Big Data Analytics Report Jan2013

7/29/2019 Big Data Analytics Report Jan2013

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Report or:

Big Data AnalyticsAn assessment o demand or labour and skills, 2012-2017

January 2013


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Big Data Analytics: An assessment of demand for labour and skills, 2012-2017

About e-skills UK

e-skills UKis the Sector Skills Council or Business and Inormation Technology

working on behal o employers to develop the software, internet, computer

gaming, IT services and business change expertise necessary to thrive in

todays global digital economy. Focused on making the biggest contribution to

enterprise, jobs and growth across the economy, e-skills UKs three strategic

objectives are to: inspire uture talent, support IT proessionals, increase digital

capability. Delivery on these strategic objectives is underpinned by employer

engagement across the sector, authoritative research, a continually developing

sector qualications and learning strategy and eective strategic partnerships.

About SAS

SAS is the leader in business analytics sotware and services, and the largestindependent vendor in the business intelligence market. Through innovative solu-

tions, SAS helps customers at more than 55,000 sites improve perormance and

deliver value by making better decisions aster. Since 1976 SAS has been giving

customers around the world THE POWER TO KNOW.

About our partners:

IT Jobs Watch provides a concise and accurate map o prevailing trends in

demand or IT sta within the UK. This is achieved by collating and analysing

related vacancy statistics sourced rom leading IT recruitment websites and pre-

senting the associated results in a reely available, ully searchable web applica-

tion, which is updated on a daily basis to ensure users have access to the very

latest inormation. Our services are employed by a variety o clients including

job seekers, careers specialists, recruitment agencies and employers who use

either our standard and/or bespoke services to, or example, measure demand

or specic skills or specialisms, identiy the skills needed by specic IT jobs, and

assess remuneration levels or IT positions. For urther inormation, please visit us

at: www.itjobswatch.co.uk.

Experian has an Economics Group o more than 40 economists and consultants

who specialise in global macroeconomic, regional and local area orecasting. We

have more than 20 years experience in economic orecasting and in recent years

have been consistently ranked above our peers in terms o orecasting accuracyby associated Sunday Times polls. We provide a suite o subscription products,

bespoke consulting services and seminars or clients across a broad range o

sectors in a growing number o countries. Our core expertise extends to a number

o key sectors, including retail, property, nancial services, public sector and IT.

For urther inormation, please visit us at: www.experian.co.uk/economics.


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Foreword

This report has been produced by e-skills UK on behal o SAS UK. It aims to provide an understanding o the develop-

ing demand trends or big data related sta in the UK, ocusing in particular on demand arising within the IT unction o

UK businesses.

Though the ndings presented within the report are the views o e-skills UK alone, we would like to oer our thanks to

two organisations that have worked very closely with us on this project. Firstly, IT Jobs Watch, who has laboured hard

to produce a bespoke set o demand data based on our specied denitions, and, secondly, Experian, who has worked

with us to develop the generic/IT specic employment orecasts and big data demand estimates.

We would also like to thank those who have responded to our ad hoc queries or background inormation about the big

data eld and related developments in the UK.


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Contents

Executive Summary............................................................................................................................................. 5

1 Background.................................................................................................................................................... 8

1.1 Overview o recent big data studies .......................................................................................................... 8

2 Study Parameters ....................................................................................................................................... 10

2.1 Methodological overview ........................................................................................................................ 10

3 Big Data Demand Trends ........................................................................................................................... 12

3.1 Demand overview ................................................................................................................................... 12

3.2 Demand by contractual status ................................................................................................................ 13

3.3 Demand by sector.................................................................................................................................. 13

3.4 Demand by salary ................................................................................................................................... 144 Demand Trends By Role............................................................................................................................. 15

4.1 Overview o big data demand trends by role ........................................................................................... 15

4.2 Demand by role and contractual status................................................................................................... 15

4.3 Big data Developers ............................................................................................................................... 17

4.4 Big data Architects ................................................................................................................................. 18

