Skills Data Digest - Webinar 3

00:52:25
https://www.youtube.com/watch?v=UqhuJ3SWezg

概要

TLDREv webinar di navbera ji bo amusedên amârên feyzê a Îrlandayê derbas e, guhertindin û třbendin di feyzên emerî nişan dikin hûn œuvre da iconGiGParameters hilberberîya zadekarî. Tu biguherîya çûnka tu li ser zоrûyî dikev zаrret nousin bi zиr'a bêjin sо mțilorulа, ve basicı té hnd girite cubeîn ţeneyʼæin ku divâr.

収穫

  • 👩‍🏫 Konferans di serrargonxeda bernameya hêjare a feyzê û fêrbûna jiyana pîştrim
  • 📊 Di webinairekê de seroketrî û rejnogireyên li ser hilberên zanistî diterçon dikin
  • 📈 Sûretan din êka developara kenningên li ser fîroşandekî krûn di Îrlandayê de peyatir dike
  • 🔍 Di jêkêlan da beraqekê ji bo demkartan û danobjikan dijw.

タイムライン

  • 00:00:00 - 00:05:00

    Norah Trench van Irish University's Associationê pêşniyarîya webinarên nimêverên înternetê dide, da ku jî bo çalakî û xalên zanîngeh awkirin destûrê dide. Ew ji ber hilberîna pêşgotina bêhiyaw a zanyarî li ser xebatên li ser pêkanîn û pêdiviyan fêrî ye.

  • 00:05:00 - 00:10:00

    Dr. Sheamus McGinnis xebata xwe li ser pêdiviyan fêrî ya pêşketinî li ser Teknolojîya Qedropol a hilbijartinê diyarî kir. Ew di binî meîkrozoftê lê dibe û di xebateke tevahî fêrbûna fêt dikin.

  • 00:10:00 - 00:15:00

    Xebata Pêwîstiyên Fêrî AMYNê di armanca xwe de pêştextir dike ku çawa Teknolojiyên nû hûnermendiyê û karmendiyê mezin dikin. Ew dide ku zanîngeh pêwîstiyên yên kevn nêtijî kirin û hûnermendan baateyek fêrî nedan.

  • 00:15:00 - 00:20:00

    Xebatên pêşketina ji bo fermanên karî di heta 5 salî de têne çêkirin û pêdiviyan fêrî li ser fêrbunan fêt hûnerman ne da ku jî hûnermendan pîvaziya fêrbûna li Teknolojiyê baz dike.

  • 00:20:00 - 00:25:00

    Fêdîi jê derheqê ji bo pargîdaniya blockchain li geloşkê dijwar an gerdûnî dîti, hûnermendan digerên pargîdaniyên zanîngeh ya harmonize kiriye. Yên pêkarê xebatiyan pêşpitîne lê hûnermenda neteweyî ne Dem.

  • 00:25:00 - 00:30:00

    Xebateke fêrbûn a din li ser blockchain leverdanên EUê rast dike û piştgirîya zanîngeh di meqama hûnermenda irlandî de pêvazî dike. Ew gotar wê pêwîstiyên fêrbûna baş dike.

  • 00:30:00 - 00:35:00

    Di lêkolînên zanistek de 10 pêncayan nîşan dide ku pêdiviyan fêrî ya blockchain, AI û Automation li hûnermenda Irlandî binpêndike û piştgirî ye.

  • 00:35:00 - 00:40:00

    Di vê seetê de lêkolîn li carenan diyadin ku dikarin hûnermendan xwe xebatbikî û xebatan hono mucetel biken. Ew digotin ku di van lêkolînan de, pirsgîrêka hûnermî bû pênc kêm di qutan de heye û tiştinber li hûr bû.

  • 00:40:00 - 00:45:00

    Zanîngeh bi zorî digot di van lêkolînên derheqê di fisharê di nav şahî û gotarqandin de wateyên fêt biken. Zanîngehê di nîştimanê devka fêt dike.

  • 00:45:00 - 00:52:25

    Fêrbûna vî xebat? Wateyên nezirî û nîşanên fêt dimîne gotara hûnermenda zêde jî temam dike. Xebateka tevn zanyarî ya nû ji bo futbeşî ya albîtedir.

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マインドマップ

ビデオQ&A

  • Çima di dema dawî de pêvajoya teknolojiyê ya li Îrlandayê girîng bû?

    Ez zanîna ku guhertinên gelî ya teknolojiya ciwan di ser endamên kazasî ya Îrlandayê de bû cî.

  • Di pêvajoya xebatê de çima guhertinek têwrheliyê ye grîng bû?

    Sîstem ji bo şopandin û bişîktina rêbazên fêrgeh û bilindbûna xeyalên zanîngehê ye.

  • Çawa gelek piştgîrên ku di LinkedIn de tê de navên xwebانه dikev bûyî havaşda bike?

    Hûn dikarin guherînên paşîn di hevsengên fêrbûna mirovî ya nuh êvarî ya rûjnameyî yên şanî neka hîb kom dike bibînin ji ber ku bikaranînên van hekan zêdetir nîşan dikin.

  • Zanyarîya Çayra kî bimêjikîne?

    Wekî ku wê tezmînîne, desteya tevî rêberê bibersivîne û tewandinên qonsikensên yekemî bibîne.

  • Çima zanyarîya hemû stvarên tehlîkê dewlemend û pir bikaranîn bi rêya zanîngeh û amojizar nekin jeberê alîka navekî û madeyekî xweşahîna rûhî ye?

    Peydakirina peyvên pîştr ya dijberan, vekirina jîrêkoumástico ya çepêl dike ku di gelek axefên alîkar da bi gotarê re rehen pişan bîne.

