ArticlePDF Available

Abstract and Figures

EmpoderaData – from the Spanish word empoderar ‘to empower’ – is a partnership research project between the University of Manchester (UK), Fundação Getulio Vargas (Brazil), Universidad del Rosario (Colombia) and Data-Pop Alliance (US and France). The project builds upon a successful data-driven, research-led paid internship programme in the UK (Q-Step) which enables undergraduate social science students to practise data skills through immersion in the workplace. Two-hundred and fifty students have benefited from the Q-Step programme in six years, many graduating into analytical careers in civic society and industry. EmpoderaData aims to build on this experiential learning initiative by developing a data fellowship programme in order to foster and develop data literacy skills in Latin America, led by the need to address society’s most pressing issues and using the framework of the Sustainable Development Goals (SDGs). EmpoderaData Phase 1 explored whether the internship model would have relevance and usefulness within the context of three Latin American case study countries (Brazil, Colombia and Mexico). The team set out to establish a baseline of the state of data literacy and existing training programs in Brazil, Colombia and Mexico. As part of a ‘Big Data for the Common Good’ event, a workshop was held in São Paulo with thirty participants representing data literacy advocacy or policy formation and drawn from civil society, academia, the private and public sector. The main conclusions from this first phase are: (1) the most requested data literacy training need is for basic skills, including introductory statistics, foundation data analysis and methodological skills; (2) paid data fellowship models are acknowledged as a useful intervention; and (3) the notion of a ‘hybrid’ professional to build data literacy capacities for ‘social science’ purposes provides a practical way forward. In the EmpoderaData Phase 2 project our focus was on Colombia to explore the challenges and opportunities of developing a pilot data fellowship model there. Engaging with national, regional and international capacity development efforts, this highlighted a demand for partnerships between universities and organisations working on the social challenges represented by the SDGs. Partnerships ensure that the in-country data literacy pipeline is strengthened in a home-grown, self-sustaining way, producing a steady flow of data literate graduates into the institutions and sectors where critical data skills are most needed. We report on how the EmpoderaData project is exploring working with students studying STEM degrees at the Universidad del Rosario, to improve the application of statistical methods to the social sciences. The aim is to strengthen STEM skills and develop youth empowerment across Colombia, urban and rural areas, to improve the quality of statistical education at the national level, and support the skills needed to deliver the SDGs. In parallel, the Fundação Getulio Vargas (FGV) Business School in São Paulo agreed to trial the work-placement programme in their undergraduate business and public policy degrees through a programme entitled ‘The FGV Q-Step Center to improve quantitative skills in undergraduate business students’. This two-year-long funded study will enable us to explore the transferability of the internship model from the UK to Brazil. The paper will discuss how the programme was established (following the lessons learned from EmpoderaData), explain how this model will be implemented in FGV, especially paying attention to how the curriculum will develop to support it, and how the impact of the programme will be monitored. The knowledge exchange generated from this study will complement the research conducted through the EmpoderaData project. The paper will cover the progress of the EmpoderaData project and FGV-Q-Step Center to date and explore how we are developing these initiatives, the challenges we have faced, and how through partnership working we are developing capacity building in statistical and data skills training.
Content may be subject to copyright.
Statistical Journal of the IAOS 37 (2021) 1009–1021 1009
DOI 10.3233/SJI-210842
IOS Press
EmpoderaData: Sharing a successful
work-placement data skills training model
within Latin America, to develop capacity to
deliver the SDGs
Jackie Cartera,, Rafael Alberto Méndez-Romerob, Pete Jonesa, Vanessa Higginsaand
Andre Luiz Silva Samartinic
aUniversity of Manchester, Manchester, UK
bSchool of Engineering, Science and Technology, Universidad del Rosario, Bogota, Colombia
cFundação Getulio Vargas, Brazil
Abstract.
EmpoderaData from the Spanish word empoderar ‘to empower’ is a partnership research project between the
University of Manchester (UK), Fundação Getulio Vargas (Brazil), Universidad del Rosario (Colombia) and Data-Pop Alliance
(US and France). The project builds upon a successful data-driven, research-led paid internship programme in the UK (Q-Step)
which enables undergraduate social science students to practise data skills through immersion in the workplace. Two-hundred and
fifty students have benefited from the Q-Step programme in six years, many graduating into analytical careers in civic society and
industry. EmpoderaData aims to build on this experiential learning initiative by developing a data fellowship programme in order
to foster and develop data literacy skills in Latin America, led by the need to address society’s most pressing issues and using the
framework of the Sustainable Development Goals (SDGs).
EmpoderaData Phase 1 explored whether the internship model would have relevance and usefulness within the context of three
Latin American case study countries (Brazil, Colombia and Mexico). The team set out to establish a baseline of the state of data
literacy and existing training programs in Brazil, Colombia and Mexico. As part of a ‘Big Data for the Common Good’ event,
a workshop was held in São Paulo with thirty participants representing data literacy advocacy or policy formation and drawn
from civil society, academia, the private and public sector. The main conclusions from this first phase are: (1) the most requested
data literacy training need is for basic skills, including introductory statistics, foundation data analysis and methodological skills;
(2) paid data fellowship models are acknowledged as a useful intervention; and (3) the notion of a ‘hybrid’ professional to build
data literacy capacities for ‘social science’ purposes provides a practical way forward.
In the EmpoderaData Phase 2 project our focus was on Colombia to explore the challenges and opportunities of developing a pilot
data fellowship model there. Engaging with national, regional and international capacity development efforts, this highlighted
a demand for partnerships between universities and organisations working on the social challenges represented by the SDGs.
Partnerships ensure that the in-country data literacy pipeline is strengthened in a home-grown, self-sustaining way, producing
a steady flow of data literate graduates into the institutions and sectors where critical data skills are most needed. We report on
how the EmpoderaData project is exploring working with students studying Science, Technology, Engineering and Mathematics
(STEM) degrees at the Universidad del Rosario, to improve the application of statistical methods to the social sciences. The aim is
to strengthen STEM skills and develop youth empowerment across Colombia, urban and rural areas, to improve the quality of
statistical education at the national level, and support the skills needed to deliver the SDGs.
In parallel, the Fundação Getulio Vargas (FGV) Business School in São Paulo agreed to trial the work-placement programme
in their undergraduate business and public policy degrees through a programme entitled ‘The FGV Q-Step Center to improve
Corresponding author: Jackie Carter, University of Manchester,
UK. E-mail: jackie.carter@manchester.ac.uk.
1874-7655
c
2021 The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons
Attribution-NonCommercial License (CC BY-NC 4.0).
1010 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
quantitative skills in undergraduate business students’. This two-year-long funded study will enable us to explore the transferability
of the internship model from the UK to Brazil. The paper will discuss how the programme was established (following the lessons
learned from EmpoderaData), explain how this model will be implemented in FGV, especially paying attention to how the
curriculum will develop to support it, and how the impact of the programme will be monitored. The knowledge exchange generated
from this study will complement the research conducted through the EmpoderaData project.
The paper will cover the progress of the EmpoderaData project and FGV-Q-Step Center to date and explore how we are developing
these initiatives, the challenges we have faced, and how through partnership working we are developing capacity building in
statistical and data skills training.
Keywords: Sustainable development goals, data skills, statistical literacy, educational innovation, internships
1. Introduction
This paper introduces and outlines the EmpoderaData
project, a transnational collaboration to develop data
fellowship programmes within Latin America. These
data fellowships are conceived as a tool for building
statistical capacity to help deliver the UN’s Sustainable
Development Goals (SDGs) [1] by strengthening the
quantitative skills pipelines in countries. EmpoderaData
builds on the success of The University of Manchester
Q-Step Centre’s innovative programme (part of a larger
UK-based initiative) which develops the quantitative
skills of undergraduate social science students through
both classroom-based instruction and workplace learn-
ing through a summer paid internship programme. As
we describe below, this model has been successful on
a number of fronts, creating partnerships and graduate
employment opportunities between the university and
many organisations with statistical research skills needs,
such as polling organisations and local and national
government. Through EmpoderaData, we are working
with partners within Latin America to take practical
steps towards developing a version of this model which
will help achieve similar results, with a focus on the
SDGs. The SDGs require an increase in levels of sta-
tistical capacity across the board [2], including at the
level of individual statistical and data literacies, and
innovative solutions are needed to help deliver this. We
report in this paper on two different approaches that are
in development by Universidad del Rosario in Bogotá,
Colombia and FGV in São Paolo, Brazil, outlining their
strategy, progress and emerging challenges.
The structure of the paper is as follows. In Section 2,
we provide some background to the project, contextu-
alising and explaining in more detail the Q-Step pro-
gramme, its experiential learning-based approach to
quantitative skills development, and how the present
EmpoderaData project came about in relation to this.
In Section 3 we discuss the role of the SDGs in this
framework, outlining the unprecedented statistical ca-
pacity needs they create and why we believe a holistic
notion of data literacy is necessary to address them.