4.5 Big data Analysts.................................................................................................................................... 19

4.6 Big data Administrators .......................................................................................................................... 20

4.7 Big data Project Managers...................................................................................................................... 21

4.8 Big data Designers ................................................................................................................................. 22

4.9 Data Scientists ....................................................................................................................................... 24

5 Demand Trends By Skill ............................................................................................................................ 25

5.1 Overview o demand or related skills needs ........................................................................................... 25

5.2 NoSQL.................................................................................................................................................... 26

5.3 Hadoop................................................................................................................................................... 26

5.4 Overview o process/methodological skills demanded............................................................................ 27

5.5 Overview o generic/unctional knowledge requirements ........................................................................ 28

6 Future Demand ........................................................................................................................................... 29

6.1 Forecasting overview.............................................................................................................................. 29

6.2 Methodological details............................................................................................................................ 29

6.3 Forecast employment o IT&T sta 20122017......................................................................................... 30

6.4 Forecasting demand or big data positions 20122017 ............................................................................ 31

Appendix A: SOC............................................................................................................................................... 33

Glossary and Terminology............................................................................................................................... 34

End Notes ........................................................................................................................................................... 35


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Executive Summary

Background to the study Despitetheexistenceofmanyreportssettingoutthestateofbigdata

developments, there remains no single, internationally recognised deni-

tion o big data and no operational denition that can be employed when

seeking to understand/compare market/related developments.

InformationonthestateofbigdatadevelopmentintheUKislimitedand

commonly based upon ndings rom global studies, which, in turn, tend

to be biased towards the experiences o extremely large (oten US-based)

employers.

Whatisclearfromthesestudies,however,isthatthevolume,variety

and velocity o data is increasing rapidly and with it the recognition that

competitive advantage and new business opportunities may be achieved

through the successul development o capability in the eld o big data

analytics.

Wheninitiatinganynewbusinessventureoractivity,therewillbeanintrin-

sic need to attract/develop an associated skills base, and respondents to

many studies have voiced concern over the availability o big data skills

within the existing labour pool both at a global and UK level.

Thisreportseekstoaidthoseundertaking/supportingbigdataprojectsin

the UK by providing a detailed analysis o current/projected demand or big

data skills based on a) an analysis o recruitment advertising data and b)

bespoke orecasts o IT&T employment and big data demand or the com-

ing ve years.

Current demand for big data skills in the UK

Itisestimatedthattherewereapproximately3,790advertisedpositionsfor

big data sta in the UK in the third quarter o 2012, 75% o which were or

permanent posts.

Themostcommonlyadvertisedrolesforbigdatastaffwere:Developers

(42% o advertised positions), Architects (10%), Analysts (8%) and

Administrators (6%).

DataScientists,whilstrecognisedasbeinganimportantroleforbigdata

developments, was ound to constitute less than 1% o all big data posi-

tions advertised.

Thetechnicalskillsmostcommonlyrequiredforbigdatapositionsasa

whole were: NoSQL, Oracle, Java and SQL, whilst the technical process/

methodological requirements most oten cited by recruiters were in relation

to: Agile Sotware Development, Test Driven Development (TDD), Extract,

Transorm and Load (ETL) and Cascading Style Sheets (CSS).


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Ananalysisofskillsrequirementsfordifferentbigdatarolesshowedthe

specic/related technical knowledge and skills currently most in demand in

each case were as ollows:

o For big data Developers: NoSQL, Java, JavaScript, MySQL and Linux

together with TDD, CSS and Agile development knowledge.

o For big data Architects: Oracle, Java, SQL, Hadoop, and SQL Server

and Data Modelling, ETL, Enterprise Architecture, Open Source and

Analytics.

o For big data Analysts: Oracle, SQL and Java together with Data

Modelling, ETL, Analytics and Data Analysis.

o For big data Administrators: Linux, MySQL, Puppet, Hadoop and

Oracle along with Conguration Management, Disaster recovery,Clustering and ETL.

