'Big Data' TARGETjobs Breakfast News 28 November 2013

704 views

Published on

Published in: Technology, Economy & Finance
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
704
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Ri5/wikijobs. ¾ of students think an internship is essential to get a grad job (!)
    Actually, found the original data on Wikijobs site: 20 votes, of an unvalidated sample. We don’t know who voted, or if they are even students…
    Accountants: It’s a survey on/from a job board. One wonders what the other 60% are signed up for?
  • Ri5/wikijobs. ¾ of students think an internship is essential to get a grad job (!)
    Actually, found the original data on Wikijobs site: 20 votes, of an unvalidated sample. We don’t know who voted, or if they are even students…
    Accountants: It’s a survey on/from a job board. One wonders what the other 60% are signed up for?
  • Hyperbolic imperative:
    Actually, if you have discovered something startling and imperative, the first thing you need to do is look for other data, and see if it supports or contradicts this research. Only very silly people take big decisions off one sample.
    Wild extrapolation:
    You can’t really do this extrapolation unless you have a carefully controlled sample of the whole. This is quite a big branch of research theory, and just “whoever clicked the link” doesn’t cut it… Political polls, for example, use clever techniques to ensure the sample is representative of income, gender, age, location etc etc.
  • First one: This is a daft question. Industry sectors make no sense to a 14 year old, and what on earth does “media” mean to them? Probably TV presenter or journalist, rather than telesales. Interests of this age group might be interesting, but let’s ask them a question they understand.
  • Hyperbolic imperative:
    Actually, if you have discovered something startling and imperative, the first thing you need to do is look for other data, and see if it supports or contradicts this research. Only very silly people take big decisions off one sample.
    Wild extrapolation:
    You can’t really do this extrapolation unless you have a carefully controlled sample of the whole. This is quite a big branch of research theory, and just “whoever clicked the link” doesn’t cut it… Political polls, for example, use clever techniques to ensure the sample is representative of income, gender, age, location etc etc.
  • ICM BBC poll, 1031 people, October 2013
  • People use graphical representations of data because numbers are scary… Remember graphical representations can be misleading
  • Sometimes these are wrong inadvertently, but always begin by considering if there might not be a vested interest at play here…
    Who commissioned the research, and what was their agenda? Does it happily co-incide with the results?
  • Keynes, but only according to Paul Samuelson, who is recorded saying it first…
  • 'Big Data' TARGETjobs Breakfast News 28 November 2013

    1. 1. OUR 30TH EVENT BIG DATA Avenue Thursday 28 November
    2. 2. AGENDA FOR TODAY Welcome and GTI update – Simon Rogers THE ECONOMIC FORECAST Dennis will (in keeping with the theme of the day) note a shift in the way that (Big) Data is used in economic forecasting; with economists moving away from big econometric models and adopting a more behavioural approach. THE ERA OF BIG DATA Lord Daniel Finkelstein OBE, Executive Editor and Chief Leader Writer, The Times believes that communication and manipulation of data has driven social and political history. His view is that the era of ‘Big Data’ is just the latest stage in this development. DATA, DATA EVERYWHERE Stephen will share his plans for using data to support members’ recruitment strategies and will draw from a number of recruiter examples to demonstrate how data and research is currently being used to plan recruitment campaigns and evaluate their impact. “AND OUR SURVEY SAYS...” Marcus thinks there’s a big problem with the way graduate research is used: even if we’ve got the data, we don’t know what to do with it. He’ll talk through the way he evaluates research, and suggest ways to turn pages of data into pragmatic conclusions.
    3. 3. WE WANT TO SEE YOUR TWEETS! • #MrToast
    4. 4. GTI acquires Inside Buzz • Inside Buzz.co.uk offers students, graduates and young professionals an inside look at companies and careers based on interviews with 1000s of employees at the UK’s top companies • Existing employer content will be incorporated into the TARGETjobs ‘employer hubs’ alongside the existing employer profiles and our own company insights, helping provide the most comprehensive overview of an organisation • Opportunity for employers to enhance their existing profile and promote by surveying their recent graduate intake on matters as diverse as culture, hours, interview process, training and career prospects
    5. 5. TARGETjobs Premium Employer Hub • Allows employers to explain exactly why the best graduates should apply to them • Help shape your graduate recruitment programme via feedback from your recent graduate intake using an Inside Buzz questionnaire • Benchmark your organisation in up to 20 categories against the competition • In 2014 we are launching an internship/placement questionnaire
    6. 6. Our launch offer • Intro cost of £500 • • • • • • Survey set up Survey promotion help Data collection Employer collaboration Key content & reviews published on TARGETjobs Key findings / data available for employers
    7. 7. Record traffic – TARGETjobs NOW over 1 million
    8. 8. trendence UK Graduate Barometer • • • • • Brand new London-based research centre Dedicated UK research team 25,000 students will take part over next 6 months 400 employers A NEW diversity focus for 2014 – covering ethnicity, nationality, social mobility, gender… Bespoke reports Workshop Online tool As well as…..uniquely surveying students in a way that generates insights by year group, Russell Group vs non-Russell Group and by individual campuses, offering a bespoke competitor analysis and much more….
