=Paper=
{{Paper
|id=Vol-1456/paper1
|storemode=property
|title=Visual Exploration of Formal Requirements for Data Science Demand Analysis
|pdfUrl=https://ceur-ws.org/Vol-1456/paper1.pdf
|volume=Vol-1456
|dblpUrl=https://dblp.org/rec/conf/semweb/DadzieD15
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==Visual Exploration of Formal Requirements for Data Science Demand Analysis==
https://ceur-ws.org/Vol-1456/paper1.pdf
Visual Exploration of Formal Requirements for Data
Science Demand Analysis
Aba-Sah Dadzie and John Domingue
KMi, The Open University
Milton Keynes, UK
{aba-sah.dadzie,john.domingue}@open.ac.uk
Abstract. The era of Big Data brings with it the need to develop new skills for
managing this heterogenous, complex, large scale knowledge source, to extract
its content for effective task completion and informed decision-making. Defining
these skills and mapping them to demand is a first step in meeting this challenge.
We discuss the outcomes of visual exploratory analysis of demand for Data Sci-
entists in the EU, examining skill distribution across key industrial sectors and
geolocation for two snapshots in time. Our aim is to translate the picture of skill
capacity into a formal specification of user, task and data requirements for de-
mand analysis. The knowledge thus obtained will be fed into the development of
context-sensitive learning resources to fill the skill gaps recognised.
Keywords: big data, visual exploration, visual analytics, demand analysis, RtD,
data-driven decision-making, ontology-guided design
1 Introduction
We are in the middle of a technological, economic, and social revolution. How we
communicate, socialise, occupy our leisure time, learn and run a business has slowly
moved online. In turn, the Web has entered our phones, our newspapers and notebooks,
our homes and cities, and the industries that power the (digital) economy. The resulting
explosion of data is transforming enterprise, government and society.
These developments have been associated with a number of trends of which the
most prominent is Big Data. As noted recently by Google [1], what is important about
data is not volume, but its contribution to innovation and, thereby, value creation. We
agree with this assessment of the situation today: we are in the midst of a Data Driven
Innovation (DDI) revolution. The benefits for DDI will be significant. Studies suggest
that companies that adopt data driven decision-making have an output and productivity
5-6% higher than would be expected given their IT investments alone [5]. This assess-
ment is backed up by Cisco, who report [4] that the Internet of Everything (the conflu-
ence of people, processes, data and things) will create $14.4 trillion in value globally
through the combination of increased revenue and cost savings. McKinsey [15] makes
similar predictions. Big Data has an estimated value of $610 billion across four sectors
in the US (retail, manufacturing, healthcare and government services), with open data
alone raising more than $3 trillion per year in seven key business sectors worldwide
– education, transport, retail, electricity, oil & gas, healthcare, consumer finance [17].
1
Visual Exploration of Formal Requirements for Data Science Demand Analysis
According to a recent OECD report EU governments could reduce administrative costs
by 15-20% by exploiting public data, equivalent to savings of e150-300 billion [5].
A major blocker for these promising prospects is the lack of Data Science skills
within the workforce, be that technical specialists, managers or public servants. A well-
known McKinsey study [16] estimated in 2011 that the US would soon require 60%
more graduates able to handle large amounts of data effectively as part of daily work.