o For big data Project Managers: Oracle, Netezza, Business Objects

and Hyperion together with ETL, and Agile Sotware Development

PRINCE2 and Stakeholder Management skills are also a common

specied requirement in this case.

o For big data Designers: Oracle, SQL, Netezza, SQL Server, Inormatica,

MySQL and Unix plus ETL, Data Modelling, Analytics, CSS, Unit

Testing, Data Integration and Data Mining.

o For Data Scientists: Hadoop, Java, NoSQL and C++ along with

Articial Intelligence, Data Mining and Analytics. A high proportion o

adverts were noted also to make reerence to a need or Statistics and

Mathematics skills.

Onaveragethesalariesadvertisedforbigdatapositionswerearound20%

higher than those or IT sta as a whole and a pay premium was observed

or all comparable roles whether or permanent or contract positions.

Trends in demand for big data skills to date

Despitethecurrentlyunfavourableeconomicclimate,demandforbigdata

sta has risen exponentially (912%) over the past ve years rom less than

400 vacancies in the third quarter o 2007 to almost 4,000 in the third quar-ter o 2012.

Theoverallincreaseindemandforthespecictypesofbigdatastaff

analysed in this report ranged rom 178% or Project Managers to 3363%

in the case o big data Developers (1643% or big data Designers, 930%

or big data Administrators, 784% or big data Architects, 350% or Data

Scientists1 and 327% or big data Project Managers).

1 Annual change figure only between Q3.11 and Q3.12.


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Demandforpermanentandcontractstaffhasfollowedsimilargrowth

trends over the past ve years though demand or contractors lagged that

or permanent sta by around two quarters or much o this period.

Forecast changes in IT employment and demand for big

data staff

Overthenextveyears,employmentofIT&Tstaffisforecasttogrowby

around 2.5% per annum on average, a rate more than three times higher

than that predicted or UK employment as a whole.

Demandforbigdatastaff,bycomparison,isforecasttoincreasebyarate

o between 13% (low growth scenario) and 23% per annum (high growth

scenario) on average.

Takingamid-pointaverageofthesetwoscenarioswouldgiveanexpected

annual average growth rate o 18% per year (92% in total)2. This would be

our preerred scenario and would equate to the generation o approximate-

ly 28,000 gross job opportunities per annum by 2017.

Overthewholeforecastperiod,underthisscenariotherewouldbearound

132,000 gross job opportunities in total created in the big data eld within

the economy between 2012 and 2017.

2 Figures do not total due to rounding.


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1 Background

1.1 Overview of recent big data studiesSince the publication o the benchmark report on big data by the McKinsey

Global Institute in June 2011i a plethora o reports have been published over the

past year that have sought to dene the term big data, establish potential use/

benets, and orecast uture uptake within the business community. In view o

this large volume o readily available supporting research, we have elected not

to go into great depth about the benets/pitalls o big data adoption, taking

it as read that this is a well-identied emerging trend and one that has well-

recognised potential or business creation and development. It was thought

pertinent, however, to provide a brie overview o some o the generic ndings

arising rom research in this eld and to highlight some important caveats that

have tended to be overlooked by many o those reporting on big data develop-ments within the media/elsewhere.

Definitions

There is currently no singular, internationally recognised denition o what con-

stitutes big data. Many reports make reerence to the three Vs proposed in

2001 by the META Group,ii i.e. Volume (a reerence to data stores o petabytes

or above), Velocity (the requirement or real-time collection/analysis o data)

and Variety (generation o data in diverse ormats rom a variety o collection

mechanisms), and, in some cases, this denition has been urther expanded

to incorporate related considerations such as Variability (temporal data peaks)

and Complexity (issues relating to linking/cleaning/editing data rom dier-

ent sources)iii or example. In all cases, however, the terminology employed to

describe big data is not an operational one and, as such, cannot be used to

identiy a distinct sector, occupation, process, etc. In act, even the core terms

are highly subjective and liable to change in accordance with social/technologi-

cal developments.iv

Uptake

Despite the absence o a specic denition, companies have warmed to

the generic term big data and many research organisations have sought to

measure associated business adoption rates by way o primary and/or second-ary data collection activities. Reported adoption rates vary signicantly, and