ビデオをもっと見る

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オートスクロール:
  • 00:00:00
    uh hearty welcome to everyone and good
  • 00:00:03
    afternoon uh my name is Norah trench BS
  • 00:00:07
    I'm the head of lifelong learning and
  • 00:00:09
    skills at the Irish University's
  • 00:00:12
    Association um and this is the the third
  • 00:00:15
    um iua skills data digest that we have
  • 00:00:18
    hosted they H kicked off earlier
  • 00:00:21
    this year and the idea behind these um
  • 00:00:26
    skills data D digest as a number of you
  • 00:00:29
    may know is it's a series of lunchtime
  • 00:00:33
    webinars um they are organized
  • 00:00:36
    specifically for colleagues across the
  • 00:00:38
    iua member universities um and it's an
  • 00:00:42
    opportunity to present and to discuss
  • 00:00:45
    some of the key skills uh data
  • 00:00:49
    Publications research coming from uh our
  • 00:00:52
    national skills
  • 00:00:54
    architecture and to support the
  • 00:00:56
    dissemination of This research across
  • 00:00:59
    the
  • 00:01:00
    universities um we previously heard from
  • 00:01:03
    the slmr on the national skills bulletin
  • 00:01:07
    we also heard from the expert group on
  • 00:01:09
    future skills needs on their skills for
  • 00:01:12
    biofarma
  • 00:01:14
    report um and the recordings of those
  • 00:01:18
    webinars and the slides that were
  • 00:01:20
    presented at them um they're they're
  • 00:01:22
    both uh available on the iua
  • 00:01:26
    website um today as you know and and as
  • 00:01:30
    you can see on this screen we're going
  • 00:01:32
    to be hearing um about a recent esri
  • 00:01:36
    report which is on skills requirements
  • 00:01:38
    for emerging
  • 00:01:41
    Technologies um This research is around
  • 00:01:45
    uh demand and supply for for automation
  • 00:01:49
    for AI for blockchain related jobs in
  • 00:01:53
    Ireland and we're going to be hearing
  • 00:01:55
    from one of the authors of the report Dr
  • 00:01:58
    Sheamus mcginness who's a research
  • 00:02:00
    professor in the
  • 00:02:02
    esri um what's also interesting about
  • 00:02:04
    this uh particular piece of um research
  • 00:02:07
    is that it is it is a part of um The
  • 00:02:13
    Joint skills research program on Irish
  • 00:02:16
    skills requirements which the which the
  • 00:02:19
    ESR and the department of further higher
  • 00:02:23
    education research Innovation and
  • 00:02:24
    science um have been running for a few
  • 00:02:27
    years so it'll I suppose also be an in
  • 00:02:30
    opportunity to hear a bit about that uh
  • 00:02:34
    program um like I said earlier after
  • 00:02:37
    Sheamus's presentation we'll have a
  • 00:02:39
    chance for uh questions answers and
  • 00:02:42
    exchange so um I'm hoping we can have a
  • 00:02:46
    lively interaction so please please do
  • 00:02:49
    think of uh questions you would like to
  • 00:02:51
    pose to the presenter um and and then
  • 00:02:55
    finally just to say uh you will have
  • 00:02:57
    seen today's session is being
  • 00:03:00
    recorded um we will follow up by email
  • 00:03:04
    with the recording and with the slides
  • 00:03:06
    after afterwards so please do pass it on
  • 00:03:09
    to any interested colleagues across your
  • 00:03:12
    uh University who may also wish to be
  • 00:03:15
    added to the iua skills data D digest
  • 00:03:19
    mailing list so that's plenty from me H
  • 00:03:23
    without further Ado I'd like to please
  • 00:03:25
    hand over to
  • 00:03:28
    Sheamus Thank you very much uh Nora and
  • 00:03:31
    uh thank you for the invite it's a great
  • 00:03:33
    opportunity to present the research
  • 00:03:36
    there and we're very happy to do
  • 00:03:37
    so so as noris said this is um piece of
  • 00:03:41
    work that was done under the joint
  • 00:03:42
    research program a relatively new joint
  • 00:03:44
    research program between the SRI and Def
  • 00:03:47
    first um so we have a series of these
  • 00:03:49
    programs in The Institute and the idea
  • 00:03:51
    is that you know over a two-year period
  • 00:03:54
    we would undertake a series of studies
  • 00:03:56
    um that would be linked um and would
  • 00:03:58
    build on each other and then um if it's
  • 00:04:01
    in mutually agreed the program would be
  • 00:04:03
    extended or would stopped if it's if
  • 00:04:05
    it's deemed of met its uh requirements
  • 00:04:08
    so this is the first study um that we've
  • 00:04:10
    published we're currently undertaking a
  • 00:04:12
    second study where we're looking at the
  • 00:04:14
    National Training fund we're looking at
  • 00:04:16
    all the major funding streams under that
  • 00:04:18
    fund we're looking at for each of those
  • 00:04:21
    elements we for example we're looking at
  • 00:04:23
    what are the objectives what are the key
  • 00:04:25
    uh recipients um what are the what are
  • 00:04:28
    the key capis that Pro from that and
  • 00:04:30
    then we ask the question how would you
  • 00:04:32
    evaluate or measure that fund or how
  • 00:04:33
    should it be measured what data
  • 00:04:36
    information is being collected and is
  • 00:04:37
    that is sufficient for evaluation
  • 00:04:40
    purposes um with the really the goal to
  • 00:04:43
    to to ensure that at least going forward
  • 00:04:45
    we start making the steps of having a
  • 00:04:48
    proper data infrastructure in place
  • 00:04:50
    where we can properly measure and
  • 00:04:53
    evaluate um the very significant um
  • 00:04:56
    streams of of funding that are that are
  • 00:04:58
    going to particular uh projects under
  • 00:05:01
    the National Training fund and and
  • 00:05:03
    elsewhere but uh in terms of this
  • 00:05:05
    research today um this is our first
  • 00:05:09
    study um and basically um what this is
  • 00:05:13
    about is is to try and assess to what
  • 00:05:17
    extent are changing our emerging
  • 00:05:21
    technology is important for the Irish
  • 00:05:22
    labor market so we know that there's
  • 00:05:24
    rapid technological change um within uh
  • 00:05:27
    within the world and and obviously labor
  • 00:05:30
    markets are impacted by that um but it's
  • 00:05:32
    particularly important from an Irish
  • 00:05:34
    perspective
  • 00:05:36
    that the system is prepared and
  • 00:05:38
    particularly the Education and Training
  • 00:05:40
    System is prepared and forewarned of any
  • 00:05:44
    changes in technological progress that
  • 00:05:47
    are that are impacting the demand for
  • 00:05:49
    jobs so obviously that we we can uh make
  • 00:05:52
    sure that we have provision in place um
  • 00:05:55
    as that demand grows and that's
  • 00:05:56
    particularly important for a country uh
  • 00:05:59
    like Ireland which
  • 00:06:02
    has built a lot of its growth on
  • 00:06:05
    ensuring that we're able to cater for
  • 00:06:06
    the needs of particularly high-tech
  • 00:06:08
    multinational companies so it's
  • 00:06:10
    important that we have proper foresight
  • 00:06:12
    and we're able to understand how the
  • 00:06:14
    world is changed in terms of
  • 00:06:15
    technological change and what jobs are
  • 00:06:17
    emerging in that context traditionally
  • 00:06:20
    when we sort of forecast out what we
  • 00:06:23
    think the labor market will look like in
  • 00:06:25
    five years so we can then um advise
  • 00:06:27
    educational and training providers what
  • 00:06:30
    coures they need to um put in place so
  • 00:06:33
    what and what the skill content of that
  • 00:06:34
    would be we would use traditional manp
  • 00:06:37
    par forecasting
  • 00:06:39
    models and this would require us using
  • 00:06:41
    historical data such as the labor force
  • 00:06:43
    uh survey data and then projecting past
  • 00:06:46
    Trends into the future the problem is
  • 00:06:48
    that data is very old so there's usually
  • 00:06:50
    an 18th month month lag before we get um
  • 00:06:53
    so if if the the labor force data that
  • 00:06:55
    would be available now get into 2024
  • 00:06:58
    would be relate into 20 23 possibly 2022
  • 00:07:01
    it's it's relatively old um so by the
  • 00:07:04
    time we we get that data and we project
  • 00:07:06
    those Trends into the future the world
  • 00:07:08
    has already changed so we need to move
  • 00:07:10
    away from using these historical
  • 00:07:13
    forecasting approaches and labor market
  • 00:07:15
    intelligent approaches and look at using
  • 00:07:18
    more more current data and different
  • 00:07:20
    approaches that allows us to get timely
  • 00:07:23
    insights into particularly labor markets
  • 00:07:26
    where there's going to be a lot of
  • 00:07:27
    change and that relates to labor markets
  • 00:07:28
    that are driven by new
  • 00:07:31
    technologies so the goal of this work is
  • 00:07:33
    really to develop a methodological
  • 00:07:35
    framework that allows us to identify
  • 00:07:38
    what are the important new technologies
  • 00:07:40
    that are emerging within the Irish labor
  • 00:07:42
    market to forecast the extent to which
  • 00:07:45
    uh we have sufficient provision in terms
  • 00:07:47
    of the number of places within the Irish
  • 00:07:49
    system to to meet that demand going
  • 00:07:51
    forward and ideally uh we also want to