With an economy of comparable size (by GDP) and similar growth prospects, Europe
will most likely be confronted with a talent shortage of hundreds of thousands of qual-
ified data scientists, and an even greater need for executives and support staff with
basic data literacy. The number of job descriptions and increasing demand in higher-
education programmes and professional training confirm this trend,1 with some EU
countries forecasting an increase of over 100% in demand for data science positions
in less than a decade.2 For example, a recent report by e-Skills UK/SAS [7] notes a
tenfold rise over the past five years in demand for Big Data staff in the UK, and a 41%
increase in the number of such jobs posted during the 12-month period from Oct 2013–
Oct 2014, with over 21,000 vacancies in 2013. The study also estimated that 77% of
Big Data roles were “hard-to-fill” and forecast a 160% increase in demand for Big Data
specialists from 2013–2020, to 346,000 new jobs. Similar trends may be extrapolated to
other EU countries. The European Commission’s (EC) communication on ‘Towards a
thriving data-driven economy’ highlights an “adequate skills base is a necessary condi-
tion of a successful data driven economy”3 , while the Strategic Research and Innovation
Agenda (SRIA) for the Big Data Value contractual Public-Private Partnership (cPPP)
lists Big Data skills development as their top non-technical priority.4
The European Data Science Academy (EDSA)5 is a new EU-funded project which
will deliver the learning tools that are crucially needed to close this problematic skills
gap. EDSA will implement cross-platform, multilingual data science curricula which
will play a major role in the development of the next generation of European data prac-
titioners. To meet this ambitious goal, the project will constantly monitor trends and
developments in the European industrial landscape and worldwide, and deliver learning
resources and professional training that meets the present and future demands of data
value chain actors across countries and vertical sectors. Thus, a core part of our work
is focused on demand analysis. We need to ensure that the data science curricula and
associated learning resources that we create meet the needs of industry across Europe,
recognising that this will vary by sector, job role and geographical region. In this paper
we describe some of the visual tools we are developing to support our Demand Analy-
sis. Through our visual analysis, we aim ultimately to make visible to a wider audience
data we are collecting through a combination of interviews with key stakeholders, on-
line surveys and data mining of job websites.
We continue in section 2 with a discussion of related work. We then describe in
section 3 the methodology we are following, through data exploration (detailed in sec-
1
Government calls for more data scientists in the UK: http://bit.ly/1RLztP8
2
Demand for big data IT workers to double by 2017 . . . http://bit.ly/1Ntwcm8
3
Communication on data-driven economy: http://bit.ly/1pNmxQq
4
SRIA on Big Data Value for Europe: http://bit.ly/1LDSR1C
5
European Data Science Academy: http://edsa-project.eu
2
Visual Exploration of Formal Requirements for Data Science Demand Analysis
tion 4), to uncover design and task requirements. We discuss our findings in section 4.1
and feed these into the definition, in section 5, of target users, typical user tasks and the
data necessary for users to meet their goals. We conclude in section 6 with pointers to
the next stage in our study. We envisage, through this process in which we use dynamic,
living data to guide our investigation, to identify intuitive, expressive approaches that
will aid our analysis and serve as pointers to our targets, mainly Data Scientists, to a
picture of capability and demand for their skills in today’s data driven economy.
2 Related Work
Ontologies provide a useful framework for capturing, sharing and guiding (re)use of
knowledge about an object, a domain or a situation [9,13,18,22]. Devedzic [6] in an
early paper forecast the utility of ontologies for the Semantic Web and to improve com-
petition in industry. The survey ([6]) illustrates how knowledge modeling and acqui-
sition using ontologies aids collaboration and interoperation within and across disci-
plines, by providing standardised references to, and, therefore, interpretation of knowl-
edge extracted from independent sources. The ESCO (European Skills, Competences,
Qualifications and Occupations) vocabulary [8], for instance, was built to reduce recog-
nised mismatch between demand in employment sectors across the EU and expertise in
the current and future workforce. ESCO aims to help reduce unemployment by match-
ing also to up-to-date training for each market. A final version is to be released in 2017
as Linked Open Data (LOD) to increase reusability in, e.g., statistical and demand anal-
ysis.
Ontologies may also be used to directly influence design, development and use of
technology [13,22]. Grimm et al., [9] describe their use to guide design choices during
software development, to generate metadata about design and other intermediate arte-
facts created during the software development process and, therefore, improve commu-
nication between developers. Paulheim et al., [18] survey work carried out to enhance
capability in employing user interfaces (UIs) for specified tasks using ontologies, e.g.,
for filtering, clustering, visualising and navigating through information, as well as cus-
tomising the UI itself for a task, user type or the user’s environment. In our case, we
aim to employ ontologies to guide: (i) knowledge capture – about demand for Data
Science skills and capability to meet this demand, (ii) (re)use of this knowledge for
context-driven, analytical and decision-making support, (iii) through an interface that
supports context- and user-centred exploration and extraction of information about skill
gaps and training resources for plugging them.