in most cases observed are subject to signicant caveats not always readily

highlighted within the associated study documents. More specically, our main

concern relates to the manner in which much o the data has been collected

and the apparent absence o any weighting to the resulting survey response

set, i.e. data collection is typically by way o an open invitation web survey with

responses collected on a global basis, primarily rom very large organisations,

which, as a result will lead to the presentation o potentially infated rates o

adoption.v

There is no universally

recognised operational

defnition o big data


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Benefits

Adoption rates aside, the potential benets o utilising big data/related tech-

nologies are signicant both in scale and scope and include, or example:better/more targeted marketing activities, improved business decision making,

cost reduction and generation o operational eciencies, enhanced planning

and strategic decision making and increased business agility, raud detection,

waste reduction and customer retention to name but a ew. Obviously, the abil-

ity o rms to realise business benets will be dependent on company charac-

teristics such as size, data dependency and nature o business activity, though

businesses operating in the Financial Services, IT & Telecoms, Healthcare/

Pharmaceuticals, Retail and Public sectors are oten highlighted as being

potentially key beneciaries.

Data sources

Companies employing or looking to employ big data analytics are increas-

ingly drawing in data rom a diverse range o sources such as web logs,

clickstreams, social media, smart meters, machine sensors, CRM systems

and micro blogging sites like Twitter. It is this diverse and expanding range o

human/automated mechanisms or data capture that is driving the demand or

scalable, oten real-time systems able to deal with high volumes o structured

and semi/unstructured inormation.

Technologies/processes

The core technologies capturing the interest o those implementing big datasolutions tend to be ocused around Hadoop/sub-projects (Cassandra, etc.)

and the growing range o NoSQL databases. This said, it would appear that big

data solutions based upon SQL and other traditional architectures are cur-

rently the most common deployed systems or rms within the UK3.

Human issues

A core concern voiced by many o those participating in big data ocused

studies is the ability o employers to nd and attract the talent needed or both

a) the successul implementation o big data solutions and b) the subsequent

realisation o associated business benets4.

For e-skills UK, as the Sector Skills Council responsible or promoting IT skills

development in the UK, it is the last o these points that causes us particular

concern and, as such, we were extremely pleased to partner SAS UK on a

programme o research that would seek to a) dene the current/uture level

o demand or big data sta (presented within this report) and b) explore the

potential or demand/supply mismatches (by way o a urther study report) with

the aim o developing a series o recommendations to aid industry, individuals

and government to capitalise on the opportunities that big data presents.

3 See, for example: Computing research: how and why big data has hit the mainstream, 10 May 2012.

4 ibid.

Though Hadoop and NoSQL

are currently in the limelight,

frms are currently more likely

to use RDBMS to address their

big data needs

Most big data studies areun-weighted and ocused

on large US/multinational

businesses


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2 Study Parameters

2.1 Methodological overviewAs noted in the previous section there is, at present, no consistent, globally rec-

ognised, operational denition o what constitutes big data, big data employ-

ment or big data related activity in general. As such, a key task in the early

stages o the project was to produce an agreed, workable denition, which

would allow us to sensibly dene the parameters o our labour market analysis

whilst remaining cognisant o the limitations o related secondary data sources

upon which we would be reliant when undertaking our analysis/developing

orecasts or the uture.