  • 00:07:54
    look at what are the types of skills and
  • 00:07:56
    competencies uh that these new courses
  • 00:07:59
    will will will will will demand in terms
  • 00:08:01
    of the technical skills and the other
  • 00:08:04
    types of competencies that will be
  • 00:08:05
    required so that there is enough
  • 00:08:08
    information in terms of the um the
  • 00:08:12
    number of courses um that are required
  • 00:08:15
    in terms of these new emerging
  • 00:08:18
    Technologies and also the skills and
  • 00:08:19
    competencies that should be contained
  • 00:08:21
    within the curriculum to inform the
  • 00:08:24
    policy side of things in terms of the
  • 00:08:25
    fet profis and people that are involved
  • 00:08:28
    in this meeting um at the
  • 00:08:30
    moment and really the goal of our
  • 00:08:32
    research is is was to set up and develop
  • 00:08:35
    this methodology that then can be used
  • 00:08:37
    going forward um so this can be
  • 00:08:40
    reran next year and the situation might
  • 00:08:44
    change might have Chang radically in
  • 00:08:46
    terms of these technologies that are
  • 00:08:48
    that are out there they may be more
  • 00:08:49
    diffusion in the labor market this type
  • 00:08:52
    of approach will pick that up our new
  • 00:08:54
    technologies will emerge this approach
  • 00:08:56
    will will will spot that so it's it's to
  • 00:08:58
    develop a dynamic framework in terms of
  • 00:09:00
    Labor Market intelligence that allows us
  • 00:09:03
    to capture new skills um as they emerge
  • 00:09:06
    in ter in terms of employment and also
  • 00:09:08
    um to allow
  • 00:09:11
    for a system that will that will capture
  • 00:09:14
    changes in those demands as we move
  • 00:09:18
    forward so I'm not going to go into an
  • 00:09:20
    awful lot of the methodology in terms of
  • 00:09:22
    exactly um what we did but basically we
  • 00:09:24
    started off we have a Steering group um
  • 00:09:27
    and we asked for 10 or 12
  • 00:09:31
    areas of new technology that were deemed
  • 00:09:34
    to be potentially important out there in
  • 00:09:37
    the
  • 00:09:37
    world and what we did then was we went
  • 00:09:40
    in and we scraped a number of vacancy
  • 00:09:43
    job job vacancy sites such as LinkedIn
  • 00:09:47
    and jobs.ie Etc um to see exactly how
  • 00:09:50
    important all of these terms were in
  • 00:09:52
    terms of the actual number of job
  • 00:09:54
    adverts uh that were being um put out by
  • 00:09:57
    employers and
  • 00:10:00
    when we completed that exercise we found
  • 00:10:03
    that in terms of these new technology
  • 00:10:06
    areas um the the highest number of job
  • 00:10:09
    adverts were in Automation and
  • 00:10:11
    artificial intelligence so those were
  • 00:10:12
    employers that specifically were trying
  • 00:10:14
    to hire into these fields um another um
  • 00:10:19
    aspect that we included here was
  • 00:10:21
    blockchain because blockchain has also
  • 00:10:23
    been deemed to be important at a
  • 00:10:25
    European level um and actually my team
  • 00:10:27
    and the SRI have been running running
  • 00:10:29
    the the forecasts for blockchain jobs
  • 00:10:32
    for at an EU level for the last number
  • 00:10:34
    of years um both for for for a project
  • 00:10:39
    that's been funded by arasmus so again
  • 00:10:41
    this work sort of Builds on the
  • 00:10:42
    methodologies that we developed for that
  • 00:10:44
    blockchain work for the EU but
  • 00:10:46
    specifically focusing in now on the
  • 00:10:48
    Irish labor
  • 00:10:51
    market so what are the goals of This
  • 00:10:56
    research first of all we will PR provide
  • 00:10:59
    a detailed analysis of the trends in
  • 00:11:02
    terms of the the number of jobs that are
  • 00:11:05
    being advertised for these emerging
  • 00:11:07
    Technologies in Ireland and how they're
  • 00:11:08
    changing over
  • 00:11:09
    time we will then map those jobs we will
  • 00:11:13
    take those jobs and we will create
  • 00:11:15
    forecasts for both the total demand for
  • 00:11:18
    jobs going forward over a fiveyear
  • 00:11:20
    period and then we figure out what
  • 00:11:22
    proportion of those jobs are related to
  • 00:11:24
    new graduates and then we map those
  • 00:11:27
    against what we think the supply of new
  • 00:11:28
    graduates in these three specific areas
  • 00:11:31
    are will be that is uh blockchain Ai and
  • 00:11:36
    Automation in addition to checking
  • 00:11:39
    whether or not we have the right number
  • 00:11:40
    of graduates in these areas or
  • 00:11:42
    sufficient number of graduates in these
  • 00:11:43
    areas we also look and use the
  • 00:11:46
    information within this job
  • 00:11:47
    advertisement data to look at the
  • 00:11:49
    competencies and skills uh that
  • 00:11:51
    employers are requiring in each area and
  • 00:11:54
    specifically we break them down into the
  • 00:11:55
    technical skills um that employers are
  • 00:11:57
    looking for the business related skills
  • 00:11:59
    and the transversal skills and clearly
  • 00:12:02
    these are factors that should inform
  • 00:12:04
    curriculum development in each array so
  • 00:12:07
    it's important to have obviously the
  • 00:12:09
    right numbers of graduates but it's also
  • 00:12:10
    important that these courses are
  • 00:12:12
    equipping the graduates with the correct
  • 00:12:14
    competencies that employers are at least
  • 00:12:17
    asking for we also measure the extent
  • 00:12:21
    and we develop a methodology that allows
  • 00:12:22
    us to measure the extent to which jobs
  • 00:12:25
    are vacancies men
  • 00:12:27
    be remain on M there may be unfilled
  • 00:12:30
    vacancies or skill shortages in each
  • 00:12:32
    respective are area and we also in this
  • 00:12:35
    research um have carried out a number of
  • 00:12:37
    consultative workshops with employers
  • 00:12:40
    for the purposes of a verifying that
  • 00:12:43
    they're that what we've produced here is
  • 00:12:46
    reasonable in their View and then to get
  • 00:12:49
    more information in terms of the skills
  • 00:12:50
    and competencies in the current state of
  • 00:12:53
    both the demand for emerging
  • 00:12:55
    Technologies and Perceptions in terms of
  • 00:12:58
    the adequacy of what's be produced by
  • 00:13:00
    the Education and Training
  • 00:13:03
    Systems so in terms of our of our data
  • 00:13:06
    here we use a number of sources to to
  • 00:13:08
    measure the demand for new jobs in these
  • 00:13:10
    emerging Technologies we use data from
  • 00:13:12
    lightcast data this is a data source
  • 00:13:14
    that basically captures everything
  • 00:13:16
    that's out there in the world in terms
  • 00:13:18
    of job advertisement data this data is
  • 00:13:21
    been currently be used by you know the
  • 00:13:24
    um European commission uses it for their
  • 00:13:26
    ovet tool the ILO the oecd uh so this is
  • 00:13:29
    a very very widely used um labor market
  • 00:13:33
    intelligence data source uh that has
  • 00:13:36
    been harnessed by uh by most of the main
  • 00:13:39
    agencies and out there both at a
  • 00:13:40
    European level an oecd level and at a
  • 00:13:43
    national level as well we also um scrap
  • 00:13:47
    LinkedIn data ourselves uh for Ireland
  • 00:13:50
    um and then we plug everything into the
  • 00:13:51
    sadif fob um forecasting system in order
  • 00:13:55
    to generate our forecast of what we
  • 00:13:57
    think the number of total jobs in each
  • 00:13:59
    area will be over a period and also what
  • 00:14:02
    will the number of jobs related to new
  • 00:14:05
    graduates be the supply side data uh
  • 00:14:08
    comes from was provided to us by the H
  • 00:14:10
    uh Solus and qqi basically on the number
  • 00:14:12
    of courses that are being produced in
  • 00:14:15
    each of these areas AI automated and
  • 00:14:17
    blockchain and as I said we also do the
  • 00:14:20
    consultation with the
  • 00:14:23
    employers so the first thing to look at
  • 00:14:25
    this is the um the share of light cast
  • 00:14:28
    this is basic B the share of the jobs in
  • 00:14:31
    each of these areas uh using the light
  • 00:14:33
    cast data for Ireland over the period uh
  • 00:14:36
    20 18 to 2023 so the first point to make
  • 00:14:40
    is whilst there's a lot of talk about
  • 00:14:42
    the importance particularly of AI and
  • 00:14:44
    Automation in the past less so
  • 00:14:46
    blockchain actually um these jobs that
  • 00:14:50
    require specialist skills in these areas
  • 00:14:53
    are are quite small um and they're not
  • 00:14:56
    really growing very rapidly over time so
  • 00:14:58
    if you look at for example automation
  • 00:15:01
    that is the largest uh grouping in terms
  • 00:15:04
    of these emerging Technologies so that
  • 00:15:06
    accounts for about one and a half% of
  • 00:15:08
    all jobs that are advertised within the
  • 00:15:10
    lightcast data for Ireland over time and
  • 00:15:12
    it hasn't changed very much the the AI
  • 00:15:15
    there you can see it's it's between half
  • 00:15:16
    a percent and 1.2% and you can see the
  • 00:15:20
    blockchain actually is very very small
  • 00:15:21
    at less than 0.2 of a% of total jobs