Visual analytics provides an intuitive, interactive approach for extracting task-based
knowledge from complex data such as in our use case [12]. Both visualisation tech-
nique and how it is applied influence where visual and cognitive attention are directed
and how data content is perceived and interpreted [10,11]. Especially for abstract, dy-
namic, large scale, multi-dimensional data, therefore, it is useful to provide alternative
perspectives on the same dataset. These, used in isolation or in concert, allow differ-
ent patterns and relationships to be revealed, triggering insight and resulting in more
comprehensive exploration. Further, integrating (highly advanced) human perception
into the analytics loop, to guide data processing, analysis and visualisation widens the
3
Visual Exploration of Formal Requirements for Data Science Demand Analysis
scope for exploration and increases confidence in decision-making based on the results
obtained [10,12].
Reusable, extensible libraries and APIs for already proven visualisation and analyt-
ics techniques are particularly useful in such cases, as they ease development of and
interaction with visual analytics tools [3,12], allowing a focus on research into novel
solutions and their application and evaluation. However, identifying the optimal tech-
nique(s) for a task is influenced by a variety of factors, including user skill, data and
domain, the task itself and whether and what subsequent use will be made of the results
[10,11,12]. Taxonomies and ontologies play a useful role here by providing a formal-
ism for specifying requirements and translating them into design. They may be used to
document best practice for specific use cases, thereby providing context-based design
guidelines [13,14,18]. We aim to harness ontologies to drive and document our design
activities, to guide the development of an intuitive, reusable, extensible solution that
serves the user’s particular and evolving needs and context.
3 Methodology
Keim et al., [12] extend the “visual information seeking mantra” [20] by placing first
analysis of the data and/or situation, before an overview that highlights salient informa-
tion, followed by exploration and further, detailed analysis of regions of interest (ROIs).
In line with user-centred design (UCD) principles and recommended practice in visual
analytics [10,11,12], we must ensure an intuitive UI and interaction methods that al-
low a focus on user tasks rather than the tool or its interface. We follow the principles
of research through design (RtD) [2,19], using the process of exploring the knowledge
and design space, during iterative data exploration, to probe initial and reveal additional
requirements – see Fig. 1.
browse generate explore & extract
source data visual overview discover knowledge
design artefact
identify ROIs
capture requirements
design
(user, task, data)
(UI & system)
as ontology
design artefact
generate detailed &
F+C views
design artefact
Fig. 1. Methodology followed in exploratory study, highlighting (with a faint border) where de-
sign artefacts are generated.
This study bears some similarity to [11]; however, we do not seek to formalise
design patterns for composite visualisations. We aim, from the knowledge exploration
exercise detailed in sections 4 and 5, to identify a range of intuitive visual analytics
options, and as a result design that will guide customisation for use, individually and in
concert, to meet the needs of different user types and tasks within the scope of demand
analysis as described in sections 1 and 2.
4
Visual Exploration of Formal Requirements for Data Science Demand Analysis
4 Initial, Visual Exploration of Demand Data
We present a case study in demand analysis: investigating capacity against capability
in key industrial sectors within the EU for expertise in Data Science. The dataset used
comprises summary data crawled from LinkedIn, the first of a number of target do-
main expert networks and web services advertising job postings. Search terms are core
and specialised skills grouped into seven skillsets (e.g., as listed in Fig. 2), identified
through interviews with policy and decision-makers in industry across the EU and a
focus group in the UK. Each skillset is translated into the official or dominant work-
ing language (37 in all) in each of 47 European countries. Due to restrictions to data
extraction on LinkedIn only term frequency in job adverts is currently extracted, col-
lected daily across three top-level dimensions: (i) industrial sector (captured as skill),
(ii) geolocation and corresponding language and (iii) time. (Term selection and the data
collection process is detailed in ([21].) While the dataset size is currently relatively
small, complexity is introduced by large differences in scale and cross-linking within it.