In Section 4, we focus on the two practical implemen-
tations currently in progress in Colombia and Brazil,
each of which adopts a different strategy. The Brazil-
ian intervention focuses on an immersion-based pro-
gramme similar to that of the Q-Step model, while the
Colombian initiative focuses on bringing real practical
social research projects into the university classroom.
Finally, we conclude by reflecting on the challenges
and opportunities that have emerged through this work
to date and outline the next steps for the project.
2. The background to the EmpoderaData project
2.1. The Q-Step Centre at University of Manchester
The EmpoderaData project was conceived in 2018,
following the development and successful roll out of a
paid internship programme at The University of Manch-
ester UK, which began in 2014 and enabled us to build
an annual cohort of ‘data fellows’. Through a UK na-
tionally funded programme strategically focused on de-
veloping data and statistical literacy skills (we expand
on these definitions below) in the undergraduate social
science population, the University of Manchester estab-
lished a Q-Step Centre [3], one of 15 in the UK. The aim
of the Q-Step programme was to create a step-change in
teaching quantitative research methods to social science
undergraduates. Q-Step centres were encouraged to be
innovative and experimental in designing resources and
educational and practical experiences, to add value to
the research methods curriculum and develop a cross-
national cohort of graduates who could enter the labour
market equipped to enter 21st century careers that re-
quire statistical competencies. With the wealth of expe-
rience in the University of Manchester Q-Step Centre
team in data-driven teaching [4
6] our approach was
two-fold. First, we created course modules that ensured
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1011
numbers and statistics were a normal part of the social
science curriculum, from the point at which students
enter their degree courses. We also introduced specialist
‘with Quantitative Methods’ pathways through degrees
for students who wished to specialise in data analysis
and statistics. This approach involved employing new
staff and developing substantively-led teaching materi-
als and activities that reflected the subject of the degree,
in our case this covered sociology, criminology, poli-
tics and international relations, English language and
linguistics, philosophy and economics. In other words,
the statistics and data analysis teaching was embedded
in the subject teaching [7]. Second, the team developed
a paid internship programme, working with external or-
ganisations across the public, private and voluntary sec-
tors to co-create data-driven, two-month long research
projects to enable students to take their learning from
the classroom and PC lab and put this into practice in
workplace environments, with research that matters to
those host organisations. The interns’ immersion into
applied social research projects provides the opportu-
nity to develop their analytical and research skills, as
well as their professional skills, alongside colleagues
(sometimes themselves in their early career stages).
Collectively these developments enable undergraduates
to learn statistics, using real-world data from official
sources (mainly) and hone their skills in a professional
environment. Carter [8] expands in her book on the
approach taken, describes how this is underpinned by
experiential learning theory, and illustrates through case
studies of former interns, and vignettes of current social
researchers, the benefits of ‘learning by doing’. The ini-
tiative has produced 250 data fellows in just six years,
with a further 62 taking place in 2021.
The University of Manchester’s strong track record
for teaching quantitative research methods to under-
graduate students on social science and humanities de-
gree courses, is accompanied by a strategic focus on
the Sustainable Development Goals (SDGs), the Uni-
versity’s third core goal being Social Responsibility.
The 2019 University of Manchester Social Responsi-
bility report made explicit reference to the Q-Step in-
ternships [9], noting that: ‘the Q-step paid internship
scheme has placed 200 students in 60 public, private
and third-sector organisations to undertake social re-
search that makes a difference and helps to identify and
progress social issues locally, nationally and globally.
The projects have addressed gender data gaps in devel-
oping countries, food poverty, recycling, immigration,
sociodemographic factors affecting university admis-
sion rates, violence against females, bilateral spend-
ing on HIV/AIDS, and modelling UK deprivation. In
2019, the scheme will be extended to three Latin Amer-
ican countries to develop a data programme around the
global SDGs’. In 2021, just two years after this report,
the University of Manchester was ranked first in the
world for its impact as measured against the SDGs [10].
This paper draws on the combined strengths of the data
fellows training programme (delivered through the Q-
Step internships) and the commitment to delivering on
the SDGs, demonstrated by the university.
By way of illustration, as noted in the extract from
the University’s SDGs report, the interns or data fel-
lows as we go on to call them throughout this paper
undertook projects that provided them with real world
opportunities to develop their data and statistical liter-
acy skills. Here we draw from two of the project outputs
(all students are required to produce a poster to evidence
their learning), chosen to reflect the research projects’
focus on the SDGs. Both examples were carried out in
consultation with Open Data Watch (an organisation
that works at the intersection of open data and official
statistics, see https://opendatawatch.com/) and Data2X
(a UN-led initiative working to improve the availability,
quality and use of gender data, see https://data2x.org/),
with the interns hosted at the first of these organisations
in Washington DC.
Niamh, a second year politics and international rela-
tions student, worked for the summer on a project called
Bridging the Data Gap assessing the availability of data
about the lives of women for selected SDG participat-
ing countries. Her role was to find, record and assess
gender-relevant development indicator data. Figure 1
was produced for the poster (required on completion
of the project) revealing that the human development
theme (domain) that had the highest percentage of sex-
disaggregated indicators for all countries included in
the study was Education, but that even for this theme
(noting SDG4 is Quality Education) a quarter of the
indicators included had no sex-disaggregated data.
Niamh’s project, conducted in 2018, followed a
project undertaken by another student, Grace, the pre-
vious year which also explored data gaps for a range of
SDG indicators. The findings of that project1were:
‘Within the 15 countries [included], 48% of all gen-
der indicators were missing. The health domain with
the greatest number of indicators (32) only has 58%
of indicators available with sex disaggregation. This
is the most complete coverage of all categories.
1
See summer intern’s project poster at http://hummedia.manches
ter.ac.uk/faculty/qstep/student-stories-2018/desouza.pdf.
1012 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
Fig. 1. Sex-disaggregation of indicators by domain. Source: Q-Step intern’s project poster at [11].
and in her final poster she reflected that:
‘I have learnt many skills . . . I was taught data liter-
acy, in order to find and reference the data needed
for the project. From looking through hundreds of
data sources, the importance of appropriate data
presentation has been emphasised, and I will use
these tools in my university research in the future.
I also garnered softer skills of working in a profes-
sional environment, an invaluable experience. For
each of the 15 countries five aspects of the 105 indi-
cators were recorded: availability, source, metadata,
consistency of data with indicators, and degree of
sex disaggregation.
Both students have contributed knowledge and em-
pirical findings to the host organisations, and Ni-
amh’s project built directly on Grace’s. Grace’s find-
ing appears in [12] ‘In total, sex-disaggregated data for
gender-relevant indicators are unavailable in any year
for 48 percent of the possible observations in interna-
tional or national databases’ [11, p. 7]. Although just
two examples of students’ work, they contributed to the
catalyst for the EmpoderaData project. Moreover, they
gave the interns an opportunity to learn through doing,
and resulted in an improved understanding of the data
associated with measuring the SDGs.
2.2. The EmpoderaData project
In 2018 an application to develop a pilot project in
three countries in Latin America Colombia, Brazil
and Mexico building on an existing international net-
work with colleagues overseas working in the area of
data skills training for Sustainable Development was
developed. This built on the data and statistical capacity
building expertise of two of the authors of this paper,
who co-led a Data Skills and Training Research Group
at the University of Manchester. The project was enti-
tled ‘Developing data and statistical literacy capacity to
achieve the SDGs: a pilot project in three Latin Amer-
ican countries’. The pump-priming funding (from the
UK Government’s Global Challenges Research Fund,
GCRF) supported collaborative research activities with
a view to preparing academic colleagues to respond to
future opportunities. Critically, this project was devel-
oped to work alongside in-country activities in Latin
America that were already underway in support of
achieving the Sustainable Development Goals (SDGs)
by 2030.
The project which became known as Empodera-
Data Phase 1 built on earlier work that had devel-
oped a research relationship between the University of
Manchester and DataPop Alliance (a global coalition
on big data and development created by the Harvard
Humanitarian Initiative, MIT Media Lab, and Overseas
Development Institute that brings together researchers,
experts, practitioners, and activists to promote a people-
centred big data revolution through collaborative re-
search, capacity building, and community engagement).
In 2018 Carter and Higgins, as invited and paid-for
fellows, presented their work and project ideas at the
MIT-hosted DPA-led event on Leveraging Big Data and
Sustainable Development. The purpose of the event was
to ‘strengthen the skills of UN staff and development
practitioners in selecting, creating, using and interpret-
ing data in support of 2030 Agenda for Sustainable De-
velopment’. Twenty-seven countries, all focused on de-
veloping capacity to deliver the SDGs, were represented
at the event.
Immediately following the MIT event a strategy
meeting was held where Carter, Higgins and members
of the DPA team explored next steps with a view to
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1013
extending existing research and training. This result-
ing bid application which became the EmpoderaData
project provided an opportunity to build on an es-
tablished network, meet face-to-face in Latin America
and Manchester and to begin to develop a large grant
application to further the work of the group.