  • 00:15:25
    that are advertised in Ireland over the
  • 00:15:27
    this period and and many going in very
  • 00:15:29
    much and that's something that we picked
  • 00:15:31
    up in our forecasts uh when we were
  • 00:15:33
    doing the work on blockchain at an EU
  • 00:15:35
    level and this isn't just an Ireland in
  • 00:15:37
    terms of that flatlining demand for
  • 00:15:39
    blockchain jobs it's it's an EU wide
  • 00:15:42
    pattern and we are not unique in this so
  • 00:15:46
    if you look at this chart this is the
  • 00:15:48
    pattern of AI job growth using lightcast
  • 00:15:51
    data uh for a number of countries and
  • 00:15:53
    you can see um whilst it's generally
  • 00:15:56
    trading up in some countries it's it's
  • 00:15:57
    flatter but it's basically between half
  • 00:15:59
    a percent and 1 and a half% of all jobs
  • 00:16:02
    that are advertised so it's not the case
  • 00:16:06
    that these jobs are are are growing
  • 00:16:09
    massively in Ireland over time they
  • 00:16:11
    they're not that's not to say that these
  • 00:16:13
    Technologies aren't being diffused
  • 00:16:14
    throughout other jobs we are looking
  • 00:16:16
    basically here at Specialists required
  • 00:16:19
    to work in the areas of blockchain AI
  • 00:16:22
    and Automation and so the trends are
  • 00:16:23
    quite small and they're they're
  • 00:16:26
    relatively um flat
  • 00:16:30
    so how do we measure the demand uh and
  • 00:16:32
    Supply going forward for these three
  • 00:16:35
    areas well basically what we do is we
  • 00:16:38
    use the lightcast data and we figure out
  • 00:16:40
    exactly how for example automation jobs
  • 00:16:43
    are distributed across
  • 00:16:45
    occupations um and once we figure out
  • 00:16:47
    what sure that for example AI makes up
  • 00:16:52
    of of of say the um of an occupation
  • 00:16:55
    then we can plug that into the um to the
  • 00:16:58
    forecasting and we can run forecasts for
  • 00:17:00
    a 5year period so if we look here in
  • 00:17:03
    terms of the AI jobs when we script the
  • 00:17:07
    data from uh from light class and then
  • 00:17:09
    we look at how that's distributed across
  • 00:17:11
    occupation so we can see that for
  • 00:17:13
    example the AI jobs uh 80% of them are
  • 00:17:17
    are contained within just 10 occupations
  • 00:17:19
    and specifically when we look at 30% of
  • 00:17:22
    all I jobs are are information and
  • 00:17:24
    communication uh professionals
  • 00:17:26
    occupation about 11% 12% in science and
  • 00:17:30
    engineering professionals and then about
  • 00:17:32
    further 11% in uh business and
  • 00:17:35
    Associated professionals and then the
  • 00:17:37
    shares uh fall off uh quite considerably
  • 00:17:40
    and and then about 20% of all AI jobs
  • 00:17:43
    are outside of these
  • 00:17:45
    occupations so basically what we do here
  • 00:17:48
    is for example we would say What
  • 00:17:49
    proportion of the total share of ICT
  • 00:17:52
    professionals are AI workers so say
  • 00:17:56
    that's 2% we then plug that 2% into the
  • 00:17:59
    saop data um and the basis of what is
  • 00:18:02
    forecast by saop for information and
  • 00:18:05
    communication Professionals for Ireland
  • 00:18:08
    over a 5year period 2% of those jobs
  • 00:18:10
    will be allocated to Ai and we do that
  • 00:18:13
    across all occupations so in that way we
  • 00:18:16
    figure out what the total demand for
  • 00:18:18
    these occupations will be uh over a
  • 00:18:20
    5year
  • 00:18:21
    period so there's a couple of things to
  • 00:18:23
    note there you may say well the
  • 00:18:26
    situation next year could be very
  • 00:18:27
    different these are rapidly changing OC
  • 00:18:30
    occupations and job areas that's true so
  • 00:18:33
    then all we need to do next year is to
  • 00:18:35
    go in here rerun this analysis if we see
  • 00:18:38
    increased shares or increased diffusion
  • 00:18:41
    across other occupations that would be
  • 00:18:43
    picked up immediately and the forecast
  • 00:18:45
    could be updated immediately so when I'm
  • 00:18:47
    saying that the key to this methodology
  • 00:18:49
    is that is highly Dynamic that it can be
  • 00:18:52
    updated almost immediately if there are
  • 00:18:54
    changes in the labor market position of
  • 00:18:56
    the emerging technologies that we've
  • 00:18:58
    identified or new technologies come on
  • 00:19:00
    board then we can do that very very
  • 00:19:02
    easily you couldn't do that using the
  • 00:19:04
    historical uh data approaches that I
  • 00:19:07
    spoke about
  • 00:19:09
    earlier then we uh we also
  • 00:19:12
    simultaneously scried uh LinkedIn data
  • 00:19:14
    for Ireland and we figured out what
  • 00:19:17
    proportion of total jobs in each area
  • 00:19:20
    require experienced graduates and these
  • 00:19:22
    are all graduate level jobs or What
  • 00:19:24
    proportion experience require uh new
  • 00:19:27
    entrance and about 40 we estimate around
  • 00:19:29
    40% of all jobs in each of these areas
  • 00:19:32
    are for new entrance and we use that to
  • 00:19:34
    calculate our new entrant demand so new
  • 00:19:38
    entrance demand for a for over the
  • 00:19:39
    forecast period will be 40% of the total
  • 00:19:42
    demand in each
  • 00:19:43
    area I hope that's probably as clear as
  • 00:19:46
    mud but anyway on the supply side then
  • 00:19:49
    we just look at the number of courses
  • 00:19:51
    that are available in each area this is
  • 00:19:53
    information that's given to us uh from a
  • 00:19:55
    range of sources including hii Solace
  • 00:19:57
    and qqi so they tell us the number of um
  • 00:20:01
    the courses in Ai blockchain and
  • 00:20:03
    automation over the 2020 and 21 period
  • 00:20:06
    we aage them and then we multiply them
  • 00:20:08
    by um by by four in order to get the
  • 00:20:11
    total Supply over the forecast period so
  • 00:20:14
    it's quite of a it's quite a a
  • 00:20:16
    mechanical exercise and then we map
  • 00:20:18
    Supply against demand so here the total
  • 00:20:20
    demand for artificial intelligence jobs
  • 00:20:23
    over the forecast period 21 to 25 and we
  • 00:20:26
    also do it for 25 to 2030 was two and a
  • 00:20:29
    half
  • 00:20:30
    thousand 40% of that would be the um
  • 00:20:34
    would be the The Graduate demand uh so
  • 00:20:37
    that's just over a thousand and we
  • 00:20:39
    figure out that on the basis of the
  • 00:20:41
    information that we've been given there
  • 00:20:43
    will be about 1,500 uh places uh in
  • 00:20:46
    terms of graduates coming out onto the
  • 00:20:48
    labor market over that period um so if
  • 00:20:50
    you look across the board it looks as if
  • 00:20:52
    uh graduate demand has been more met by
  • 00:20:55
    Supply particularly in artificial
  • 00:20:57
    intelligence not so much in blockchain
  • 00:20:59
    but we figure that there are actually
  • 00:21:00
    more potential courses out there in
  • 00:21:02
    blockchain than than the 80 that's been
  • 00:21:04
    that was been specified to us blockchain
  • 00:21:06
    is another one specifically that may not
  • 00:21:09
    necessarily require specialist graduates
  • 00:21:11
    but could be facilitated by having
  • 00:21:13
    blockchain modules in general ICT
  • 00:21:16
    degrees but um the based on how these
  • 00:21:20
    Technologies are diffused throughout the
  • 00:21:22
    labor market at the moment there doesn't
  • 00:21:24
    seem to be any worries in terms of the
  • 00:21:26
    ability of the Irish system to meet the
  • 00:21:28
    needs of the labor market in terms of
  • 00:21:30
    the demand for new graduates uh going
  • 00:21:33
    forward over the forecast period and we
  • 00:21:34
    do this also for uh
  • 00:21:37
    2025 to
  • 00:21:39
    2029 again the details are all in the
  • 00:21:43
    reports so another issue um to deal with
  • 00:21:46
    to look at here is even if you've got
  • 00:21:48
    the numbers right in terms of okay we
  • 00:21:50
    have enough graduates to meet demand
  • 00:21:51
    across these three emerging technology
  • 00:21:54
    areas if the curriculum structure isn't
  • 00:21:58
    correct and not meeting the needs of
  • 00:21:59
    employers if they're not getting the
  • 00:22:01
    skills that employers require uh then um
  • 00:22:04
    you still have a problem you still will
  • 00:22:05
    have skill shortages so again we use
  • 00:22:09
    light cast data in order to understand
  • 00:22:12
    what are the
  • 00:22:13
    main competencies that are required
  • 00:22:17
    within these three emerging Technologies
  • 00:22:18
    and how what are the correlations
  • 00:22:20
    between them um and and and and what are
  • 00:22:23
    and what are the the skills that are
  • 00:22:26
    area specific if you want so we I'm
  • 00:22:29
    giving you the results here for um the
  • 00:22:31
    total taxonomy for all jobs but we also
  • 00:22:34
    break it down in the report to look
  • 00:22:36
    specifically at the taxonomy in terms of
  • 00:22:38
    the skills and the competencies that are
  • 00:22:41
    required by employers for new graduates
  • 00:22:43
    so that would be more relevant again for
  • 00:22:44
    the curriculum um development side of
  • 00:22:47