A key requirement is therefore scalability, to handle growth in size with time, and also
complexity as additional context is mined from LinkedIn and other relevant sources.
We focus in this study on the temporal element in the data, treating the spatial
attribute (geolocation and, by derivation, language) as an additional lens (filter) through
which we examine the non-spatial data (skillsets). The aim here is two-fold: to identify
effective methods for revealing, first, temporal patterns in the data, and then relating
these to our target audience, using techniques that speak to them [10,12]. Another key
requirement is therefore learnability, and to a point, customisability. The second goal
is, in the process of exploring the data, to clarify our target end user characteristics and
identify key tasks users would expect to be able to perform. We expect as a result, also,
to identify additional data requirements (structure and content) for completing these
tasks.
This necessitates data exploration from different perspectives, to identify where and
how insight is triggered and which views reveal ROIs and answer key questions. While
our overall goal includes the exploration of novel (visual) analytics approaches, this
exploratory exercise focused on obtaining an initial, broad picture of demand and the
identification of ROIs – anomalies, peaks and islands – within the data. We therefore
made use of web-based solutions able to support quick prototyping of simple, yet in-
formative overviews. A number of research prototypes and working visualisation tools,
graphics libraries and APIs exist, implementing one or more of a range of visual anal-
ysis techniques (examples can be found in [10,11,12,14,20]). These include (not con-
sidering 3D for practical reasons): for high-dimensional data – parallel coordinates and
small multiples (e.g., scatterplot matrices); techniques useful for temporal or dynamic
data such as timelines and theme rivers; cartographic or geographical plots; statistical
charts (e.g., line and scatter plots, bar and pie charts); and finally, techniques typically
applied to non-spatial data such as word maps, tree and node-link graphs, and space-
filling techniques such as tree maps and sunbursts. Freeware and open source tools such
as p5.js, Cytoscape.js, Raphaël, D3.js and Leaflet DVF vary with regard to scalability,
stability, author support, user community and compliance with web standards. Tools
backed by commercial organisations, such as Visual.ly, Tableau Public, IBM Watson
Analytics and Google Charts typically make available a limited set of features as free to
5
Visual Exploration of Formal Requirements for Data Science Demand Analysis
use and/or open source, often with restricted licenses. Such services may also require
data upload to company servers.
For this exercise we use D3.js, a relatively well-established JavaScript library devel-
oped for “data-driven”, interactive visualisation [3]. D3.js was built to overcome chal-
lenges encountered by its authors using existing web-based libraries, due to, among oth-
ers, reliance on custom features with inconsistent compliance with web standards and
browsers, or with high complexity. (Server-side) data input and initial, basic parsing
was carried out with PHP, reading from the demand data written to CSV. The visuali-
sations have been tested in Firefox, Chrome and Safari. It should be noted that not all
events are triggered in all browsers, e.g., onChange in drop-down lists. We report our
findings, in section 4.1, from the first three visual analytics techniques we employed:6
(i) line and dot timeline plots, to obtain an overview of trends and variation in de-
mand (patterns) over time;
(ii) small multiples, employing a matrix plot, to compare variation in patterns in at-
tributes of interest for each data point and the whole dataset;
(iii) aster plots, to examine skill demand by location.
We then discuss, in section 5, formal requirements specification and the implications
for design for intuitive, interactive demand analysis and decision-making.
4.1 Findings
Summary statistical analysis showed some skew7 with counts several times higher
across all skillsets for language == ‘gb’ (English) and also for the UK only. How-
ever, further investigation showed more uniform relative distribution across countries.