The team’s primary aim was built on a belief that
education and partnerships provide the cornerstone of
achievable international development, and are required
to support the delivery of the Sustainable Development
Goals (SDGs). Our collective intention was to develop
insight into the transferability of a UK-led initiative
(the Q-Step Centre described above), by exploring the
feasibility of the model in three pilot Latin American
locations. We had two primary aims: first to work in-
countries to explore the data and statistical literacy ca-
pacity to help achieve the SDGs; and second to explore
the extent to which the University of Manchester Q-
Step paid internship programme might be useful work-
ing with in-country partners to help achieve the first
aim, through a data fellowship programme.
The first phase of EmpoderaData (further described
in Section 4) was designed to enable the team to:
(a) baseline the data and statistical capacity of those
selected countries for delivering the SDGs (working
in partnership with their statistical offices, and cross-
checking with the UNDP and others) and (b) explore
their attitudes and willingness to having an interven-
tion SDGs data fellowship programme developed with
their relevant stakeholders, building on the success of
the University of Manchester’s Q-Step work placement
programme. This fed directly into the second phase of
the project, which involved working closely with Data-
Pop Alliance colleagues in Colombia and the Universi-
dad del Rosario in Bogotá to tailor a solution that would
be able to achieve an impact in that context. We report
on key takeaways from this process in Section 5.
3. The SDGs and the need for data literacy
3.1. The SDGs as a framework
The UN Sustainable Development Goals set an am-
bitious global set of targets to reach by 2020. These
17 goals, which comprise 169 associated targets and
231 indicators with which to measure social and eco-
nomic progress, are the central framework for the 2030
Agenda for Sustainable Development [1]. There is a
global need at the level of statistical capacity to deliver
these goals [1] accompanied by scepticism of whether
they are indeed achievable [13
17]. Nonetheless these
goals set a broad backdrop to the complex global chal-
lenges we face. We argue in this paper that in order for
countries to be in a position to deliver on these goals,
a fundamental starting place is to have well-trained
citizens who are data literate.
The SDGs create a global need to deliver on an
agenda which relies heavily on quantitative data. This
creates a demand for human capital with the requisite
statistical and data skills to work on the challenges rep-
resented by the SDGs. The country-led model we report
on here is designed to address this need, rooted in the
belief that “capacity building is most effective when
it is home-grown, long-term in perspective and man-
aged collectively by those who stand to benefit” [18,
p. 897]. Through a focus on strengthening the data lit-
eracy pipeline in countries through a programme com-
bining statistical education and workplace learning, this
model aims to provide a steady home-grown flow of the
skills and competencies needed to tackle the SDGs into
the sectors and institutions where they are most sought
after.
The SDGs present enormous measurement chal-
lenges, as has been well documented in recent years [14,
19]. Not only do they require an unprecedented amount
and breadth of data for populating the many indicators,
they also require an increase in human capacity across
the board to ensure that there are enough trained indi-
viduals to work on these data and the complex mea-
surement problems they involve [20]. As such, there is
increasing recognition that data alone will not be suffi-
cient for meeting the challenges of the SDGs, and that
countries require investment in data literacy and skills in
order to sustainably improve the capacity for effective
use and understanding of data [21,22]. While the bene-
fits of investing in data production, infrastructure and
technology are relatively well-known and understood,
there is still comparatively little emphasis on the need to
invest in the individual capabilities necessary to make
effective use of these data and technologies. In a recent
survey of national statistical offices (NSOs) around the
world on statistical capacity assessment, the Partner-
ship in Statistics for Development in the 21
st
Century
(PARIS21) found that “while only 2% of assessed capa-
bilities target the individual, 32% of countries expressed
that individual capabilities need to be improved to rise
to new data ecosystem challenges” [23, p. 16]. As such,
care must be taken to ensure that the short-term de-
sire to populate the SDG indicators does not divert re-
sources away from the longer-term development of the
overall system capacity [18] or promote “the produc-
1014 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
tion of data and information at the expense of statistical
capacity” [23, p. 10]. Improving data skills and liter-
acy in the population is a key element in this long-term
human-centred approach to capacity building.
This more skills-oriented perspective on statistical
capacity development is a more central focus of the
Capacity Development 4.0 framework developed by
PARIS21. This framework acknowledges that “too little
investment in people and skills” [21, p. 19] has slowed
progress towards the so-called data revolution for de-
velopment [20], and thus aims to “go beyond the tradi-
tional production-side interventions to also include the
strengthening of data use, literacy and results” [21]. In
line with this recognition, we want to emphasise that
an important aspect of ensuring home-grown and long-
term capacity development requires connecting the big
picture measurement challenges of the SDGs with the
earlier stages of the statistical literacy pipeline. By fo-
cusing on interventions at the level of statistical edu-
cation, and by centring partnerships between statistical
stakeholders and the in-country educational system, we
can ensure that there is a strong and sustainable supply
of human capital with the necessary skills and compe-
tencies for working on the complex social challenges
represented by the SDGs.
3.2. What is data literacy?
The EmpoderaData project focuses on creating data
literate graduates ready to work on SDG problems. As
such, it is worth unpacking what we mean by data lit-
eracy and which skills and competencies this encom-
passes. This task is made trickier by the various ways in
which the term is deployed, and how these overlap with
other related terms such as numerical literacy, statistical
literacy and information literacy [25]. We therefore aim
to clarify here our understanding of data literacy and
why this term best reflects the skillset EmpoderaData
aims to develop.
The technical, analytical and conceptual aspects of
quantitative analysis have traditionally been considered
under the term statistical literacy, broadly understood
as a combination of “the ability to produce, analyse and
summarise detailed statistics in surveys and studies”
and “the ability to read and interpret summary statis-
tics in the everyday media: in graphs, tables, statements
and essays” [26, p. 135]. However, the demands of the
current digital age and the rise of more holistic data
science approaches to statistical work have led to an
increased emphasis on a broader set of competencies
for working with data which includes the “ability to
collect, manage, evaluate, and apply data, in a critical
manner” [27, p. 2]. As the amount of data permeating
our day-to-day lives has increased and the boundaries
between producers and users of data have been eroded,
critical data skills are now often seen as necessary parts
of the statistical curriculum, with the term ‘data liter-
acy’ increasingly being used to refer to this inclusive set
of skills [28]. The current set of data challenges we face,
including the daunting quantitative requirements of the
SDGs, require a holistic skill set which includes and
goes beyond understanding the basic formulas, princi-
ples and analytical aspects of statistical reasoning. In
order to both leverage the power and understand the
limits of the varied data sources which now permeate
our world, a kind of literacy is needed which includes
“a critical appreciation of data provenance and qual-
ity” [29, p. 12], “understanding issues of data privacy
and ownership” [28, p. 3], and “understanding how data
are stored” (ibid.). This is reflected in the UK Govern-
ment Data Task Force’s recent emphasis “[t]o make the
best use of data, we must have a wealth of data skills to
draw on. That means delivering the right skills through
our education system, but also ensuring that people can
continue to develop the data skills they need throughout
their lives [30].
These data skills are in addition to, rather than instead
of conventional statistical literacy skills. We therefore
follow Data-Pop Alliance [31] in understanding data
literacy as “the desire and ability to constructively en-
gage in society through or about data”, a broad defini-
tion which “interacts with and builds on [other related
literacies such as statistical and information literacies]
and requires a combination of the technical, critical,
quantitative and conceptual skills on which they are
based”. The conceptual, mechanical and critical aspects
of doing statistics with real data all remain important
elements of the skillset required to work on understand-
ing and addressing social challenges such as those rep-
resented by the SDGs [28,32,33]. However, the inclu-
sive notion of data literacy we have outlined here is
important for developing the next generation of social
statisticians to work on practical official statistics chal-
lenges as, unlike textbook data, “real data about soci-
ety are often more complex and messy” [32, p. 46]. If
learners are to use their statistical skills to work on SDG
problems, they need to be prepared to work with such
real-world data and develop the various data literacy
requirements that this entails. This is particularly im-
portant for ensuring that data sources which fall outside
the traditional sphere of official statistics are adequately
understood when these are utilised towards the SDGs.
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1015
Likewise, the ability to draw insights from such data
also requires conventional statistical training: the social
problems represented by the SDGs are highly complex,
and understanding them therefore “requires the ability
to explore, understand, and reason about complex mul-
tivariate data, because social phenomena do not happen
in a vacuum, and their understanding requires aware-
ness of how variables co-vary, or affect each other, or
are situated in a network of causal factors that may
change over time in manifold ways” [32, p. 45].