To aid readers interpretation o the ndings presented in this paper, we have

summarised our thinking in this area and set out the related caveats employedwhen conducting our analysis:

i) The ocus o this report is to provide an understanding o the demand or

big data practitioners5 as opposed to big data users6. The reasons or this

are threeold: rstly, the realisation that the IT unction (i.e. in which practi-

tioners are generally employed) appears, at this time, to be the most com-

mon driver o big data related adoption/developments; secondly, attempts

to dene/quantiy the overall employment eects o big data adoption in

the UK have already been carried out by other research organisations7;

and thirdly, it is our opinion that more detailed analysis o demand or user

skills would not be easible considering the limited availability o required

(secondary) data or other occupations/proessions.

ii) More specically, the report is based upon an analysis o demand exhib-

ited by recruiters operating within the IT & Telecoms (IT&T) space, i.e. those

advertising or big data practitioners via some/all o the main associated

recruitment sites and/or portals this is once again due in part to the rec-

ognition o IT&T as a main driver or big data developments and in part to

the availability o detailed demand data or this recruitment sector. It is also

our belie that the majority o positions or both practitioners and power

users are, in any case, advertised either solely or jointly within this recruit-

ment space.

iii) At an operational level we have dened big data related demand as

instances in which a job advert makes reerence to either a) the specic

term big data, b) a job title deemed to be big data specic or c) a skill

deemed to be big data related. The denition has been developed accord-

ing to the ollowing logic:

5 Those involved in the design, development, maintenance, administration and support of big datasystems/services.

6 Individuals using big data/big data tools as a means of undertaking tasks associated with a differentoccupation, i.e. marketeers using big data analytics to perform customer segmentation.

7 Such as Cebr or the EIU for example in their respective reports: Data Equity: Unlocking the Value ofBig Data, April 2012, and Big Data: Lessons from the Leaders, 2012.

This report looks at thedemand or big data labour

and skills rom employers o

IT&T sta in the UK


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a. Adverts citing big data as the eld o work are included as this is the

common language o recruiters. It is recognised that in some cases

there may be a propensity or recruiters to include terms that are in

vogue. However, ollowing a preliminary analysis o related adverts, it

was determined that this would not have a major eect upon the result-

ing analysis as such instances appeared minimal in number.

b. To determine which job titles could be considered to be big data

related, an analysis o the top 500 commonly occurring titles within the

IT recruitment space was undertaken and a value judgement made as

to the likelihood that the positions on oer were a suitable t. In reality,

owing to the overlap with generic Analytic/Business Intelligence related

roles, this resulted in our selecting just one title Data Scientists or

inclusion within our denition.

c. To determine which skills were considered to be commensurate with

big data employment, an extensive background research exercise was

rst undertaken to identiy the common technical/related skills called

or. This listing was then considered by industry experts and cross-

reerenced with job titles commonly used by IT recruiters as an addi-

tional check. The resulting list o just under 40 technical skills was then

used as the primary identier o big data vacancies or our analysis

(i.e. together with cases citing big data and/or a requirement or Data

Scientists).

iv) In developing our orecasts o uture demand or big data sta, we elected

to base our model upon a dedicated series o IT&T employment orecastsprovided by Experian using a denition o IT&T occupations derived rom

11 specic occupational codes set out by the Oce or National Statistics

(ONS) Standard Occupational Classication system (SOC2010)8.

Further details o the methodology and, in particular, that relating to employ-

ment orecasts is contained within the related sections/appendix o the report.

8 See appendix A.


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3 Big Data Demand Trends

3.1 Demand overviewAlthough big data has been something o a media darling over the past year or

so, many would point out that, undamentally, big data has in act been around

or a much longer period o time albeit most likely under the banner o analyt-

ics and/or business intelligence. It is the growth in data volumes, together with

associated technological developments and declining relative cost o storage

retrieval and analysis, that has really pushed big data into the mainstream.

In act, as illustrated in the chart below, demand or big data sta has been a

readily identiable aspect o the IT recruitment market or at least ve years

albeit at levels well below those observed in the current period.vi

Demand or big data sta overall is thought to have increased by approximately

912% in total between the third quarter o 2007 and the third quarter o 2012,

with the number o advertised positions in this eld rising rom around 380 in

Q3.07 to 3,790 in Q3.12 an equivalent annual average increase o 182%.