We therefore normalise the data or exclude the UK or all ‘gb’ countries where necessary
to reveal trends suppressed in the remaining data.
Fig. 2 shows the trend in three timeline plots from the start of the collection period,
11 Mar 2015, to 05 Jul 2015 for demand for the skillset visualisation for ‘gb’ countries.
There is a small peak early in the plot, and a sharp rise from 17th Apr to the 20th, peak-
ing on the 19th. Beyond this there is in general a gradual rise for the rest of the period,
with a few small dips. Trends are similar across all skills but D3.js, which records no
counts till 11 Jun, rising to five on the 30th.
One skill, interaction, records counts more than 20 times greater than all others,
suppressing, as a result, trends in the latter (see Fig. 2a). We looked at two options for
revealing this detail. In the line chart hiding the outlier and modifying, correspondingly,
the range of the y-axis, provides more space for the remaining attributes (see inset,
Fig. 2a). We show also in Fig. 2b the second option: the bottom plot contrasts a logn
scale with the linear scales used in the other two plots. By stacking the journal plot
with the linear scale on top of the normalised plot we obtain two gains. We are able to
examine relative trend for each skill, still within the context of overall demand patterns,
but with little increase in cognitive load.
6
Additional, high-resolution snapshots (including video) at: http://bit.ly/1FLevZE
7
The skew may be due in part to differences in terminology usage and interpretation across
regions and/or the translator used. Further, the data collected to this point comes from a single
(web) source. While we take this into consideration for further analyses, a full investigation of
the cause of the skew is out of scope for this paper.
6
Visual Exploration of Formal Requirements for Data Science Demand Analysis
(a) A multi-line chart showing the demand trend for a selected skillset
(b) Normalisation using a logn scale to reveal relative trend for each skill
Fig. 2. Daily demand for ‘gb’ visualisation; large variation across skillsets for the four month
period requires filtering (2a) and/or normalisation (lower plot, 2b) to reveal suppressed patterns.
We looked next at a data snapshot, taken in Apr 2014,8 aggregating for five countries
(excluding the UK), demand categorised under eleven key topics in Data Science (see
[21] and bottom, right, Fig. 3). We use small multiples to examine multiple attributes
simultaneously. Fig. 3 stacks, from left-right and top-bottom, mini plots showing in de-
scending order counts for demand for each skill for each country, followed by skill and
country percentage. Dynamic sliders are used to examine each skill as a percentage of
the total demand for all countries (in the dataset, including the UK – olive inner bor-
der), and skill distribution within each country (faint blue outer border). The snapshot
highlights values for skill and country percentage greater than 30 and 25% respectively.
Blue borders are found predominantly at the top, showing top-heavy demand for se-
lected skills. This is mirrored in the colour-coded line plot for the full dataset overlaid
8
While the picture of demand continues to change the variables to be examined remain the
same. Comparing earlier findings with current demand allows us to revisit the initial project
requirements defined with respect to data structure and content.
7
Visual Exploration of Formal Requirements for Data Science Demand Analysis
Fig. 3. Small multiples used with dynamic filters to investigate trends across three attributes –
skill count and distribution (%) by country and skill percentage per country. Data for the UK,
which is ∼70% of the total (see Fig. 4) is filtered out.
on each mini plot, which shows a long tail with very small counts per skill and country.
Olive borders are more randomly distributed – e.g., Germany sees 50% of all Statistics,
with a count of one, near the tail end of the chart (the other 50% (one) is in the UK).