    9. 9. NEW trendence STEM female Student Barometer NEW trendence Law Student Barometer • 25 key law course campuses • 3000+ responses • Law & non-law students, cut by year group Competitor Analysis A NEW! Diversity focus Collecting 25% more responses from Law Students @ target group campuses and courses • Bespoke reports Workshop Online tool 50+ key STEM course campuses • 4000+ responses • STEM female students only, cut by year group Competitor Analysis A NEW! Diversity focus Line your firm up against the top 10 firms that STEM females most want to work for – and find out why!
    10. 10. National Graduate Employability Conference • • • The only employability conference in the UK to bring together 600–800 multidisciplinary undergraduates with recruiters and universities Keynote speaker announced shortly Facilitated by Radio 1’s Aled Haydn-Jones New 22 April at Wembley Stadium Main event sponsors • • Presentations, cross-sector panel debates, interactive mixed-table discussions and networking sessions Sponsorship opportunities available including hosting your own table of students from your target course area
    11. 11. THE ECONOMIC FORECAST Dennis Turner, former chief economist, HSBC Bank plc
    12. 12. OSBORNE’S MISSED TARGETS
    13. 13. The growth shortfall 4 2013 Budget Annual GDP growth forecasts in each Budget 2012 Budget 3 2011 Budget 2010Budget %2 1 0 2010 2011 2012 2013 2014 2015
    14. 14. …means more borrowing 160 Public sector net borrowing (£bn) 150 2010 Budget 140 2011Budget 130 120 2012 Budget 110 2013 Budget 100 90 80 70 60 50 40 30 20 2010 2011 2012 2013 2014 2015 2016
    15. 15. …and higher debt levels 85 85 80 % of GDP 90 80 75 75 70 70 Public sector net debt: 65 2010 Budget 2013 Budget 2011 Budget 65 2012 Budget 60 60 55 55 50 50 2009/10 2011/12 2013/14 2015/16 % of GDP 90
    16. 16. THE FAILURE OF FORECASTING
    17. 17. GDP Forecasts 2013 2.2 2.0 1.8 Highest % annual growth 1.6 1.4 1.2 1.0 Median 0.8 0.6 0.4 Lowest 0.2 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
    18. 18. Fixed investment forecasts, 2013 7 6 Lowest 5 Median 4 Highest 3 2 1 0 Jan Mar May Jul Sep Nov -1 -2 -3 -4 -5 -6 -7
    19. 19. Export forecasts, 2013 7 7 Highest Median Lowest 6 5 6 5 4 3 3 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Annual % change 4
    20. 20. WHERE TO IN 2014 – THE CONSENSUS
    21. 21. Forecasts for 2014 - GDP 3 Highest Lowest % 2 1 0 GDP
    22. 22. Forecasts for 2014 – Consumer spending 5 Highest 4 Lowest 3 % 2 1 0 GDP Cons Exp
    23. 23. Forecasts for 2014 - Investment 12 Highest 9 Lowest % 6 3 0 GDP Cons Exp Invest
    24. 24. Forecasts for 2014 - Exports 12 9 Highest Lowest % 6 3 0 GDP Cons Exp Invest Exports
    25. 25. Forecasts for 2014 - Inflation 12 9 Highest Lowest % 6 3 0 GDP Cons Exp Invest Exports Inflation
    26. 26. Forecasts for 2014 - Unemployment 12 Highest Lowest 9 % 6 3 0 GDP Cons Exp Invest Exports Inflation Unemploy.