As the EmpoderaData model is premised on the ap-
plication of statistics in the workplace, we regard the
need to have the full set of data and statistical literacies
to provide an inclusive set of skills which will have
most benefit in strengthening the skills pipeline nation-
ally. As discussed above, the value of the Q-Step paid
internship model is that it exposes students not only to
classroom-based instruction in the basic statistical prin-
ciples and analytical techniques which underpin statis-
tical literacy, but also an appreciation for critical data
skills through hands-on experience working with real
data. A key challenge for statistics education is to ensure
that students develop more than just the ability to recall
and apply formulae and calculations from textbooks,
but also a higher-level understanding of the principles
of making sense of data. The experiential approach rep-
resented by EmpoderaData can help to bridge the gap
between the formulae learned and the conceptual un-
derstandings underpinning them by putting statistics
into the context of front-line social research [34]. In the
next section, we describe how EmpoderaData has set
about identifying the particular skills and data needs of
its partner countries in relation to the SDGs, and how
this feeds into the development of its data fellowship
model.
4. EmpoderaData pilot research
EmpoderaData Phase 1 aimed to establish an under-
standing of the following within Brazil, Colombia and
Mexico:
unmet needs in terms of data literacy skills;
the extent to which a data literacy fellowship
model might help to develop these skills;
the extent to which the SDGs are monitored, and
the SDG goal that might be relevant to work on
within each country’s context.
These three countries were chosen as pilots because
of their strong official statistical and academic systems
that could serve as a backbone for the “data revolution”,
their wide penetration of mobile and internet technol-
ogy, their established open data movement and their
active and vibrant civil societies.
The research methodology was qualitative and was
undertaken in three stages:
1.
May 2019: a workshop was held in São Paulo as
part of a ‘Big Data for the Common Good’ event.
Thirty participants, who were involved in data
literacy advocacy or policy-making, attended the
workshop. The participants represented a range of
sectors including civil society, academia, private
and public sector. The workshop aimed to (1) map
the needs for training, (2) identify partners for
future fellowship programs, and (3) present the
current University of Manchester Q-Step intern-
ship model and explore its applicability in Brazil,
Colombia and Mexico.
2.
June-July 2019: Eighteen qualitative interviews
were undertaken with stakeholders within Brazil,
Colombia and Mexico. A semi-structured inter-
view guide was used and interviews were con-
ducted online (Zoom) in either Portuguese, Span-
ish or English. Fifteen interviews focussed on the
data literacy needs and Q-Step internship model
(6 in Brazil, 5 in Mexico and 4 in Colombia).
The sample of interviewees was selected to repre-
sent a diversity of sectors (academia (private and
public), civil society and public sector), and as
much as possible provide a gender and regional
(within countries) representation. The interviews
were structured around (1) the availability of, and
need for, training in traditional quantitative skills
such as such as survey, census or official aggregate
data (2) the availability of, and need for, training
in data sciences/artificial intelligence, focussing
on big data/new sources of data and artificial in-
telligence analytics, i.e. programming, machine
learning, etc. (3) the interest in the adoption of
the Q-Step internship model. The three countries
are structured around pervasive inequalities in ac-
cess to education, employment, healthcare etc.
Therefore, the interviews included questions on
inequalities in the need for data literacy training
among sub-groups of the population (for example,
inequalities related to income, and gender).
The remaining three interviews focussed on un-
derstanding the progress of Brazil, Colombia and
Mexico in measuring progress towards the SDGs.
One interview was conducted with a relevant pro-
fessional working directly with the SDGs for the
government of each country. Interviewees were
1016 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
asked about (1) their impression of the national
monitoring and evaluation system re: SDGs in the
context of their country (2) which goal they think
it is relevant to work in the context of their coun-
try (3) key resources/sources for monitoring and
evaluation of the SDGs in their country.
3.
October 2019: a workshop was held at the Univer-
sity of Manchester in October 2019, to present and
discuss the preliminary findings with 31 invited
stakeholders and potential partners.
The full results from the research are reported in [35]
through the EmpoderaData project website
2
and the
main conclusions follow:
Data Literacy Training: There is a clear need for
more data literacy training across academia (particu-
larly undergraduates), the public sector and civil so-
ciety within all three countries. The biggest training
needed is for basic skills such as introductory statis-
tics, foundational data analysis, basic methodological
skills and also basic data science skills. This was high-
lighted in all three countries. Interviewees and attendees
at the workshops expressed that the end-goal is to foster
critical analyses of data, rather than the development
of pure mathematical competencies, in order to create
leadership that can think critically about data.
Hybrid Fellowship Models: In all three countries,
paid data fellowship models were acknowledged as a
useful intervention. The participants in the research
recommended that the target audience for fellowships
should be students and potentially young profession-
als. However, different adaptations of the Q-Step model
were suggested for each country; it is very important
that the context of the country is considered when de-
veloping a fellowship model. Another recommenda-
tion was that a training curriculum should be based on
non-proprietary software to ensure sustainability and
facilitate access.
A key recommendation from the research was the
need to foster ‘hybrid’ professionals that can under-
stand, use and analyse data for social science purposes,
i.e. evidence-informed policy, journalism, activism etc.
A hybrid model would bring together people with com-
plementary backgrounds (data science and social sci-
ence) to work together at the host organization, on one
specific sustainable development challenge. It was felt
that this would lead to a truly critical analysis of data
and would have a higher impact in terms of measur-
ing achievement of the sustainable development goals.
2https://datapopalliance.org/empoderadata-project/.
Since this research was undertaken, the field of com-
putational social science has rapidly emerged and this
may be an alternative to the hybrid model, with com-
putational social science lying at the intersection of the
social sciences, statistics and computer science [36,37].
Sustainable Development Goals: The research ex-
plored which SDG(s) might be used within each coun-
try to base the data literacy training around. The out-
come was that training should not be restricted to a
particular SDG within a country; the SDG content of
the training should remain flexible to incorporate the
host organizations’ interests, as well as sectoral funding
opportunities within each country. However, Colombia
and Brazil interviewees suggested offering fellowships
to students from poor backgrounds who were in need of
a stable source of income. A specific outreach strategy
to involve typically excluded subgroups may, therefore,
generate an impact in terms of SDGs 10 (reduced in-
equalities) and 4 (quality education). However, it was
noted that, paradoxically, the individuals would also
need to have an acceptable educational background at
entry level. Therefore, there is a need to find the balance
to avoid the risk that a fellowship program would end up
working with those with more privileged backgrounds.
In summary, the main conclusions from Empodera-
Data Phase 1 were: (1) the most requested data literacy
training need is for basic skills (2) paid data fellow-
ship models are acknowledged as a useful intervention;
and (3) the notion of a ‘hybrid’ professional to build
data literacy capacities for ‘social science’ purposes
provides a practical way forward. The full results from
Phase 1 [35] were used to inform and design Empodera-
Data’s second phase the implementation of the Q-Step
model in Colombia and Brazil.
5. Implementing the data fellowship model in
Colombia and Brazil
Building on what was learned in the EmpoderaData
pilot research, the next steps for the project were to
build the necessary partnerships to develop and deploy
a data fellowship programme in the region. Two par-
allel projects emerged from the São Paulo workshop
to this end; the first led by Universidad del Rosario in
Bogotá, Colombia, and the second at FGV Business
School in São Paolo, Brazil. In order to ensure that the
data fellowship model serves the needs of the particular
country in which it is implemented, the process must be
led by partners within those countries. Local partners
are uniquely positioned to understand both the needs of
their societies and the challenges and opportunities for
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1017
achieving the most transformative impact in the local
statistics education pipeline. In this section, we report
on how these two parallel initiatives will work and how
we will learn from them in designing future regional
interventions.
5.1. Data fellowships in Colombia
Here we describe the actions in Colombia that aim
at strengthening statistical capacities, the mobilization
of knowledge (quantitative) and the impact of knowl-
edge transfer in the communities. The Universidad del
Rosario, a 367-year-old Colombian higher education
institution, materializes its academic commitment to the
common good through high-level educational offerings
and impact in the country. The first college mathematics
course was held at this university several centuries ago
by José Celestino Mutis, who also led the first botani-
cal survey of the country. That commitment to science
continues today not only through academic programs in
the context of mathematics and science but also through
a dynamic curriculum, in courses of study such as the
social sciences. That includes academic products for
the strengthening of quantitative competencies for the
resolution of complex problems (which requires inter-
disciplinary and diverse reading), such as hackathons,
seminars, and collaborative projects.
This action that we refer to above does not work
solely with numerical expertise. It is undoubtedly nec-
essary to possess a social sensitivity that allows, among
other things, engaging with the communities for the de-
tection of social problems that can potentially be solved
by whoever is learning.