We used, next, a space-filling technique, aster plots, a variant of a nested pie chart,
to examine further skill distribution within each country, including the UK, using again
the small multiples technique. Fig. 4 shows on the far left an overview of total de-
mand for each country, then distribution by skill. Area maps to count for each slice. For
the first aster, height maps to skill count per country (up to 11), and for all others skill
percentage (over all countries). While the UK dominates all others, the individual coun-
try plots reveal a degree of similarity in skill distribution. Business Intelligence, Data
Engineering and Cloud Computing are in demand across all, followed by Artificial In-
telligence, which is highest in Poland. One skill, Data Quality Management, records
one count in only one country, the UK – so slim that only by thickening the borders of
each inner slice, to provide an additional visual cue, is it recognised. Here, we see the
Fig. 4. The overview (first two from left) shows total demand per country and distribution of
skills, respectively, for the six countries that follow, clockwise, from 12:00. As in Fig. 2, juxta-
posing the two sets of charts enables focus on the detail for each country within the context of the
overview (F+C). Colour coding is as in Fig. 3.
8
Visual Exploration of Formal Requirements for Data Science Demand Analysis
power in multiple perspectives – this is highlighted in the matrix plot (e.g., in Fig. 3)
for the full dataset with the skills slider set to the maximum (100%).
5 Core Requirements for Demand Analysis
A key point reiterated throughout this exercise is the importance of letting the data
drive our exploration of the knowledge and design space. By examining the questions
raised as we explored this initial dataset, we have identified four areas that we must
address if we are to meet our goals for demand analysis: (i) definition of target users
and (ii) the tasks relevant to each; (iii) data, and therefore, knowledge requirements for
effective task performance and completion, all of which lead to: (iv) effective support
for decision-making. Fig. 1 shows three points at which we expect to generate design
artefacts; we focus in this section on requirements specification, a living artefact that
will evolve with the project. To allow use also as a communication tool with end users
and within the project team, and to feed into the design, development and evaluation
cycle, we aim to specify our requirements formally using an ontology. At this early
stage we use simple structure diagrams, as in Fig. 5, to map this knowledge space. We
document in the process, also, existing standards that we may reuse.
5.1 Target User Types
In line with our aim to match skill gaps in Data Science with context-sensitive training,
at the start of the study we had two key targets identified: Data Scientists, practising
foundIn
Sector
hasDemand C
JobRoleOrType isRequiredFor
isDesirableIn
feedsInto
Demand
hasExpertise owl-time:durationOf
Expertise
leadsTo
owl-time:Duration
hasSkill
Skill
requires acquiredVia
hasSkillGap drives
drivenBy defines
InformationNeed
B D
DataSource
derivedFrom
FormalKnowledgeBase
createdFrom
LearningResource
satisfies
foaf:Person
gumo:hasInterest
gumo:Interest
sameAs gumo:hasKnowledge
gumo:User gumo:Knowledge
sameAs
foaf:based_near sameAs hasDemandFor
User gumo:Location geo:Location
DecisionMaker isA
A
userType
Recruiter
EducatorOrTrainer isA
subClassOf PractisingDataScientist
DataScientist
TraineeDataScientist
JPublic
Fig. 5. High level definition of knowledge structure for design space
9
Visual Exploration of Formal Requirements for Data Science Demand Analysis
or new, and the institutions and personnel (Educators or Trainers) who will develop
learning resources. In exploring the data we recognised that the picture of demand as ex-
tracted from recruitment sites such as LinkedIn may not adequately define the resources
required to fill the gaps identified. A third user is critical in ensuring that training re-
sources meet the needs of the market: the Decision Maker ultimately responsible for
the definition of new roles and essential skills for them, who will also influence training
of new and existing employees. Decision makers may be data scientists or other tech-
nology or domain experts. Finally, we recognise two additional user types. Recruiters
may influence the language in and the interpretation of job adverts. In line with require-
ments for communicating the results of research to the (interested) public, we include
the non-domain end user, who may or may not be a technology expert.
5.2 Task Identification and Requirements Analysis
Sections B and D of Fig. 5 focus on task requirements for data scientists, looking at what
factors drive the acquisition of new skills and what is required to identify which skills
are in demand and where. For clarity, Fig. 5 omits the detailed breakdown of skills
as shown in, e.g., Fig. 2; the demand data will however be mapped along these cate-
gories (classes) to related information captured from, among others, LOD and OERs
(Open Educational Resources). Our exploration raised three questions which impact
the development of training resources: (1) which skills are typically required together?
