2005

38

The Atlas of Sport 2005 included, in addition
to data organized by collection and analysis
segments, a dataset representing states,
municipalities, or the entire nation under
the denomination of “scenarios” (as seen in
the image with graphs). By covering these
extensive areas, the limitations of Data
Science methodology based on segmenttrends-patterns were overcome, making it
more suitable for localized observations. By
adopting this scenario-based approach, theAtlas incorporated data from other existingsources in Brazil related to Physical
Education, sports, and leisure.

https://cev.org.br/biblioteca/cenario-industria-esporte-domarketing-eventos/
39

Ailton Oliveira
Federal University of Sergipe
Identification of demand and data collection
A project by the Federal
Government of Brazil aimed at
mapping sports practice in the
country through surveys andresearch conducted by six Federal
Higher Education Institutions.
This purpose was implemented ina decentralized manner,collecting data related topractitioners, infrastructure,legislation, and investments.

CASE STUDY
OF NATIONALDIAGNOSIS 201540

STRUCTURING AND ACCESS
Publication of the survey results by institutions linked to theproject with data direction for entities promoting Physical
Activities for the population. The organization, execution,and publication of the research involved collaboration fromuniversities in Italy and the United Kingdom.
The National Sports Diagnosis, also known as Diesporte,
aimed to promote research using collected data, in addition
to providing various consultations and allowing for updates.
However, due to changes in the Federal Government, thedynamic intentions of Diesporte were not fully realized. As aresult, today there are published works without properorganization, similarly to the other cases mentioned. The
book published in the United Kingdom and the articles
published in Italy about Diesporte’s functioning or using
data from this project now represent the results of thepreviously promoted international cooperation.
41‘Knowledge as Legacy’ in Poyter 2015 – Diesporte:
https://library.olympics.com/Default/doc/SYRACUSE/62386/megaevent-cities-urban-legacies-of-global-sports-events-ed-byvalerie-viehoff-et-al?_lg=en-GB
Diesporte in Spliss 2022 pp. 66 – 67:
https://www.researchgate.net/publication/
259466482_ORGANIZATIONAL_STRUCTURE_OF_BRAZILIAN_PAR
ALYMPIC_SPORTS
Diesporte in Mussino – 2022 pp. 199 – 207:https://www.researchgate.net/publication/
360046656_Physical_and_Sports_Activities_Diagnosis_in_Brazil_
Methodology_to_Support_Policies_to_Improve_the_Population's_
Quality_of_Life42

CASE STUDYOF DATA
PLATFORM IN
PHYSICALEDUCATION
2024

Cristiano Belem
Federal Instituteof Espirito Santo

Lucas Guesse
Federal Instituteof Espirito Santo43

IDENTIFICATION OF DEMAND
AND DATA COLLECTION
In a partnership between the Federal Council of Physical
Education (CONFEF) and the Federal Institute of Espírito Santo,
data management tools were developed and organized throughtwo digital platforms. The objective of this project was tointegrate the Confef System with the 22 regional councils(referred to as ‘Cref’) and connect them with associatedprofessionals and local societies throughout the country. Under
these conditions, the first platform serves the purpose ofproviding public information about physical education
professionals in various functional roles during their activities.
The second platform, in turn, utilizes collected data to guideinternal operations, both platforms relying on updated
information and various indicators of identified trends andpatterns. This comprehensive operation follows the DataIntelligence model, aiming to empower all individuals
associated with the Confef-Cref System in generating andmanaging data.
44

Another dimension of the adopted data strategy
is to create a specific repertoire of information
following the Small Data model, facilitating
integration between the databases of the two
platforms and other databases related toPhysical Activities and the health sector. These
decisions were made in response to thegrowing demands of the 650,000 members of
the Confef-Cref System and the 1,200 collegesor higher education programs in Physical
Education currently existing in Brazil. These
numbers have international relevance and givemeaning to the spatial distribution of
professionals associated with Cref in acontinental country like Brazil (see image).
Access to the Confef – Cref Platform:
Plataforma Educação Física em Dados
45

Platforms
1

STRUCTURING AND ACCESS
Presents data from various sources related toPhysical Education to provide the public withinformation about the presence, personal
characteristics, roles, and geographicaldistribution of physical education professionals
across the entire national territory.