This idea materializes on two fronts. The first is of-
fering interesting courses that allow students to gener-
ate discussions mobilising what is typical in their dis-
ciplines but with a discourse permeated by mathemat-
ics, approaching what is typical of critical mathemat-
ics [38]. The second is a type of living laboratories,
called the mINNga Labs [39], due to their closeness
to the spirit of the indigenous mingas, as certain An-
dean communities called collective agricultural work
carried out for the benefit of the tribe. “We understand
living laboratories to be an innovation model where all
the actors actively participate appropriating innovation
tasks, clearly open and collaborative, in scenarios of
co-creation and validation of solutions that they need
themselves, in real life contexts, using, to a large extent,
ICT as a medium, thus forming a research and innova-
tion ecosystem that permanently and explicitly enables
social innovation” [39, p. 255].
These laboratories are governed by five fundamental
principles:
1.
Continuity: Building trust and exploring an ap-
proach that leads to efficient, long-term sustain-
able innovations, involving users rather than re-
stricting them [40].
2.
Openness: Allowing the presence of multiple per-
spectives of thought, in user-driven research sce-
narios.
3.
Realism: Providing solutions to real problems in
the daily lives of those concerned.
4.
Empowerment of users: This takes into account
(and builds from) human needs and desires. The
social dynamics of the living laboratories ap-
proach guarantees the rapid propagation of inno-
vative solutions (viral adaptation) through social-
emotional intelligence mechanisms.
5.
Spontaneity: Allowing flexible and robust models.
5.2. The FGV Q-Step initiative
After the 2019 EmpoderaData workshop in São
Paulo, a project to implement the Q-Step model at FGV
Business School was submitted to a Brazilian funding
agency (FAPESP). In collaboration with The Univer-
sity of Manchester, the two-year project, beginning in
March 2021, is described below.
The Q-Step Center at FGV aims to develop and im-
prove the quantitative skills of undergraduate Social
Science students and increase general interest in quanti-
tative research among students. We expect the program
to encourage students to work in quantitative careers
after graduating and to apply to undertake quantitative
studies in postgraduate courses.
The program consists of basic disciplines, already in
the curriculum, internships and advanced disciplines.
The internships and the advanced disciplines will be
offered in the following fields: public administration,
finance, marketing, accounting and data science. Other
fields may be offered, depending on student demand.
Students will be selected from two undergraduate pro-
grammes at FGV-EAESP: Business Administration and
Public Administration.
Students from the second year can apply for the Q-
Step program and they must be approved in basic quan-
titative disciplines in the first year (Mathematics 1 and
2, Statistics 1, Information Technology and Program-
ming Logic). Half of the students who apply for the
program will be randomly selected to participate in the
program. These students will participate in internships
offered by the university in partnership with companies.
As we select the students randomly, among those inter-
ested, we are able to compare those who participate in
1018 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
the Q-Step program with those that do not and in terms
of quantitative skills, and, thus, assess the efficacy of
the program.
As part of the curriculum, FGV undergraduate busi-
ness students must participate in two “immersions” or
internship activities. These week-long “immersions”
take place twice a year (April and October). The stu-
dents have no classes during this period, but spend one
week at a company developing a project or idea. As
a result, they present a proposal to the company team
and then are evaluated. These “immersions” range from
trips to “favelas” to developing ideas for e-commerce
companies. For Q-step participants, new immersions
that require and develop quantitative skills will be of-
fered and students can choose among these to complete
Q-Step credit requirements.
Besides immersions, students can take summer in-
ternships, lasting from 3 to 4 weeks, to be developed
by the Q-Step coordinator at FGV, in partnership with
companies, and in consultation with the Q-Step intern-
ships lead from the University of Manchester.
Besides the basic disciplines (Statistics, Mathemat-
ics, Introduction to IT), FGV offers more than 60 elec-
tive disciplines, some of which are quantitative. In addi-
tion, as part of the Q-Step program, FGV will elaborate
advanced quantitative-driven disciplines specifically to
the Q-Step program. The Q-Step participant will have
to complete at least one of these advanced disciplines.
The learning outcomes of the FGV Q-Step program
are:
Improve quantitative skills of the students; this
will be measured by summative tests applied at the
end of the program. We expect that the selected
students of the Q-step program will score higher
on these tests than the ones that were not selected;
Increase the use of quantitative approaches and
methods in students’ works and research; it will
be measured by a survey of both selected and not
selected students;
Increase the interest in using quantitative meth-
ods after graduating (in post-graduate courses or
workplace); this will be measured by a survey of
both selected and not selected students.
Some of these outcomes are aligned with the ones
of the undergraduate program and are described in the
pedagogic project. FGV is one of the few Latin Amer-
ican schools that participates in AACSB (Associate
of Advanced Collegiate School of Business) and must
evaluate whether the students are achieving the learning
goals through the Assurance of Learning” system. At
different points of the course, students are evaluated in
respect to the learning objectives of the course.
5.3. Collaborative work to materialize the alliance
with Latin America
Having said all the above, we wonder how to con-
tinue materializing strategies that allow strengthening
inter-institutional alliance and thus generating real and
achievable actions within the framework of learning
quantitative skills for solving social problems, through
learning-by-doing in the business sector, but always
aligned with the SDGs.
These initiatives should nurture the development of
global citizen competencies in our students and guide
strategies in the Internationalization of the Curriculum.
Through these competencies, the aim is for students
to be able, for example, to interact and communicate
effectively in another language with people from other
cultures and countries, to think globally and consider
issues from different cultural perspectives and to lead
work teams in culturally diverse environments, but,
particularly, focusing on the mobilization of quantitative
knowledge for the solution of real problems.
An optimal outcome is the Collaborative Online In-
ternational Learning (COIL), as a privileged pedagogi-
cal setting where internationalization goals are actively
developed in the teaching-learning process. This strat-
egy is aligned with the learning to learn, which artic-
ulates the development of disciplinary contents with
international virtual educational environments to enrich
the training of students through collaborative and mean-
ingful learning with students from different cultural
and linguistic contexts. More specifically, we propose a
COIL-type subject, which links students with different
cultural and geographical experiences, sensitising them
to the global world and deepening their understanding
of themselves, their culture, how they are perceived
by others and how they perceive the “others”. A good
COIL experience design engages students in learning
the subject matter through their own cultural perspec-
tive and by exchanging their cultural and experiential
perspective as they move through learning with interna-
tional students. This scenario will give many students
the opportunity to have an international experience and
to develop linguistic and digital competences that are
very valuable in a global and interconnected world.
The subject that we propose to work collaboratively
in takes into account the germinal aspiration of respond-
ing to the SDGs. In particular, we propose, through a
shared subject (with the use of COIL methodologies),
to develop statistical competencies in the exercise of
problem solving within the framework of SDGs. This
subject will be based on experiential learning, with the
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1019
particularity that the problems that will be solved in
class will correspond to real problems of the commu-
nities, using living laboratories. In this way, it will not
only allow the construction of mathematical knowledge,
but it will be developed from social justice and critical
thinking.
6. Conclusions
The paper draws on the combined strengths of the
data fellows training programme (delivered through the
Q-Step internships) and the commitment to delivering
on the SDGs, demonstrated by the University of Manch-
ester in the UK. We have explored whether the Q-Step
internship model has relevance and usefulness within
the context of Brazil, Colombia and Mexico. We have
outlined the strategy, progress and emerging challenge
of two different approaches that have been developed
via partnerships between the University of Manchester
and the Universidad del Rosario in Bogotá, Colombia
and FGV in São Paolo, Brazil.
There is no doubt that, within Brazil and Colombia,
the data fellowship model is perceived as a tool for
building statistical capacity to help deliver the UN’s
SDGs. The results from the EmpoderaData project gives
a very clear narrative that a data fellowship model can
be flexibly adapted to different disciplines/subjects (tra-
ditional social science, business studies and mathemat-
ics), within different country contexts and with different
curriculum design. For example, the Brazilian interven-
tion focuses on an immersion-based programme similar
to that of the Q-Step model, while the Colombian ini-
tiative focuses on bringing real practical social research
projects into the university classroom. The project also
gives support to MacFeely and Barnat’s argument of ca-
pacity building being “most effective when it is home-
grown, long-term in perspective and managed collec-
tively by those who stand to benefit” [18, p. 897].
We have identified some key challenges to build-
ing a successful data fellowship model within Brazil
and Colombia. The first is that there are SDG data
system/infrastructure deficits that restrict the capacity
of data fellows to work with real data to measure the
SDGs. The second challenge is the pervasive inequality
in education among subgroups of the population. To
overcome these inequalities, efforts must be made to
target the recruitment of marginalised sub-groups (such
as women, those with lower incomes and those in rural
communities) into a data fellowship programme [41].
The early data fellowship initiatives within the Uni-
versidad del Rosario and FGV are a positive step for-
ward in striving to build statistical capacity to help de-
liver the UN’s SDGs. As these initiatives develop fur-
ther, they will be monitored and evaluated to identify the
successes and the improvements needed to strengthen
the data fellowship model and work further towards the
development of quantitative skills pipelines in Colom-
bia and Brazil.