(2) how are skills ranked in and (3) how transferable are skills across sectors and re-
gions? We must provide means for users to answer these and other questions, and also
filter the data on different criteria, with a baseline for filtering on sector, skill, location
and time, before matching to resources recognised to meet their needs. For technology
experts (such as data scientists) functionality for complex query formulation should be
useful for more complex analysis and knowledge retrieval.
5.3 Input Data Requirements
The summary data gives a broad overview of demand. However, our analysis is limited
by the lack of context; while distinct patterns can be seen, what led to them cannot be
ascertained. To complete the picture of demand we need to fill in the gaps resulting
from content access restrictions on web and other services. We require detail that an-
swers questions such as raised in section 5.2, and that may be used to enrich information
extracted from other target sources. Data requirements include: (i) ideally, full text for
role descriptions, containing, among others: (ii) term frequency per advert, (iii) weight-
ing of terms, i.e. required vs. desired; and (iv) other detailed views on the sectors and
skills of interest, from, e.g., related ontologies and vocabularies, LD and OERs.
With this additional detail we can begin to build a knowledge base (KB) that our
target may draw from to make informed decisions, based on current market data and
context-specific skills training. In section D of Fig. 5 we use a catch-all – Data Source
– to represent both the data mined specifically for the project and other third party KBs.
In the next stage of our study we will define more fully target KBs and how we will link
to and reuse their content.
10
Visual Exploration of Formal Requirements for Data Science Demand Analysis
6 Discussion & Conclusions
Big Data presents a challenge for industry, due not just to its scale and the rate at which
it continues to grow, but because its inherent heterogeneity and complexity present ad-
ditional challenges for mining and reusing its valued content, value which contributes
to gaining competitive advantage. Making effective use of Big Data starts with the spec-
ification of the skills required of the Data Scientist, for roles specific to and that span
industrial sector and local context.
Our exploratory study has provided an initial picture of the demand for Data Sci-
ence skills in key sectors across the EU. We have, in the process, uncovered questions
with implications for the design of effective, intuitive knowledge exploration and an-
alytical support for our target users. Demand is in turn sparse and large, within each
and across skillsets. Relative distribution, conversely, is fairly uniform across location,
but with instances where a specific skill is isolated to a small pocket. We must bear
in mind, however, a key limitation in our study – the loss of context in the summary
data. A second is that our current picture of demand comes from a single source, albeit
extracted using search terms specified by a range of technology and domain experts.
These impact the depth of analysis and the determination of optimal techniques for
doing so, both for our requirements and those of our target users. We must therefore
design for scalability, to manage continued increase in size and complexity and the po-
tential for even greater variability. This demands alternative perspectives from which to
examine data content and structure. Following the methodology used in this study, we
will investigate additional approaches to ensure the generation of intuitive, informative
overviews, with lenses for detailed analysis tailored to the user’s particular context.
The knowledge structure summarised in Fig. 5 is a living document that will evolve
as we obtain a more balanced and complete picture of demand and corresponding skill
gaps. We have started to map the concepts and relationships defined so far to other re-
lated knowledge sources such as the ESCO vocabulary and new information obtained
from further interviews with industry experts. This is to enable more detailed examina-
tion of the relationships between skills within and across skillsets and industry sectors.
The aim is to refine our current skill definitions and map these to role descriptions. We
will then be able to return to our target users, to review the formal, updated requirements
and discuss further design for the analytical and decision-making tools they require.
The ultimate aim is to map the picture of demand to the user’s specific requirements
and feed the knowledge thus obtained into developing effective learning resources. This
is to aid data scientists and decision-makers in industry and academia to identify optimal
paths to acquiring and updating skills that meet the requirements for managing Big Data
in the modern digital economy.
Acknowledgments. The work reported in this paper was funded by the EU project
EDSA (EC no. 643937).
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