2

Management tool that organizes Physical
Education data to allow cross-referencing of
information from different sources, supporting
the Confef-Cref System in its administrative
functions.

46

CASE
INTERFACESBETWEEN THE
USERS OF THE
PLATFORMS
AND ACCESS TO
THE DATA

47

THE LIKELY FUTURES OF
THE CONFEF PLATFORMS
Observing the meanings of the Confef-Cref Platforms inlight of current advances in digital technology and AI
language models allows us to anticipate possible future
developments of these tools. A review of updated knowledge
establishes the following expectations:
1. Consolidation of Data Intelligence and Adoption of the
Small Data Model: Within the scope of Confef-Cref, theconsolidation of Data Intelligence and the adoption of the
Small Data model are expected to occur naturally as the
Platforms undergo enhancements and expansion.
2. Progressive Incorporation of AI: The broadening scope of
the Platforms will result in the gradual incorporation of
AI as part of the efficiency achieved by these tools. This
trend is driven by the increasing involvement of AI inmanaging companies and institutions overall.
48

3. Growing Integration of Databases: Influenced by the
expansion of the Small Data solution, there is a rising
trend of integrating databases. This leads toassociations between diverse organizations,
including companies, universities, government
entities, etc. AI also plays a role in generating mutual
operational benefits.
4. Integration with New Databases: Stimulating or
implementing integration between the database
represented by the Confef-Cref Platforms and new
databases related to Physical Education and Sports(such as CEV, Atlas, Universities, etc.) is essential
due to the expansion of Small Data solutions and thedemands of AI.

49

Small Data:
https://www.weforum.org/agenda/2016/01/how-small-datawilltransform-companies/
Small data and scientific research:https://www.sciencedirect.com/science/article/pii/
S1877050923012711
Data Intelligence:
Explainable AI in big data intelligence of community detection
for digitalization e-healthcare services - ScienceDirect
Interface between users and platform:
Usability Evaluation of an Open Data Platform | Proceedingsof the 18th Annual International Conference on Digital
Government Research (acm.org)

50

Analog photo

AI listAI images

Position
paper link

Blocks of descriptive and analytical texts – AI + HUMAN

CLOSINGPROPOSAL –HYBRID AI
PUBLICATIONS

Traditional charts + comments

Links to library

Traditional infographics

Human conclusive remarks
51

DATA INTELLIGENCE
The experience from the six case studies
suggests that participants in the datacollection-organization-utilization cycle,
when well-informed or equipped withknowledge about their tasks, constitute asensitivity focus in data management.
Specifically, the cases from 2002, 2005,
and the implementation of the Confef
Platforms in 2024 indicated the feasibilityof Data Intelligence development. The
viability of this enhancement in otherprojects, such as Small Data and AI
implementation, also became predictable.

CONCLUDINGREMARKS52

DATA STRATEGIESThe account of data strategies experimented in
the six projects examined in this study yielded
effective results, either partially or entirely,across their different implementation stages.
In other words, creativity and awareness ofcontexts were present in each routine
environment. To summarize, the various
strategies resulted from pioneers andinnovators. Specifically, the conclusive focus
on the ‘strategy’ factor in the examples
mentioned earlier refers to an entrepreneurial
approach to handling data. It’s also essential
to anticipate specific actions for entity
management and research. Above all, theintention is to specify these strategies asnecessary for future combinations involving
Artificial Intelligence demands.
53