    things whereas the skills and
  • 00:22:49
    competencies required across all
  • 00:22:50
    graduates our senior graduates is more
  • 00:22:52
    an issue in terms of lifelong
  • 00:22:54
    learning the taxomony that diecast use
  • 00:22:57
    they break it into into the technical
  • 00:23:00
    the competencies that are required in
  • 00:23:01
    terms of Technical and hard skills
  • 00:23:03
    business skills and then these soft
  • 00:23:05
    transversal
  • 00:23:07
    skills and the first thing to note is
  • 00:23:09
    that across all three areas um whilst
  • 00:23:13
    obviously hard tactical skills are
  • 00:23:16
    important um just under half of uh the
  • 00:23:21
    competencies that are that are being
  • 00:23:23
    requested for example by employers into
  • 00:23:24
    automation our business and transversal
  • 00:23:27
    um that figures just over 40% for
  • 00:23:30
    artificial intelligence and 40% for
  • 00:23:32
    blockchain skills so the first thing
  • 00:23:34
    that emerges in terms of without looking
  • 00:23:36
    at the specific competencies is that
  • 00:23:38
    there is at least employers are asking
  • 00:23:41
    uh for a high level of of Competency in
  • 00:23:44
    both transversal and business
  • 00:23:46
    competencies among for among new
  • 00:23:48
    graduates entering the labor market in
  • 00:23:51
    these emerging areas it isn't certainly
  • 00:23:52
    all about having technical
  • 00:23:57
    skills then we we um we look across the
  • 00:24:00
    technical skills that are been asked for
  • 00:24:02
    and and we look for similarities and
  • 00:24:05
    basically these are the top 10 skills
  • 00:24:07
    that are being asked for in these job
  • 00:24:08
    adverts so for example in jobs that we
  • 00:24:10
    identified for a for automation 34% of
  • 00:24:15
    adverts ask for control um systems
  • 00:24:19
    competencies if you look at artificial
  • 00:24:21
    intelligence 61% of the of the job
  • 00:24:23
    adverts in that area asked for um
  • 00:24:27
    competency in machine learning learning
  • 00:24:29
    and then if you look at blockchain uh
  • 00:24:30
    31% of those jobs that the most
  • 00:24:32
    frequently um requested competency was
  • 00:24:35
    agile
  • 00:24:36
    methodologies and then we have a coding
  • 00:24:38
    system if a competency is asked
  • 00:24:41
    in two of the uh of the emerging
  • 00:24:45
    technology areas the code is orange if
  • 00:24:48
    it is in three it's green if it is in
  • 00:24:50
    Black it is H in if it there only asked
  • 00:24:54
    specifically in one area then the it's
  • 00:24:56
    black so what we see for example in
  • 00:24:59
    computer science that owns one on
  • 00:25:01
    artificial intelligence computer science
  • 00:25:03
    is in the top 10 competencies that's
  • 00:25:05
    required in blockchain and in artificial
  • 00:25:07
    intelligence the same with agile
  • 00:25:10
    methodology none of the competencies
  • 00:25:12
    that are being requested by Employers in
  • 00:25:14
    automation are common to either
  • 00:25:17
    artificial intelligence or blockchain so
  • 00:25:20
    that's important to note that the the
  • 00:25:22
    competencies the Tactical competen
  • 00:25:24
    required for automation jobs are very
  • 00:25:26
    specific um whereas there is a common
  • 00:25:29
    sort of there is a commonality between
  • 00:25:30
    the competencies that are required uh
  • 00:25:33
    for artificial intelligence and
  • 00:25:35
    blockchain when we look at then the
  • 00:25:38
    business skills that are asked for in
  • 00:25:39
    these adverts we see much more
  • 00:25:42
    commonality for example we we're
  • 00:25:43
    starting to see green occurring here so
  • 00:25:47
    uh management skills are asked for in
  • 00:25:49
    automation jobs intelligence artificial
  • 00:25:52
    intelligence jobs and blockchain jobs
  • 00:25:54
    that that is the most common business
  • 00:25:56
    related competency um there are
  • 00:25:58
    competencies that are only common to
  • 00:26:00
    artificial intelligence and blockchain
  • 00:26:02
    such as marketing and business
  • 00:26:03
    development and then there are still a
  • 00:26:05
    series of business related competencies
  • 00:26:08
    in automation that are specific to
  • 00:26:10
    automation jobs process Improvement
  • 00:26:12
    procurement training and development and
  • 00:26:14
    time management and change
  • 00:26:16
    management and then when we look at
  • 00:26:17
    these transversal skills that are being
  • 00:26:19
    requested by employers again uh these
  • 00:26:22
    these common competencies are there's
  • 00:26:24
    much more commonality you can see that
  • 00:26:26
    there's 1 2 3 4 5 six seven of the top
  • 00:26:28
    10 uh competencies are being required
  • 00:26:31
    across all three areas with
  • 00:26:34
    Communications uh being the top one
  • 00:26:36
    across all three areas problem solving
  • 00:26:39
    also uh proving to be a highly requested
  • 00:26:42
    competency across all three emerging
  • 00:26:44
    tech technology
  • 00:26:46
    areas so we also do this in the report
  • 00:26:49
    um for hard skills uh transversal
  • 00:26:51
    business skills again separated out the
  • 00:26:54
    demands that are put in place for jobs
  • 00:26:58
    that are at the new graduate level only
  • 00:27:00
    so those competency and that information
  • 00:27:02
    would be more relevant uh
  • 00:27:05
    for program development and particularly
  • 00:27:07
    at the undergraduate
  • 00:27:10
    level we also um try and measure the
  • 00:27:13
    extent to which firms are struggling to
  • 00:27:15
    fill posts in either in any of these
  • 00:27:18
    areas we do that using LinkedIn
  • 00:27:21
    data um and what we see is we the the
  • 00:27:24
    value of the LinkedIn data is it tells
  • 00:27:26
    us it has a good measure of how long the
  • 00:27:28
    advert is live for it also tells us the
  • 00:27:31
    number of applications um that any
  • 00:27:34
    particular ad gets but on on the online
  • 00:27:37
    system so you can see for example that
  • 00:27:40
    across all three um areas the average
  • 00:27:44
    duration of an ad is been 10 or 11 days
  • 00:27:47
    the average number of applicants for
  • 00:27:49
    each job is between 33 and 50 so what we
  • 00:27:53
    say here is a job is likely to be
  • 00:27:55
    struggling or there's likely to be a
  • 00:27:59
    difficulty in terms of filling that job
  • 00:28:01
    if it has a duration of more than 30
  • 00:28:03
    days so it's three times longer than the
  • 00:28:05
    average and it has less than 10
  • 00:28:08
    applications okay so it's it's it's it's
  • 00:28:11
    it has potentially less than 20% of the
  • 00:28:14
    average number of people applying for it
  • 00:28:16
    those we're not saying that those jobs
  • 00:28:18
    are all likely to be skill shortages but
  • 00:28:20
    the skill shortages are likely deeper
  • 00:28:22
    content in that clump of jobs and when
  • 00:28:25
    we look across then our measure we
  • 00:28:28
    estimate that around 3.9% or less than
  • 00:28:31
    4% of jobs in blockchain are likely to
  • 00:28:34
    be difficult to fill um but that figure
  • 00:28:37
    Rises for about 7.7% or just under to 8%
  • 00:28:40
    of automation jobs about 6% of jobs we
  • 00:28:44
    estimate in the AI uh labor market are
  • 00:28:46
    likely to be difficult to
  • 00:28:48
    fill we do some econometric analysis in
  • 00:28:52
    that I'll not bore you with that
  • 00:28:53
    particularly but a couple of things um
  • 00:28:56
    did arise in terms when we looked at the
  • 00:28:58
    potential skill shortages we didn't find
  • 00:29:00
    any evidence when we controlled for
  • 00:29:02
    other things uh that potential skill
  • 00:29:04
    shortages are are statistically higher
  • 00:29:06
    in any one of the three areas compared
  • 00:29:08
    to the other two um there may be a
  • 00:29:11
    problem in terms of new graduates um
  • 00:29:14
    within the automation um job
  • 00:29:17
    market and and within particularly AI
  • 00:29:20
    within within manufacturing the
  • 00:29:21
    manufacturing sector and we did find
  • 00:29:23
    that where jobs tended to have remote or
  • 00:29:26
    hybrid working
  • 00:29:28
    advertised as part of their um their job
  • 00:29:31
    terms and conditions those jobs were
  • 00:29:33
    much less likely to be U classified as
  • 00:29:37
    skill potential skill shortages so the
  • 00:29:39
    jobs that had the hybrid working uh or
  • 00:29:42
    the remote working not surprisingly were
  • 00:29:44
    more likely to be filled more quickly in
  • 00:29:47
    each of these
  • 00:29:48
    areas and then moving on to the final
  • 00:29:51
    part of our analysis we basically asked
  • 00:29:53
    then we had a series of workshops mostly
  • 00:29:56
    um the firms that that attend the these
  • 00:29:58
    workshops were really from the large uh
  • 00:30:00
    multinational end of things were were
  • 00:30:03
    tended to be more over
  • 00:30:06
    represented so we asked them um
  • 00:30:08
    basically do you think our methodology
  • 00:30:10
    is sensible do the numbers look okay and
  • 00:30:14
    generally the employers felt that
  • 00:30:18
    our our methods looked looked perfectly
  • 00:30:21