    27. 27. WHERE TO IN 2014 – MY VIEW
    28. 28. but now likely to ease 6 Forecast % change month on month 5 4 3 Target 2 Range 1 0 CPI RPI -1 -2 2009 2010 2011 2012 2013 2014
    29. 29. So interest rates to stay low 7 6 Forecast 5 % 4 3 2 1 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    30. 30. and sterling to remain competitive 2.1 1.5 2.0 US$ / £ (L axis) 1.4 Forecast 1.9 1.8 euro / £ (R axis $/£ 1.3 ) €/£ 1.7 1.2 1.6 Sterling weaker 1.1 1.5 1.4 2005 1.0 2006 2007 2008 2009 2010 2011 2012 2013
    31. 31. Where is growth coming from? GDP (100%) = Consumer spending (64%)
    32. 32. A slow consumer recovery 5.5 4.5 3.5 Forecast % CHANGE 2.5 1.5 0.5 -0.5 Consumer spending growth (%) -1.5 -2.5 -3.5 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
    33. 33. Where is growth coming from? GDP (100%) = Consumer spending (64%) + Investment (15%)
    34. 34. …but not spending 140 120 Investment by Private Non-financial Corporations 110 130 Level of investment (R axis) 100 120 110 100 % 90 80 80 70 70 60 60 50 40 50 2001 2003 2005 2007 2009 2011 £ billion Investment relative to post-tax surplus (L axis) 90
    35. 35. Investment to pick up……at last 24 Business Investment 20 Forecast – OBR 2013 16 % annual growth 12 8 4 0 -4 -8 -12 -16 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
    36. 36. Where is growth coming from? GDP (100%) = Consumer spending (64%) + Investment (15%) + Government spending (23%)
    37. 37. Government continues in deficit 180 12 Net borrowing (L axis) 150 10 % of GDP* (R axis) 120 8 £ bn % 90 6 60 4 30 2 0 0 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016-17 2017-18
    38. 38. Where is growth coming from? GDP (100%) = Consumer spending (64%) + Govt consumption (23%) + Investment (15%) + Net trade (-2%) (Exports 30% – Imports 32%)
    39. 39. Trade becomes a plus for growth 5 9 4 Annual export growth (% RHS) 6 3 1 0 0 -3 -1 -2 -6 -3 -9 -4 BALANCE OF PAYMENTS DEFICIT (LHS) -5 -12 2009 2011 2013 2015 2017 Annual % change % of GDP 3 Annual import growth (% RHS) 2
    40. 40. TURNING THE CORNER
    41. 41. Sluggish growth as good as it gets 3.0 Long-term average 2.0 QUARTERLY 1.0 Forecast ANNUAL 0.0 -1.0 % -2.0 -3.0 -4.0 -5.0 -6.0 -7.0 2007 2008 2009 2010 2011 2012 2013 2014
    42. 42. Thank you
    43. 43. THE ERA OF BIG DATA Lord Daniel Finkelstein OBE, Executive Editor and Chief Leader Writer, The Times
    44. 44. Data, Data Everywhere Stephen Isherwood, CEO, Association of Graduate Recruiters
    45. 45. Data, Data Everywhere • UK Graduate market is not short on data points – Salaries and vacancies: AGR 22,000 vacancies – Student market research: trendence 400 employers – Destinations: HECSU 243, graduates – HE sector: HESSA 2,496,645 HE students – Application data: UCAS 653,600 university applicants
    46. 46. What external data can tell a recruiter • • • • • Gender split by subject studied UCAS tariff by institution and course Race profile by university The career preferences of graduates What jobs graduates end up doing
    47. 47. What internal data can tell a recruiter • • • • • • • • • • • Are relevant or non-relevant students more or less successful in your business? Does a UCAS tariff really predict success in your organisation? Is your selection process efficient, biased, cost effective? Drive business engagement Link strategy to business needs Help set level of investment Set benchmarks to measure your team’s performance Allows you to reward success Enables you to map the candidate experience Helps drive efficiency improvements Measures competitiveness
    48. 48. The business case for more budget Why spend money on graduate recruitment when there are 360,000 students and 83 applicants per vacancy?
    49. 49. The business case for more budget Graduated with a 1st or 2:1 240,000 (HESA) 24% have AAB+ 57,600 (UCAS) Minus 14% going to further study 49,600 (DLHE) 7% want accounting/financial services3,472 (trendence) Professional services vacancies 5,300 (AGR) Shortfall 1,828
    50. 50. The business case for better interviewers Applications 100 Testing 60 1st IV 30 Assessment centre 12 Offer 7 Offer accepts 6 100 60 30 17 7 5
    51. 51. “In God we trust, everyone else bring data” Michael Bloomberg
    52. 52. “AND OUR SURVEY SAYS...” Marcus Body, Head of Research, Work Group
    53. 53. Research in the War for Talent… “A great part of the information obtained in war is contradictory, a still greater part is false, and by far the greatest part is of a doubtful character.”
    54. 54. An imperfect storm. We have more reliable data… …so we rely more on data We have more unreliable data too …and sometimes we’re relying on that. Critical analysis is more important than ever.
    55. 55. The critical eye: • Who did the research? • What is their commercial/political interest? • Do you trust their integrity? • Do you trust their competence? • Have they shared the source data? • Have they shared the method?