DATA BANK AND DATABASE
From the set of experiences mentioned earlier, three(1971, 2002, and 2005) were established as Databanks, although not always explicitly acknowledged
as such. On the other hand, the most recent
implementation (2024) represents a typical productof the digital era, organizing itself as a Database—capable of unfolding and integrating with various
repertoires and other external databases, which was
not possible in previous cases. The foreseeable
direction based on the analyzed experiences is tomaintain the availability of the three Data banks—for
instance, at the Virtual Sports Center—asrepositories for queries. Additionally, the intention isfor the 2024 experience to continue standing out as a
Database, leveraging its technologicaladvancements, and integrating with similar-scoped
projects for mutual development.
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HYBRID MODEL
The term “hybrid model” refers to a
combination of interventions involving
both technological processes and humanactions. These interventions aim toachieve a balance between the two, withactive participation from both parties. In
the context of data strategies, humaninterventions have historically been moreprevalent. However, as AI continues toexpand, a new environment ofrelationships is emerging, still in the earlystages of understanding and definition.
This publication aims to explore hybrid
solutions, as demonstrated in the current
work, with the expectation of generating
future developments
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INTERNATIONAL COOPERATIONIn retrospect, the Sport for All and Diesporte
initiatives played a significant role ininternational cooperation between 2002 and2015. In the initial experiment, Sport for All
established a Data bank with theparticipation of two international agencies
and 36 countries. These endeavors, driven
by academic purposes, offer insights into
potential new relationships with foreign
entities. Furthermore, they emphasize theimportance of knowledge exchange, aligningwith the International Olympic Committee’s
Agenda 2020+5. Notably, international
cooperation trends may intersect with the
integration of databases, particularly in thecontext of sharing information with artificial
intelligence systems.

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RESEARCH AND
MANAGEMENT
Research endeavors are inherently
reliant on data, a principle that also
applies to technology-driven
management initiatives. However, thelessons from this study primarily
centered around data strategies, which
exhibited a generic nature. The focus
was on the entire data organization
cycle, rather than solely its end use—a
topic of greater interest to bothmanagement andresearch. Consequently, the conclusive
direction for various data demands
pertains to their non-specificity, withthe exception of the end user.
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SMALL DATA
Small data is a Data Management model generally
understood as a small volume of data, with minimal
structured organization, subject to digital processing, andaccessible and comprehensible for queries, whether bycomputer, cell phone, or tablet. In summary, it is a solution
for greater independence and lower costs for organizations
or groups facing large technological and AI enterprises. For
the expected effects of data organization in universities,
clubs, gyms, etc., this management model and researchsupport are associated with Data Intelligence initiatives(enhancing participants’ capabilities). In terms of directional
forecasting, the Small Data solution has expanded andshould guide current trends in integrating databases amongorganizations transitioning digitally or expanding theiractivities. In summary, conclusive directions in this topic are
based on studies and published research on AI in current
stages, considering future predictions.
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CALL TO ACTION
The ongoing experience in the Confef 2024case reinforced the idea that data platforms
would expand and operate efficiently withthe support of Data Intelligence. Connecting
this expectation with previous findings, it ispertinent to create a general direction forthis publication, named ‘Data IntelligenceSmall Data-Database Integration.’ Thissynthesis of actions to be undertaken
ultimately assumes a composition with
Olympic Agendas and propositions related toIntellectual Property and International
Cooperation.

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The big role of Small Data in AI:
https://hbr.org/2020/02/small-data-can-play-a-big-role-in-ai
Small Data impact in AI:
https://www.afcea.org/signal-media/cyber-edge/
small-data-may-have-big-impact-artificial-intelligence-edge
Integration between Databases:
https://www.researchgate.net/publication/
374474809_THE_INTEGRATION_OF_ARTIFICIAL_INTELLIGENCE_INTO_DATABASE_SYSTEMS_AIDB_INTEGRATION_REVIEW
Integration of AI with Databases:
https://www.devteam.space/blog/how-to-integrate-ai-to-adatabase/60

Texts and images
updated on July
17th, 2024.

eMuseum of Sport,
Rio de Janeiro,
Brazil.
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