    fine and our numbers looked sensible but
  • 00:30:23
    they did say that there was a risk in
  • 00:30:24
    terms of the a AI labor market that the
  • 00:30:27
    job could be that are demanded there in
  • 00:30:30
    the future could be much higher than
  • 00:30:31
    what we are predicting but as I said the
  • 00:30:35
    because we have a dynamic approach here
  • 00:30:37
    if we do see a rise in uh jobs in
  • 00:30:40
    particular for instance Ai and so there
  • 00:30:42
    there's higher shares across the the
  • 00:30:44
    occupations that we looked at are across
  • 00:30:46
    other occupations we could see that auto
  • 00:30:48
    automatically on and immediately we
  • 00:30:50
    could update our
  • 00:30:52
    forecasts in terms of then the
  • 00:30:55
    um other issues that the employers
  • 00:30:58
    again um in terms of what is needed in
  • 00:31:01
    the labor market they players F
  • 00:31:04
    particularly in terms of AI there was a
  • 00:31:06
    substantial amount of legal ethical and
  • 00:31:09
    Regulatory change coming down the road
  • 00:31:11
    so they pointed to example for the EU
  • 00:31:13
    artificial intelligence act um and there
  • 00:31:15
    was a similar concern for blockchain as
  • 00:31:17
    well we're seeing increased regulation
  • 00:31:19
    there and that in addition to the the
  • 00:31:22
    technical uh people and the people with
  • 00:31:24
    the technical skills there is also going
  • 00:31:26
    to be a strong demand the employ first
  • 00:31:28
    FA for people who can Implement and deal
  • 00:31:30
    with the legal requirements and the
  • 00:31:32
    regulators and the change in regulation
  • 00:31:34
    requirement as we go forward so so there
  • 00:31:37
    there is an increased demand for for
  • 00:31:39
    legal Specialists and Regulatory
  • 00:31:41
    specialists in these areas um and the
  • 00:31:43
    employers felt that is important that
  • 00:31:45
    policy needs to adjust in that respect
  • 00:31:47
    as well to to help firms adjust to
  • 00:31:51
    change in regular environment in these
  • 00:31:53
    key technology areas going
  • 00:31:56
    forward the employers also felt that
  • 00:31:59
    again in consistent with what we found
  • 00:32:01
    when we with the LinkedIn dat with the
  • 00:32:03
    dcast data was that there is a strong
  • 00:32:06
    need for transversal skills and work
  • 00:32:08
    experience at entty level so people
  • 00:32:10
    coming out from degrees um there was a
  • 00:32:13
    sense that maybe the um the community
  • 00:32:16
    the transversal um and and the and the
  • 00:32:18
    business skills of current graduates um
  • 00:32:22
    aren't what they have been in the past
  • 00:32:23
    and that Co may have been um an
  • 00:32:25
    explanation for for some of that um
  • 00:32:29
    again in in addition to um teaching
  • 00:32:33
    graduates or students technical
  • 00:32:36
    capabilities um they they felt that it
  • 00:32:38
    was also important that students had
  • 00:32:40
    business capabilities that would teach
  • 00:32:42
    them actually how to adapt with new
  • 00:32:45
    technologies in a business environment
  • 00:32:47
    and and that those skills to some extent
  • 00:32:49
    needed to be they would like to see more
  • 00:32:51
    of those capabilities in terms of the
  • 00:32:53
    graduates that are coming on to the
  • 00:32:55
    labor market again the importance of
  • 00:32:57
    micr finals as a as a as a as a valuable
  • 00:33:00
    tool to facilitate lifelong learning was
  • 00:33:04
    um and upskilling was emphasized by the
  • 00:33:05
    employers although there was concern
  • 00:33:08
    that perhaps these weren't being adopted
  • 00:33:10
    into the
  • 00:33:11
    qqi qualification framework as quickly
  • 00:33:14
    as as they would like and if if
  • 00:33:15
    obviously if these microc credentials
  • 00:33:17
    aren't widely recognized and aren't
  • 00:33:19
    transferable then people are less likely
  • 00:33:22
    to engage in
  • 00:33:24
    them um and again there was a need they
  • 00:33:26
    felt for IND and educational
  • 00:33:29
    institutions uh to to collaborate more
  • 00:33:32
    more more effectively and more
  • 00:33:33
    intensively in order to smooth the
  • 00:33:35
    transition uh from education to work for
  • 00:33:38
    for young people graduating in these
  • 00:33:42
    areas so in terms of uh just to sum up
  • 00:33:46
    in terms of our conclusions um so what
  • 00:33:49
    we think we have um attempted to do here
  • 00:33:52
    is is developing a methodology that will
  • 00:33:54
    allow us to identify um new and emerging
  • 00:33:58
    technologies that are relevant
  • 00:34:00
    particularly in for the Irish labor
  • 00:34:02
    market as they emerge and as they evolve
  • 00:34:05
    over time a technology that is highly
  • 00:34:07
    Dynamic and that will allow us uh
  • 00:34:10
    to to update uh the forecast and feed
  • 00:34:13
    that information back into planning um
  • 00:34:17
    as things
  • 00:34:19
    evolve so I said the the objective then
  • 00:34:22
    why are we doing this we're doing this
  • 00:34:23
    so we have a system of in terms of lab
  • 00:34:26
    Market intelligence and foresight
  • 00:34:28
    uh that is dynamic uh that that can
  • 00:34:31
    then translate into changes in in both
  • 00:34:35
    the level and the nature of provision
  • 00:34:37
    that will hopefully um help avoid skill
  • 00:34:40
    shortages occurring in these key
  • 00:34:42
    emerging technologies that are likely to
  • 00:34:44
    be very important uh in terms of the
  • 00:34:47
    future and continuing Economic
  • 00:34:48
    Development as of the country as a
  • 00:34:54
    whole we think that um generally
  • 00:34:57
    speaking the research findings suggest
  • 00:35:00
    that Ireland is relatively well placed
  • 00:35:02
    to meet the demands for the emerging
  • 00:35:04
    technologies that we've looked at um at
  • 00:35:06
    the moment in terms of the level of um
  • 00:35:10
    the level of courses that are being
  • 00:35:12
    produced compared to what we think the
  • 00:35:13
    number of graduates um that that are
  • 00:35:16
    being required there are issues that
  • 00:35:18
    need to be dealt with in terms of the
  • 00:35:20
    regulatory environment and it's
  • 00:35:23
    important also both the evidence from
  • 00:35:25
    our lightcast theer and our consultation
  • 00:35:28
    uh to ensure that
  • 00:35:31
    um that the competencies that that
  • 00:35:35
    students and graduates have are not just
  • 00:35:37
    focused on the hard competencies but
  • 00:35:39
    also the the rising importance of the
  • 00:35:41
    technical and business competencies and
  • 00:35:43
    this is something that we've picked up
  • 00:35:45
    in our own research when we look at the
  • 00:35:46
    high technology changes labor
  • 00:35:49
    markets there were some catastrophic
  • 00:35:52
    sort of predictions that you know 50 60%
  • 00:35:54
    of jobs would be automated now we've got
  • 00:35:56
    the data what we can actually see is
  • 00:35:59
    that as technology is introduced it
  • 00:36:02
    tends to change the the content and the
  • 00:36:04
    skill competencies within jobs it tends
  • 00:36:06
    not to wipe jobs out uh and what tends
  • 00:36:09
    to happen is that the more manial and
  • 00:36:12
    basic tasks tend to get automated away
  • 00:36:15
    um so that there's greater need then
  • 00:36:17
    within jobs there's more focus on higher
  • 00:36:19
    level competencies and particularly
  • 00:36:22
    communication skills and transversal
  • 00:36:24
    skills then become much more uh
  • 00:36:26
    important in that cont
  • 00:36:28
    contexts and that was certainly
  • 00:36:29
    something um that was emphasized To Us
  • 00:36:33
    by the employers when we spoke to um
  • 00:36:36
    this is not a One-Shot finally this is
  • 00:36:38
    not a oneshot uh operation we just don't
  • 00:36:41
    run this once and walk away what we've
  • 00:36:43
    tried to do here is to develop a
  • 00:36:45
    methodology that for instance can't sit
  • 00:36:47
    in the department the analysis can be
  • 00:36:49
    rerun on a on an annual basis so the the
  • 00:36:54
    change in nature of a demand
  • 00:36:56
    particularly for a emerging Technologies
  • 00:36:59
    can be continually monitored both in
  • 00:37:01
    terms of the levels of jobs that are
  • 00:37:03
    being
  • 00:37:05
    um advertised but also the competencies
  • 00:37:08
    and the skills that are being requested
  • 00:37:10
    uh within within them so it's it's more
  • 00:37:12
    of uh the we feel that our contribution
  • 00:37:15
    here is goes beyond saying okay we're
  • 00:37:17
    okay in terms of the number of courses
  • 00:37:19
    um of or the number of degrees going out
  • 00:37:23
    in these areas over the next five year
  • 00:37:25
    period it's more about developing this
  • 00:37:27
    and taking it away uh and developing it
  • 00:37:30
    as a tool that hopefully sits within the
  • 00:37:31
    Departments as a monitoring framework
  • 00:37:34
    and also as a forite framework that will
  • 00:37:36
    help ensure that both provision in key
  • 00:37:40
    areas remains consistent um and meets
  • 00:37:43
    demand both in terms of the level of of
  • 00:37:47
    graduates the number of graduates get
  • 00:37:48