Acknowledgments
The authors would like to thank Julie Ricard,
Valentina Casasbeunas and Emmanuel Letouzé from
Data-Pop Alliance for their collaboration on Empoder-
aData Phase 1. We would also like to thank the funders
of the Q-Step programme (The Nuffield Foundation and
the Economic and Social Research Council) and the
University of Manchester who collectively enabled the
Q-Step Centre data fellows programme to be developed
and grow.
References
[1]
United Nations. Transforming our world: The 2030 agenda for
Sustainable development [Internet]. Sustainable Development
GOALS Knowledge Plataform; 2015 [cited 2021 May 20]
p. 41. Report No.: A/RES/70/1. Available from: https://sustain
abledevelopment.un.org/content/documents/21252030%20Ag
enda%20for%20Sustainable%20Development%20web.pdf.
[2]
Pfeiffer A, Middeke F, Tambour M. 2030 agenda for sustain-
able development: implications for official statistics. Stat J
IAOS [Internet]. 2017 [cited 2021 May 20]; 33(4): 911–8.
Available from: doi: 10.3233/SJI-170360.
[3]
Nuffield Foundation, Economic and Social Research Council
(ESRC), Higher Education Funding Council for England
(HEFCE). Aims and activities of the Q-Step centres [Internet].
2014 [cited 2021 May 20]. Available from: https://www.nuffie
ldfoundation.org/sites/default/files/files/Aims%20and%20Acti
vities%20of%20the%20Q-Step%20Centres(1).pdf.
[4]
Carter J, Noble S, Russell A, Swanson E. Developing statistical
literacy using real-world data: investigating socioeconomic
secondary data resources used in research and teaching. Int J
Res Method Educ [Internet]. 2011 [cited 2021 May 20]; 34(3):
223–40. Available from: doi: 10.1080/1743727X.2011.6095
53.
[5]
Wathan J, Brown M, Williamson L. Increasing Secondary
Analysis in Undergraduate Dissertations: A Pilot Project. In:
Teaching Quantitative Methods: Getting the Basics Right [In-
ternet]. London: SAGE Publications Ltd; 2011 [cited 2021
May 20]. pp. 121–41. Available from: doi: 10.4135/9781446
268384.
[6]
The University of Manchester. Enriching Social Science
Teaching with Empirical Data (ESSTED) [Internet]. ESSTED.
2013 [cited 2021 May 20]. Available from: https://sites.manch
ester.ac.uk/essted/.
[7]
Buckley J, Brown M, Thomson S, Olsen W, Carter J. Embed-
ding quantitative skills into the social science curriculum: case
1020 J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America
studies from Manchester. Int J Soc Res Methodol [Internet].
2015 Sep 3 [cited 2021 May 20]; 18(5): 495–510. Available
from: doi: 10.1080/13645579.2015.1062624.
[8]
Carter J. Work Placements, Internships & Applied Social Re-
search [Internet]. 1st ed. United Kingdom: SAGE Publications
Ltd; 2021 [cited 2021 May 20]. 304. Available from: https://
uk.sagepub.com/en-gb/eur/work-placements-internships-app
lied-social-research/book253221.
[9]
The University of Manchester. The University of Manchester
and the Sustainable Development Goals [Internet]. 2019 [cited
2021 May 20]. Available from: https://documents.manchester.
ac.uk/display.aspx?DocID=43121.
[10]
THE World University Ranking. Impact Ranking 2021 [Inter-
net]. Times Higher Education (THE). 2021 [cited 2021 May
20]. Available from: https://www.timeshighereducation.com/
impactrankings.
[11]
Evans N. Open Data Watch: Bridging the Gap [Internet]. [cited
2021 May 20]. Available from: https://documents.manchester.
ac.uk/display.aspx?DocID=46665.
[12]
Open Data Watch, Data2X. Bridging the Gap: Mapping Gender
Data Availability in Africa. [Internet]. 2019 [cited 2021 May
20] p. 49. Available from: https://data2x.org/wp-content/uploa
ds/2019/06/Bridging-the-Gap-Technical-Report-Web-Ready.
pdf.
[13]
Gennari P, Kalamvrezos Navarro D. Are We Serious About
Achieving the SDGs? A Statistician’s Perspective. ISSD: SDG
Knowledge Hub [Internet]. 2020 [cited 2021 May 20]; Avail-
able from: https://sdg.iisd.org:443/commentary/guest-articles/
are-we-serious-about-achieving-the-sdgs-a-statisticians-pers
pective/.
[14]
MacFeely S. Measuring the sustainable development goal in-
dicators: an unprecedented statistical challenge. J Off Stat [In-
ternet]. 2020 [cited 2021 May 20]; 36(2): 361–78. Available
from: doi: 10.2478/jos-2020-0019.
[15]
MacFeely S, Nastav B. “You say you want a [data] revolution”:
a proposal to use unofficial statistics for the SDG global indi-
cator framework. Stat J IAOS [Internet]. 2019 [cited 2021 May
20]; 35(3): 309–27. Available from: doi: 10.3233/SJI-180486.
[16]
Moyer JD, Hedden S. Are we on the right path to achieve the
sustainable development goals? World Dev [Internet]. 2020
Mar 1 [cited 2021 May 20]; 127: 104749. Available from: doi:
10.1016/j.worlddev.2019.104749.
[17]
Editorial. Time to revise the Sustainable Development Goals.
Nature [Internet]. 2020 Jul 14 [cited 2021 May 20]; 583(7816):
331–2. Available from: https://www.nature.com/articles/d41
586-020-02002-3.
[18]
MacFeely S, Barnat N. Statistical capacity building for sus-
tainable development: developing the fundamental pillars nec-
essary for modern national statistical systems. Stat J IAOS
[Internet]. 2017 Nov 24 [cited 2021 May 20]; 33(4): 895–909.
Available from: doi: 10.3233/SJI-160331.
[19]
Dang H-AH, Serajuddin U. Tracking the sustainable develop-
ment goals: emerging measurement challenges and further re-
flections. World Dev [Internet]. 2020 [cited 2021 May 20]; 127:
104570. Available from: doi: 10.1016/j.worlddev.2019.05.024.
[20]
United Nations Secretary General’s Independent Expert Advi-
sory Group on a Data Revolution for Sustainable Development
(IEAG). A World that Counts: Mobilising the Data Revolution
for Sustainable Development [Internet]. United Nations; 2014
[cited 2021 May 20]. Available from: https://repositorio.cepal.
org/bitstream/handle/11362/40319/AWorldThatCounts.pdf?se
quence=1&isAllowed=y.
[21]
PARIS21. A road map for a country-led data revolution [Inter-
net]. OECD; 2015 [cited 2021 May 20]. 52 p. Available from:
doi: 10.1787/9789264235250-fr.
[22]
Stuart E, Samman E. The data revolution: finding the missing
millions [Internet]. odi.org. 2015 [cited 2021 May 20]. Avail-
able from: https://odi.org/en/publications/the-data-revolution-
finding-the-missing-millions/.
[23]
PARIS21. Statistical Capacity Development Outlook 2019
[Internet]. Paris: PARIS21; 2019 [cited 2021 May 18] p. 75.
Available from: https://paris21.org/flagship/2019.
[24]
Keijzer N, Klingebiel S. Realising the Data Revolution for
Sustainable Development: Towards Capacity Development 4.0.
In: SSRN [Internet]. 2017 [cited 2021 May 20]. pp. 1–27.
Available from: doi: 10.2139/ssrn.2943055.
[25]
Gal Iddo, Ograjenšek Irena. Official Statistics and Statis-
tics Education: Bridging the Gap. J Off Stat [Internet]. 2017
[cited 2021 May 20]; 33(1): 79–100. Available from: doi:
10.1515/jos-2017-0005.
[26]
Schield M. Chapter 11: Assessing Statistical Literacy: Take
CARE. In: Bidgood P, Hunt N, Jolliffe F, eds. Assessment
Methods in Statistical Education: An International Perspec-
tive [Internet]. Chichester, UK: John Wiley & Sons, Ltd;
2010 [cited 2021 May 20]. pp. 133–52. Available from: doi:
10.1002/9780470710470.ch11.
[27]
Ridsdale C, Rothwell J, Smit M, Ali-Hassan H, Bliemel M,
Irvine D, et al. Strategies and Best Practices for Data Liter-
acy Education: Knowledge Synthesis Report [Internet]. Dal-
house University; 2015 [cited 2021 May 20]. Available from:
https://DalSpace.library.dal.ca//handle/10222/64578.
[28]
Gould R. Data literacy is statistical literacy. Stat Educ Res J
[Internet]. 2017 [cited 2021 May 20]; 16(1): 17–21. Available
from: http://iase-web.org/documents/SERJ/SERJ16(1)_Grant.
pdf?1498680907.
[29]
Ridgway J, Nicholson J, McCusker S. “Open Data” and the se-
mantic web require a rethink on statistics teaching. Technol In-
nov Stat Educ [Internet]. 2013 [cited 2021 May 20]; 7(2): 1–12.