    56. 56. The wrong researcher Survey reveals a flawless red carpet look can be yours for just £323 20% of men self-conscious about beach body on holiday Single men change bedsheets only 4 times per year Boring Facebook statuses named as number one annoying internet habit
    57. 57. The wrong researcher – the tell-tale signs Have they stated: •Why they did the research? And is there an obvious answer, with an obvious outcome they set out to find? •The number of participants? If not, keep your eyes peeled for suspiciously small-number percentages (e.g. 16.6%, 12.5%) •When the survey was conducted? Is this recycled “old news” and is it still relevant? •What format the survey took? Face-to-face/online. When? How?
    58. 58. The critical eye: Who EXACTLY did they ask? How did they source this group? What incentives were offered? Are this group relevant to your interests? If partially, what proportion?
    59. 59. The wrong sample 1) The very big… “43% of students at UK Universities…” 2) The very small… “A focus group of 12 economists at Nottingham…” 3) The “not actually students any more”… “67% of last year’s intake now say…”
    60. 60. A few simple questions… 1) Does this sample contain my ideal candidates? If yes, how large a proportion are they of it? 2) Are they likely to be consistent with this group? Am I looking for exceptional individuals, or typical ones? 3) Could I do something better? Or something that will “check” the validity of this sample?
    61. 61. The wrong source/audience
    62. 62. The critical eye: What EXACTLY did they ask? Is this really what you wanted to know? If partially, to what extent? What answer options were given? Have they released ALL answers?
    63. 63. The wrong question 1) The “no alternative”… “Is a company’s environmental record important to you?” 2) The “ridiculous specificity”… “On a scale of one to ten, how important is training?” 3) The “unreasonable expectations”… “Which FMCG employer are you most likely to apply to?” 4) The “Where’s my answer option?”… “Which of the following do you use to…?”
    64. 64. Open vs closed questions Closed questions (e.g. rankings/scores/options) are quick and easy to answer, and quick and easy to analyse. But you sacrifice understanding exactly what people thought. Open questions (typed answers) give you a much fuller insight into what people really thought, but are much more time-consuming and complex to process and analyse.
    65. 65. The wrong question – an example A survey of 14-15 year olds:
    66. 66. The critical eye: Does the data ‘prove’ the conclusions? Does the data support the conclusions? Are there other interpretations? What about those prior interests? Can you find supporting results elsewhere? Can you find alternative analysis elsewhere?
    67. 67. The wrong conclusion 1) The hyperbolic imperative “SOMETHING MUST BE DONE…!” 2) The wild extrapolation “50% of my survey, so 50% of everyone…” 3) The iffily corroborated “This backs the significant body of opinion that says…” 4) The downright dishonest “My survey says you should buy my product/service”
    68. 68. An example of selective analysis…
    69. 69. “…most people think public services are as good or better”
    70. 70. A quick regraph of the data…
    71. 71. “BBC austerity survey: why the public is wrong this time” “Every now and again, an opinion poll will be published which appears to show that most people don't know what they're talking about. A fairly typical headline in this spirit is "British public wrong about nearly everything, survey shows". In that case, the public's ignorance on issues such as welfare, crime and immigration favoured the government. And the latest poll from the BBC about public services under the Tories could represent something similar. Most people when asked about the state of hospitals, schools, colleges, GP surgeries, and so on either think they have stayed the same, or are getting better.” “The majority of people would not directly experience those cuts, and their effects are unlikely to be detected when the poll asks mainly about the consumption of key infrastructure.”
    72. 72. Actually, the Guardian are wrong too…
    73. 73. A general word on charts…
    74. 74. A matter of perspective 3D is for the cinema, not for good data representation. Watch out for colour and data labels too…
    75. 75. We love straight lines…
    76. 76. xkcd.com/1007
    77. 77. Correlation and causation Correlation is a measure of relationship between two mathematical variables or measured data values and is a mathematical property. Causation is the relation between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first, and is a philosophical concept explored at length by Aristotle.
    78. 78. xkcd.com/552 Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'.
    79. 79. Be careful of assuming an answer “Interns don’t join us as graduates, so they must not enjoy the internships. Can we survey what they don’t like?”
    80. 80. AND OUR SURVEY SAYS… RESEARCH?. . . . . 38 WHO DID THE WHO WAS ASKED? . . . . . . . . 27 WHAT WERE THEY ASKED?. . . . . 20 HOW WAS IT ANALYSED? . . . . . 15
    81. 81. One final warning… Confirmation bias: •Ignoring evidence which doesn’t fit your view. •Over-rating evidence that says you’re right. “When I find new information, I change my mind. What do you do?” Keynes (possibly) “Intelligence is the ability to adapt to change” Hawking
    82. 82. Get in touch marcus.body@workcomms.com 020 7492 0057
    83. 83. 2014 Dates for TARGETjobs Breakfast News 2014 will be available soon. See you next year!

    ×