    into the labor market um and also um the
  • 00:37:51
    competencies that are required it also
  • 00:37:53
    helps you distinguish between the
  • 00:37:54
    competencies that are required for new
  • 00:37:56
    graduants from the with respect to those
  • 00:37:58
    with more experience um so distinguishes
  • 00:38:01
    between the so that would happen for
  • 00:38:03
    them in terms of curriculum development
  • 00:38:04
    and distinguish between the needs and
  • 00:38:06
    the competencies for new graduates as
  • 00:38:08
    opposed to those that would be better
  • 00:38:10
    placed within lifelong learning programs
  • 00:38:12
    or micro
  • 00:38:14
    credentials so yeah that's
  • 00:38:18
    that's all I have to say on the research
  • 00:38:21
    so I'll stop sharing now or maybe David
  • 00:38:22
    you can stop
  • 00:38:25
    sharing thanks so much jamus yeah great
  • 00:38:28
    those are those are the slides down uh
  • 00:38:31
    now so listen thanks a lot for that uh
  • 00:38:34
    really interesting
  • 00:38:36
    presentation um colleagues we do have
  • 00:38:39
    time for uh questions and for discussion
  • 00:38:43
    so please do um raise your your hand as
  • 00:38:47
    they say for those unfamiliar with zoom
  • 00:38:50
    the hand can be found under the react uh
  • 00:38:54
    button at the bottom of your screen um
  • 00:38:58
    so maybe while while we wait for uh for
  • 00:39:01
    questions to come through I might uh I
  • 00:39:04
    might take my um chair's uh privilege to
  • 00:39:08
    to to pose one myself um I suppose as
  • 00:39:13
    you say Sheamus it's uh really good to
  • 00:39:15
    see at a high level that I suppose the
  • 00:39:18
    system is is is well um positioned in in
  • 00:39:22
    this particular particular area I think
  • 00:39:25
    it's it it is also really um interesting
  • 00:39:28
    to see those kind of specific uh
  • 00:39:31
    competences um being uh detailed so well
  • 00:39:35
    the ones that are called called out by
  • 00:39:38
    employers and I think that's kind of
  • 00:39:40
    really helpful uh content for for
  • 00:39:44
    colleagues around the call I suppose I'd
  • 00:39:47
    be interested in your thoughts on we we
  • 00:39:50
    hear about kind of the you know move to
  • 00:39:53
    say a uh as they say it a skills first
  • 00:39:58
    approach and if we take kind of
  • 00:40:00
    artificial intelligence as an example
  • 00:40:03
    like you say there's kind of the
  • 00:40:05
    specialist roles for for AI but then
  • 00:40:09
    there's the you know varying roles
  • 00:40:12
    across varying or organizations and
  • 00:40:16
    companies that require you know some H
  • 00:40:20
    uh knowledge and familiarity with
  • 00:40:23
    artificial in intelligence do you have
  • 00:40:26
    any thought thought about you know
  • 00:40:28
    that's maybe a bit harder to capture
  • 00:40:31
    through our national skills
  • 00:40:33
    in intelligence it kind of maybe
  • 00:40:36
    requires us to move away from the
  • 00:40:38
    occupation uh focus and and perhaps have
  • 00:40:41
    a kind of
  • 00:40:42
    wider um view of the relevant courses
  • 00:40:46
    how how might that be incorporated into
  • 00:40:50
    what we
  • 00:40:52
    capture yeah thanks Nora um so those are
  • 00:40:56
    important part Point um and as you said
  • 00:40:59
    there's there's the issue here we're
  • 00:41:00
    we're just capturing the number of jobs
  • 00:41:02
    that are being advertised in these areas
  • 00:41:04
    so Specialists automation Specialists
  • 00:41:07
    and uh blockchain Specialists but that's
  • 00:41:09
    a very different question in terms of uh
  • 00:41:13
    how what are AI competencies what are
  • 00:41:15
    the level of AI competencies that are
  • 00:41:16
    being used in the Li Market and how are
  • 00:41:18
    they been distributed across occupations
  • 00:41:21
    so uh while this tool is useful it is
  • 00:41:23
    not sufficient to to answer everything
  • 00:41:26
    uh because there's a number of problem s
  • 00:41:27
    with using job vacancy data that we've
  • 00:41:30
    figuring out now at the moment first of
  • 00:41:32
    all even if you know the technical
  • 00:41:34
    competencies that are being required
  • 00:41:35
    within these jobs you don't know how
  • 00:41:37
    they're distributed uh so you know for
  • 00:41:39
    example that control systems 34% of jobs
  • 00:41:43
    um are asking that that that automation
  • 00:41:47
    that that competency is required but we
  • 00:41:48
    that doesn't mean to say that it
  • 00:41:50
    accounts for 34% of all techical tasks
  • 00:41:53
    that a person in automation does again
  • 00:41:56
    when we look at the transversal skills
  • 00:41:57
    and we look at the business skills we
  • 00:42:00
    can see what employers are asking for
  • 00:42:01
    those competencies but again we don't
  • 00:42:03
    know how they're distributed within jobs
  • 00:42:05
    how much time does uh people invol take
  • 00:42:09
    undertaking each of those tasks and what
  • 00:42:11
    is the relative sh of it and then as you
  • 00:42:13
    say um we don't know then to what extent
  • 00:42:17
    people are involved in terms of Noni
  • 00:42:19
    Specialists or non automation
  • 00:42:21
    Specialists or non-blockchain specialist
  • 00:42:23
    Specialists doing tasks within their
  • 00:42:26
    jobs that Rel to blockchain relate to
  • 00:42:28
    artificial intelligence and relate to AI
  • 00:42:31
    so in order to do really get a handle on
  • 00:42:33
    that okay you look at what the employers
  • 00:42:35
    are asking for but you need good data in
  • 00:42:37
    terms of the competencies that are used
  • 00:42:40
    within jobs currently and how and
  • 00:42:43
    particularly around digital skills so
  • 00:42:46
    and we don't have that I mean currently
  • 00:42:48
    we do have some information on that we
  • 00:42:50
    have uh the European skills and job
  • 00:42:51
    survey the second wave that was that's a
  • 00:42:53
    survey that was produced by cob looking
  • 00:42:56
    at digital competencies across European
  • 00:42:59
    countries and they ask very specific
  • 00:43:00
    questions around all you know from very
  • 00:43:02
    basic do you use do you use um Excel and
  • 00:43:07
    Word right up to you know what type of
  • 00:43:09
    computer programming do you do so we at
  • 00:43:11
    the moment we're able to look at all
  • 00:43:13
    those competencies at least for 2021 and
  • 00:43:16
    and look at the distribution of those
  • 00:43:17
    tasks both within the key occupations
  • 00:43:20
    but also in terms of the ICT occupations
  • 00:43:23
    and the engineering occupations but also
  • 00:43:25
    throughout the labor market and we can
  • 00:43:26
    compare how Ireland compares to other
  • 00:43:29
    countries in terms of the the
  • 00:43:30
    requirements for those digital
  • 00:43:32
    competencies um but you need to be
  • 00:43:35
    collecting that information regularly uh
  • 00:43:38
    on an annual basis and we don't do that
  • 00:43:40
    we have none of that data in Ireland at
  • 00:43:42
    the moment so um the points that you
  • 00:43:44
    raise are important and they're key for
  • 00:43:47
    for informing policy but we do not have
  • 00:43:49
    the infrastructure here at the moment to
  • 00:43:51
    deliver that information uh to policy
  • 00:43:54
    makers and that's a big problem
  • 00:43:57
    thanks so much for that H Sheamus yeah
  • 00:43:59
    that's that's certainly kind of an
  • 00:44:01
    ongoing conversation and I know for
  • 00:44:04
    example the national skills Council
  • 00:44:06
    newly reformed are kind of looking at
  • 00:44:10
    how uh intelligence is is is um is uh
  • 00:44:14
    collected across the system and I'm sure
  • 00:44:16
    that's that's part of their uh
  • 00:44:19
    conversation too um we have a hand up um
  • 00:44:23
    and I might ask you to introduce
  • 00:44:25
    yourself uh and then pose your question
  • 00:44:28
    thanks yeah so my name is J Connor and
  • 00:44:30
    I'm in uh in University of gway I'm part
  • 00:44:34
    of the adult ed activity there but also
  • 00:44:36
    part of physics uh so I'm very
  • 00:44:38
    interested in
  • 00:44:39
    manufacturing um so these Technologies
  • 00:44:42
    of Automation and certainly AI are very
  • 00:44:44
    relevant for manufacturing and
  • 00:44:45
    interesting to see that um I suppose
  • 00:44:47
    maybe my question is this you know that
  • 00:44:50
    most Technologies follow an S curve so
  • 00:44:53
    they're slow to take off at the start
  • 00:44:56
    then they accelerate through the rapid
  • 00:44:58
    adoption and then they saturate the
  • 00:45:00
    decline and we move on to a different
  • 00:45:02
    technology and I suppose my question is
  • 00:45:04
    are we in danger here of
  • 00:45:07
    underestimating the growth of these
  • 00:45:10
    Technologies like Ai and blockchain and
  • 00:45:13
    in a sense the more advanced
  • 00:45:15
    automation uh by just looking at today's
  • 00:45:18
    data and seeing how it progresses um
  • 00:45:21
    that's a great question Jord and that's
  • 00:45:22
    exactly why we do this so if we look at
  • 00:45:25
    today's data we see the picture as it is
  • 00:45:27
    today you know so this this this was the
  • 00:45:30
    data that was that was these were the