Available from: https://escholarship.org/uc/item/6gm8p12m.
[30]
United Kingdom Government: Department for Digital, Cul-
ture, Media & Sport. Policy Paper: National Data Strategy [In-
ternet]. GOV.UK. 2020 [cited 2021 May 20]. Available from:
https://www.gov.uk/government/publications/uk-national-da
ta-strategy/national-data-strategy.
[31]
Data Pop Alliance. Beyond Data Literacy: Reinventing Com-
munity Engagement and Empowerment in the Age of Data
[Internet]. White Paper; 2015 [cited 2021 May 20]. Available
from: http://datapopalliance.org/wp-content/uploads/2015/10/
BeyondDataLiteracy_DataPopAlliance_Sept30.pdf.
[32]
Engel J. Statistical literacy for active citizenship: a call for data
science education. Stat Educ Res J [Internet]. 2017 [cited 2021
May 20]; 16(1): 44–9. Available from: http://ez.urosario.edu.
co/login?url=http://search.ebscohost.com/login.aspx?direct=
true&db=edselc&AN=edselc.2-52.0-85021060913&lang=es
&site=eds-live&scope=site.
[33]
Prodromou T, Dunne T. Statistical literacy in data revolution
era: building blocks and instructional dilemmas. Stat Educ Res
J [Internet]. 2017 [cited 2021 May 20]; 16(1): 38–43. Available
from: http://ez.urosario.edu.co/login?url=http://search.ebscoh
ost.com/login.aspx?direct=true&db=edselc&AN=edselc.2-
52.0-85021077944&lang=es&site=eds-live&scope=site.
[34]
Carter J, Brown M, Simpson K. From the classroom to the
workplace: how social science students are learning to do data
analysis for real. Stat Educ Res J [Internet]. 2017 May 1 [cited
2021 May 20]; 16(1): 80–101. Available from: http://ez.urosa
rio.edu.co/login?url=http://search.ebscohost.com/login.aspx?
J. Carter et al. / EmpoderaData: Sharing a successful work-placement data skills training model within Latin America 1021
direct=true&db=eric&AN=EJ1152506&lang=es&site=eds-
live&scope=site.
[35]
Higgins V, Casasbuenas V, Ricard J, Carter J. Empoderata
Data Literacy Assesment and Sustainable Development
Goals Data Gaps. Brazil, Colombia and Mexico [Internet].
University of Manchester and Data-Pop Alliance; 2019 [cited
2021 May 20]. Available from: https://datapopalliance.org/wp-
content/uploads/2020/09/EMPODERADATAREPORT_final_
oct2019.pdf.
[36]
Lazer DMJ, Pentland A, Watts DJ, Aral S, Athey S, Contrac-
tor N, et al. Computational social science: obstacles and op-
portunities. Science [Internet]. 2020 Aug 28 [cited 2021 May
20]; 369(6507): 1060–2. Available from: doi: 10.1126/science.
aaz8170.
[37]
Edelmann A, Wolff T, Montagne D, Bail CA. Computational
social science and sociology. Annu Rev Sociol [Internet]. 2020
Jan [cited 2021 May 20]; 46(1): 61–81. Available from: doi:
10.1146/annurev-soc-121919-054621.
[38]
Gates P, ed. 18. Critical Mathematics Education. In: Issues in
mathematics teaching. 1st ed. London; New York: Routledge;
2001.
[39]
Méndez-Romero RA, Gauthier-Umaña V. mINNga Labs: una
innovación pedagógica-tecnológica para Colombia. In: En las
regiones de Colombia. La Universidad del Rosario piensa el
pais. Bogotá: Universidad del Rosario; 2021. pp. 92–107.
[40]
Liedtke C, Jolanta Welfens M, Rohn H, Nordmann J. LIVING
LAB: user-driven innovation for sustainability. Int J Sustain
High Educ [Internet]. 2012 Jan 1 [cited 2021 May 20]; 13(2):
106–18. Available from: doi: 10.1108/14676371211211809.
[41]
Jones P, Carter J, Renken J, Arbeláez Tobón M. Strengthen-
ing the Skills Pipeline for Statistical Capacity Development to
Meet the Demands of Sustainable Development: Implementing
a Data Fellowship Model in Colombia [Internet]. University
of Manchester: Centre for Digital Development Global Devel-
opment Institute, SEED; 2021 [cited 2021 May 20]. Available
from: https://www.gdi.manchester.ac.uk/research/publications
/di/di-wp89/.
... A lack of quantitative data skills among social scientists in the UK has been recognised for over twenty years by government, businesses and research funders (Carter et al, 2021c;MacInnes, 2009;UK DCMS Policy, 2021). The importance of this has become more apparent during the coronavirus pandemic when data literacy skills have been critical for research capabilities. ...
... Moreover, we focus here on the use of quantitative data and analysis, and the Data Fellows programme is specifically aimed at increasing skills in this area to enable students to critically evaluate and use numerical data (usually but not always statistical data) through data-driven research projects. For further information about how we apply this to the Sustainable Development Goals context see Carter et al (2021c). ...
... nterested in developing the model in their own countries. Through a collaboration with DataPop Alliance, that resulted from a Data Fellow being placed with an organisation they work closely with (Open Data Watch), we have been able to develop an international dimension to the initiative. We describe the origins and early stages of this research in Carter et. al (2021c) where we discuss the EmpoderaData project which has explored the transferability of the data fellows scheme to Colombia, Mexico and Brazil. The full results from the early stages of the EmpoderaData project are available in Higgins et al (2019). ...
Article
Full-text available
This paper describes two successful approaches to quantitative data literacy training within the UK and the synergies and collaborations between these two programmes. The first is a data literacy training programme, being delivered by the UK Data Service, which focuses on training in basic data literacy skills. The second is a Data Fellows programme that has been developed to help undergraduate social science students gain real-world experience by applying their classroom skills in the workplace. The paper also discusses next steps in the global development of data literacy skills via the EmpoderaData project, which is trialling the Data Fellows programme in Latin America.
... In particular, in Colombia and Brazil, there was a very keen interest in adopting the data fellows model to build the statistical and data literacy capacity to help deliver the SDGs. Furthermore, a key recommendation from the research was the notion of a hybrid model that would bring together data fellows with complementary backgrounds (such as social scientists and STEM students) to work collaboratively on SDG-related challenges [33,34]. This finding-that hybrid teams are required to conduct rigorous statistical analyses informed by strong subject expertise-is at the heart of what we propose in this paper. ...
... As a result of phase 1 of the EmpoderaData project, two parallel in-country projects emerged in Colombia (led by the Universidad del Rosario in Bogotá) and Brazil (led by the FGV Business School in São Paolo). This second phase notes "the results from the EmpoderaData project give a very clear narrative that a data fellowship model can be flexibly adapted to different disciplines or subjects (traditional social science, business studies and mathematics), within different country contexts and with different curriculum designs" [34] (p. 1019). ...
Article
Full-text available
The aim of this paper is first to examine, through a qualitative analysis of statistics syllabi, the current state of statistical education in a sample of universities in Colombia. The focus is on statistics teaching in degrees for economics and business administration students. The results from the qualitative analysis reflect a preponderance of traditional and didactic teaching methods centered on the teacher, not on the student. The second aim is to present findings from a case study that has developed an innovative pedagogical intervention, called a data fellows program, from the University of Manchester, United Kingdom, which evidences opportunities for how statistics can be taught effectively to non-STEM majors. Further, the data fellows model has also been explored in the context of developing statistical and data skills capacities in Latin America. We reflect on how the lessons from the UK case study could open up opportunities for rethinking the teaching of statistics in Colombia through developing data projects and experiential learning to practice statistics in the real world.
... The data fellows programme described is set firmly against the backdrop of a global need for data and statistical skills development. The most obvious example of this is the UN Sustainable Development Goals 2 (SDGs), which have established the need for a data literate global citizenship, and we report elsewhere on how we are exploring the potential of the data fellows model to develop skills capacity to deliver on the SDGs [15]. ...
... Blog posts are often popular with organisations to help students share their learning, and for the organization to provide a platform for dissemination of the work covered. An example 15 where the data fellow (studying for a sociology and quantitative methods degree) talks about her work on the development of the updated Carstairs Index of Deprivation based on the (then) latest available census data, provides an example of where a student intern used official statistics. She went on to develop this methodology further in her return to her final year, using it to form the basis of her third-year dissertation topic, and continued to pursue this in her Master's thesis. ...
Article
Full-text available
This paper presents an innovative model for developing data and statistical literacy in the undergraduate population through an experiential learning model developed in the UK. The national Q-Step (Quantitative Step change) programme (2013–2021) aimed to (i) create a step change in teaching undergraduate social science students quantitative research skills, and (ii) develop a talent pipeline for future careers in applied social research. We focus on a model developed at the University of Manchester, which has created paid work placement projects in industry, for students to practise their data and statistical skills in the workplace. We call these students data fellows. Our findings have informed the development of the undergraduate curriculum and enabled reflection on the skills and software that we teach. Data fellows are graduating into careers in fields that would previously have been difficult to enter without a STEM (Science, Technology, Engineering and Mathematics) degree. 70% of data fellows to date are female, with 25% from disadvantaged backgrounds or under-represented groups. Hence the programme also addresses equality and diversity. The paper documents some of the successes and challenges of the programme and shares insight into non-STEM pipelines into social research careers that require data and statistical literacy, A major advantage of our approach is the development of hybrid data analysts, who are able to bring social science subject expertise to their research as well as data and statistical skills. Focusing on the value of experiential learning to develop quantitative research skills in professional environments, we provoke a discussion about how this activity could not only be sustained but also scaled up.
... Las instituciones públicas deben ser efectivas en la atención integral al ciudadano para lo cual deben tener las habilidades necesarias para su desenvolvimiento diario (Nankya-Mutyoba et al., 2022), por lo que se debe capacitar a todo los agentes necesarios (Estrada Molina et al., 2015) no solo en habilidades básicas sino también en habilidades de gestión y muy importante el manejo de nuevas tecnologías de información (Carter et al., 2021). Ya que las capacidades humanas que impulsan los resultados organizacionales están siendo reconocidos y las organizaciones gastan cada vez más en mejorar las Capacidades Humanas Intrínsecas de sus empleados, Teniendo en cuenta la singularidad de cada individuo (Jain & Singh, 2019). ...
Article
La investigación tuvo como objetivo realizar una revisión de la literatura en bases de datos de revistas indexadas sobre la capacitación en el sector público de Perú en el período 2020-2022. La metodología empleada fue la revisión sistemática y las bases de datos consultados fueron Scopus y Scielo. Se aplicó la guía Prisma para la selección de estudios y Strobe para evaluar la calidad editorial y metodológica. La búsqueda se realizó utilizando palabras relacionadas al tema de investigación, entre ellas; “capacitación en el sector público” “gestión de la capacitación”. En la búsqueda inicial considerando la palabra “training in the public sector” se identificaron 7628 artículos científicos de los cuales se tamizaron 817 artículos que incluyeron la variable capacitación en el sector público en título o resumen, quedando excluidos 6758 artículos. Con base en la aplicación de criterios de inclusión y exclusión se seleccionaron 150 artículos que guardan relación directa con el tema de investigación y finalmente se meta-analizaron 57 publicaciones científicas. Los hallazgos de la revisión sistemática de los artículos seleccionados en las diferentes bases de datos, evidencian que todos los autores coinciden sobre la importancia de la gestión de la capacitación en el sector público. La gestión de la capacitación debe ser una política institucional de las entidades públicas. La gestión de la capacitación debe ser implementada y diseñada por cada una de las entidades y esta debe de gestionarse en funciones de los niveles y de los cargos, así como de las funciones que realiza el servidor dentro de la entidad.
... Another example is the "EmpoderaData Project", a transnational collaboration among the University of Manchester (UK), Fundação Getulio Vargas (Brazil), Universidad del Rosario (Colombia) and Data-Pop Alliance (the USA and France). This project enables undergraduate social science students to practise data skills through workplace immersion, improving the quality of statistical education at the national level and supporting the skills needed to deliver the SDGs (Carter et al., 2021). Table 1 summarises the mentioned cases and adds others. ...
Purpose – Higher education institutions (HEIs) around the world are engaged in internationalisation efforts. Yet internationalisation per se is associated with significant pressures on the environment and environmental resources, which need to be addressed. Therefore, this study assessed the opportunities, benefits, and challenges associated with the internationalisation of universities at a global level. Design/methodology/approach – A total of 27 relevant case studies were extracted from the literature to illustrate how HEIs worldwide are ensuring sustainability in their internationalisation efforts. Findings – Through case studies of international HEIs, the study lists the opportunities, benefits, and challenges associated with the internationalisation of universities at a global level and some of the measures that may be deployed to reduce the environmental impacts of their international activities. Originality/value - This study provides a welcome contribution to the literature since it outlines some of the works taking place at universities, where matters related to sustainable development are considered against a background of internationalisation efforts.
Article
Full-text available
The UN Sustainable Development Goals (SDGs) create a global need to deliver on an agenda which relies heavily on quantitative data. This creates a demand for human capital with the requisite statistical and data skills to work on the challenges represented by the SDGs. Increasingly, long-term domestic solutions to the building of statistical capacities are being sought, in order to decrease the dependency on external support and ensure a sustainable flow of qualified professionals. In this paper we argue for the value of thinking of statistical capacity as a pipeline which needs to begin in the education system, and illustrate how investment in this end of the pipeline can deliver a more sustainable and long-term pathway to building up the holistic skills-base to enable the challenges of the SDGs to be tackled. We describe an existing triedand-tested experiential learning model called Q-Step based on developing skills in the workplace, and propose that the successes of this partnership-driven model illustrate how ‘data fellowships’ can fulfil some of the unmet capacity needs of the data revolution for sustainable development. We illustrate our argument through a practical exploration of the development of such a learning model in Colombia. Although there are challenges in ensuring that educational access is equal, we demonstrate that there are significant opportunities and a data fellowships model opens doors for addressing local skills gaps to help deliver the SDGs.
Article
Full-text available
Data sharing, research ethics, and incentives must improve
Article
Full-text available
In March 2017, the United Nations (UN) Statistical Commission adopted a measurement framework for the UN Agenda 2030 for Sustainable Development, comprising of 232 indicators designed to measure the 17 Sustainable Development Goals (SDGs) and their respective 169 targets. The scope of this measurement framework is so ambitious it led Mogens Lykketoft, President of the seventieth session of the UN General Assembly, to describe it as an ‘unprecedented statistical challenge’. Naturally, with a programme of this magnitude, there will be foreseen and unforeseen challenges and consequences. This article outlines some of the key differences between the Millennium Development Goals and the SDGs, before detailing some of the measurement challenges involved in compiling the SDG indicators, and examines some of the unanticipated consequences arising from the mechanisms put in place to measure progress from a broad political economy perspective.
Article
Full-text available
The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: ( a) social network analysis and group formation; ( b) collective behavior and political sociology; ( c) the sociology of knowledge; ( d) cultural sociology, social psychology, and emotions; ( e) the production of culture; ( f ) economic sociology and organizations; and ( g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology. Expected final online publication date for the Annual Review of Sociology, Volume 46 is July 30, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Article
Full-text available
The Sustainable Development Goals (SDGs) call upon all countries to achieve 17 broad development goals by 2030. The SDGs are a central component of many national development plans and foreign aid strategies. While the SDGs have become a central aspect of development planning, how achievable are they under present conditions? This paper explores a dynamic “middle-of-the-road” baseline global development scenario (Shared Socio-economic Pathway 2) using an integrated assessment model (International Futures) to evaluate progress toward target values on nine indicators related to six human development SDGs. We find that, between 2015 and 30, the world will make only limited progress towards achieving those SDGs with our current set of policy priorities. Our study finds that across the variables explored here (nine indicators for 186 countries = 1674 country-indicators), 43 percent had already reached target values by 2015. By 2030, target values are projected to be achieved for 53 percent of country-variables. This paper highlights special difficulty in achieving targets on some SDG indicators (access to safe sanitation, upper secondary school completion, and underweight children) representing persistent development issues that will not be solved without a significant shift in domestic and international aid policies and prioritization. In addition, we highlight 28 particularly vulnerable countries that are not projected to achieve any of the nine human development related target values in a middle-of-the-road scenario. These most vulnerable countries (MVCs) must be the focus of international assistance.
Article
Full-text available
Abstract. This paper argues that the Global Indicator Framework required to support the 2030 Agenda Sustainable Development Goals will not be successfully populated, using only existing approaches and mechanisms. Official statistical systems must adapt and consider new approaches if only partial success is to be averted. This paper presents a proposal to accredit unofficial statistics as official for the purposes of compiling sustainable development goal indicators. While there may be some reluctance, and there are certainly risks with this proposal, the arguments put forward highlight the potential for collaboration. Keywords: 2030 Agenda, accreditation, risk management, fundamental principles of official statistics
Article
Full-text available
The Sustainable Development Goals (SDGs) recently adopted by the United Nations represent an important step to identify shared global goals for development over the next two decades. Yet, the stated goals are not as straightforward and easy to interpret as they appear on the surface. Review of the SDG indicators suggests that some further refinements to their wordings and clarifications to their underlying objectives would be useful. We bring attention to potential pitfalls with interpretation, where different evaluation methods can lead to different conclusions about country performance. Review of the United Nations' SDG database highlights the overwhelming challenge with missing data: data are available for just over half of all indicators and for just 19 percent of what is needed to comprehensively track progress across countries and over time. We offer further reflections and propose some simple but cost-effective solutions to these challenges.