  • 00:45:32
    adverts and the jobs that employers were
  • 00:45:34
    at and the people and the numbers that
  • 00:45:36
    were required uh for 2021 2022 um but
  • 00:45:40
    the the the value with this approach as
  • 00:45:43
    opposed to using the historical labor
  • 00:45:44
    force data that's always a year and a
  • 00:45:46
    half behind is that if we feel that
  • 00:45:49
    things have changed even in a six month
  • 00:45:51
    period we can go straight in uh and we
  • 00:45:54
    can see actually has there been a jump
  • 00:45:56
    in the number of AI jobs blockchain jobs
  • 00:45:59
    automation jobs we can see exactly where
  • 00:46:02
    the where the occupations are that those
  • 00:46:05
    increasing jobs are um and we can then
  • 00:46:09
    update both the demand estimates for now
  • 00:46:11
    and the forecasts going into the future
  • 00:46:13
    we can update the competency information
  • 00:46:15
    that's been required so in a sense yeah
  • 00:46:18
    there is
  • 00:46:19
    a this approach actually is is is
  • 00:46:22
    dynamic in it we use contemporaneous
  • 00:46:25
    information on the basis of we we
  • 00:46:26
    recognize that things can change very
  • 00:46:28
    very radically but it is also approach
  • 00:46:30
    that is highly replicable uh and can be
  • 00:46:33
    updated at any point in order to if
  • 00:46:35
    there's an uptake in employment or an
  • 00:46:37
    uptake in jobs that can be spotted
  • 00:46:38
    straight away where you're not waiting
  • 00:46:40
    18 months for a historical uh uh data to
  • 00:46:44
    come out from the CSO so we feel that
  • 00:46:47
    that that is a a key value in using this
  • 00:46:49
    type of data approach the a but what I
  • 00:46:51
    will say is uh with the S curve again we
  • 00:46:55
    were commissioned by the European
  • 00:46:56
    commission this is going back in 2020
  • 00:46:59
    because the perception was that
  • 00:47:00
    blockchain for example was just going to
  • 00:47:02
    be you know there was going to be um a
  • 00:47:05
    massive growth in terms of blockchain uh
  • 00:47:08
    requirements going forward and what we
  • 00:47:10
    found actually was yeah there was a
  • 00:47:11
    little bit of a surge in 2021 and and
  • 00:47:14
    this is not just for then our forecast
  • 00:47:16
    using this technology we were able to
  • 00:47:17
    pick up the fact that those jobs have
  • 00:47:19
    actually had like the demand for those
  • 00:47:21
    jobs had actually fallen off um in 2022
  • 00:47:24
    and 2023 were were able to pinpoint um
  • 00:47:27
    the occupations where they demand had
  • 00:47:28
    fallen off and we were also able to see
  • 00:47:30
    that actually this happened across most
  • 00:47:33
    countries in the EU uh in the EU but we
  • 00:47:35
    looked at the exception where the demand
  • 00:47:36
    kept on Rising so we were able to spot
  • 00:47:39
    that um very very quickly so the the
  • 00:47:43
    fact that these Technologies are so fast
  • 00:47:45
    moving is why we need forecasting or
  • 00:47:48
    labor market intelligence tools that
  • 00:47:50
    uses Dynamic and current information so
  • 00:47:52
    they can pick those those changes up
  • 00:47:55
    okay thank you very much I just leave
  • 00:47:57
    one other comment I guess these emerging
  • 00:47:59
    Technologies are very significant
  • 00:48:01
    leverage factors you know a good
  • 00:48:03
    automation a good AI specialist in an
  • 00:48:05
    Enterprise will leverage several other
  • 00:48:08
    job growth so it's really important what
  • 00:48:10
    you're doing and I so just want to State
  • 00:48:13
    point and thank you very much I we
  • 00:48:14
    recognize J exactly that we we need to
  • 00:48:17
    be making sure that we're at least we're
  • 00:48:19
    meeting the demands for of employers and
  • 00:48:21
    More in these areas we you know we can
  • 00:48:23
    so it is back in the envelope
  • 00:48:25
    calculations it to some extent but at
  • 00:48:27
    least it is a calculation based on the
  • 00:48:29
    our assessments reasonable assessments
  • 00:48:31
    that are based on what we see in the
  • 00:48:32
    data that we feel that the numbers are
  • 00:48:34
    at least you know sufficient to meet the
  • 00:48:36
    requirements for new graduates but as I
  • 00:48:38
    said the problem actually when you look
  • 00:48:40
    at how skill shortages evolve it's
  • 00:48:42
    usually at the um at the at the at the
  • 00:48:45
    system at the at the two years plus
  • 00:48:47
    experience level within organizations
  • 00:48:49
    you know the more that that is really
  • 00:48:51
    where the the pinch points uh really
  • 00:48:53
    come in terms of of development okay
  • 00:48:56
    thank you very much okay cheers sir
  • 00:48:58
    thanks very much for that um we I think
  • 00:49:01
    we have time for one more uh question
  • 00:49:03
    and Susie if you could introduce
  • 00:49:05
    yourself please hi yeah I'm Susie Jarvis
  • 00:49:09
    from University College Dublin really
  • 00:49:11
    enjoyed the talk and the uh the
  • 00:49:14
    methodology is really fascinating the
  • 00:49:17
    question I had is um obviously there's a
  • 00:49:19
    lot of other data on LinkedIn um where
  • 00:49:23
    individuals talk about the skills they
  • 00:49:25
    have um so I wondered if LinkedIn would
  • 00:49:28
    let you scrape that data so that we
  • 00:49:31
    could actually get some evidence about
  • 00:49:33
    higher education closing skills gaps so
  • 00:49:36
    you would have the job information and
  • 00:49:39
    you would have what skills individuals
  • 00:49:41
    are claiming to have and it would be
  • 00:49:42
    very powerful information I think for
  • 00:49:44
    the higher education sector going
  • 00:49:46
    forward I agree sus and we have you know
  • 00:49:50
    we recognize that there is data in
  • 00:49:51
    LinkedIn that isn't in the lightcast
  • 00:49:53
    data for example that's why we we use
  • 00:49:55
    LinkedIn to measure skill shortages
  • 00:49:57
    because we we can see exactly how how
  • 00:49:59
    long the adverts are there for and how
  • 00:50:02
    many applications they were they're
  • 00:50:04
    getting there are strengths and
  • 00:50:05
    weaknesses across both data sets um so
  • 00:50:08
    the strengths in the like the LinkedIn
  • 00:50:11
    data and we did have good cooperation
  • 00:50:13
    with the people from LinkedIn in this
  • 00:50:14
    study is that they did provide more um
  • 00:50:17
    there's there's more information
  • 00:50:18
    captured within LinkedIn the problem is
  • 00:50:20
    in that that um actually the big problem
  • 00:50:23
    from our perspective is is that the jobs
  • 00:50:25
    in LinkedIn aren't linked to the
  • 00:50:27
    occupational framework um so when we did
  • 00:50:30
    our our blockchain work we did that
  • 00:50:32
    using LinkedIn data but we then we had
  • 00:50:34
    to figure out where the jobs then sat
  • 00:50:36
    within the occupational framework so
  • 00:50:38
    then we could feed it into the
  • 00:50:39
    forecasting methodology and that was
  • 00:50:42
    while we could do that for one
  • 00:50:43
    technology for Block it was very very
  • 00:50:45
    time consuming so I feel that LinkedIn
  • 00:50:48
    if LinkedIn can get its act together and
  • 00:50:50
    start and start matching the jobs to
  • 00:50:53
    where they sit within the occupational
  • 00:50:55
    system it's going to be of a lot more
  • 00:50:56
    more use for researchers and people
  • 00:50:58
    interested in how um jobs are evolving
  • 00:51:00
    over time but that that's the main
  • 00:51:03
    weakness of it at the moment but as you
  • 00:51:05
    said it does have a lot of very very
  • 00:51:07
    useful information and they were very
  • 00:51:08
    Cooperative with us and we do use a lot
  • 00:51:10
    of their data so so when you look at the
  • 00:51:12
    report we do exploit a lot of their data
  • 00:51:14
    within
  • 00:51:15
    that great thank you thanks so much for
  • 00:51:18
    that um we we could squeeze in one uh
  • 00:51:22
    one final quick question if um anyone
  • 00:51:24
    wants to put up their hands um in the
  • 00:51:27
    meantime I'll just remind uh colleagues
  • 00:51:30
    that we will be sharing the recording of
  • 00:51:32
    today's webinar H Sheamus's slide and
  • 00:51:36
    also again the the link to the full uh
  • 00:51:40
    report which which has um lots of the
  • 00:51:43
    detailed
  • 00:51:44
    information um if there are no more
  • 00:51:47
    hands going up that's pretty good timing
  • 00:51:50
    for lunch time thanks everyone for
  • 00:51:54
    joining us um many many thanks again uh
  • 00:51:57
    to
  • 00:51:59
    Sham Dr Sheamus mcginness from the
  • 00:52:02
    esri for presenting your uh research
  • 00:52:07
    today fantastic to to disseminate this
  • 00:52:11
    uh more so across the universities and I
  • 00:52:13
    would absolutely encourage colleagues to
  • 00:52:16
    forward on to um interested uh
  • 00:52:19
    colleagues who will be interested in
  • 00:52:20
    seeing this um so thanks again and take
  • 00:52:24
    care
タグ
  • Lifelong learning
  • AI
  • Emerging technologies
  • Ireland
  • Skills data
  • Education system
  • Blockchain
  • Automation
  • Competence development
  • Job forecasting