Figure 1: Demand or labour and skills in the UK 20072012 (indexed)vii

Source: e-skills UK analysis of data provided by ONS/IT Jobs Watch

Whilst a remarkable growth gure in itsel, when refecting upon this demand

increase it should be remembered that this level o growth has been over a

period in which the UK economy has drited in and out o recession and one

in which demand or sta as a whole has declined by around 30% in total, or

6% on average per year. Even within the IT sector, where employment levels

have been quite resilient in the ace o a troubled economic climate, advertised

demand or sta has still declined over the past ve years, both as a whole and

or Data Warehousing/Business Intelligence specialists more specically.

Whilst overall demand or sta

has declined in the past fve

years, demand or big data has

grown by 182% per annum

Demand or big data sta has

outstripped that or IT sta in

general and Data Warehouse/

Business Intelligence sta more

specifcally

All sta (ONS)

IT sta (IT Jobs Watch)

Data Warehouse/Business Intelligencesta (IT Jobs Watch)

Big Data sta (e-skills

UK/ IT Jobs Watch)

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

200

180

160

140

120

100

80

60

40

20

0


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3.2 Demand by contractual status

The dramatic growth in demand or big data proessionals over recent years

has been apparent within the markets or both permanent and contract sta,

which have grown by 186% and 171% respectively per annum over the Q3.07

Q3.12 period as illustrated in the chart overlea. This chart also shows how

demand or contractors was seen to surge upward over the Q2Q3.09 period

(increasing by 102%) roughly two quarters prior to a similar jump in demand

within the permanent jobs market (where demand rose by 79% between the

nal quarter o 2009 and the rst quarter o 2010). Thereater, growth trends

appear to have continued on a similar path albeit with demand or contractors

two quarters in anticipation o that or permanent sta up until the third quarter

o 2011 (ater which they ell back into alignment ollowing a dip in the contract

recruitment market).

Figure 2: UK demand or big data sta by contractual status 20072012

(indexed)9

Source: e-skills UK analysis of data provided by IT Jobs Watch

Proportionally, however, demand or permanent big data sta has generally

been well in excess o that or contractors much as is the case or demand

more generally within the IT labour market (and the labour market as a whole)

with typically around 75% o advertised positions or big data jobs thought to

be o a permanent nature.

3.3 Demand by sector

Although it is not possible to provide a denitive analysis o the demand or big

data (or other IT) jobs by industry sector,viii it would appear that, where sector

reerences are made, these most oten relate to nance (reerenced in 21%

o adverts or big data sta), banking (7%), marketing (5%), games (3%), retail

(3%) and telecoms (3%).

9 Indexed in this case to the third quarter of 2007, which has a value of 100.

Around 75% o big data

positions advertised are or

permanent sta

Demand or big datasta (permanent)

Demand or big datasta (contract)

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

1,200

1,000

800

600

400

200

0


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3.4 Demand by salary

Although trend data or big data salaries is not currently available, gures or

the latest quarter indicate that big data sta are likely to achieve levels o remu-

neration signicantly higher than those oered to other IT specialists. More

specically the median advertised annual salary or big data sta in the third

quarter o 2012 was ound to be 21% higher than that or permanent IT sta

as a whole (i.e. with comparison gures o 52,500 and 43,500 respectively).

Advertised rates or big data contractors were also ound to be higher than the

norm, though in this case the dierence was smaller at just 6% in total (adver-

tised weekly median rates o 410 and 390 respectively).

Not only were advertised rates ound to be higher or big data sta as a whole,

they were also ound to be higher or each o the main roles analysed in the

course o this study and both with respect to permanent and contract posi-tions being recruited. In particular, this pay dierential between big data and

other IT sta was ound to be most pronounced or permanent IT Administrator

posts and contract positions or Designers as illustrated within the table below:

Table 1: Comparison o advertised rates or big data/other IT positions Q3.12


Advertised rates or big data

sta 21% higher than or other

IT workers

Advertised rates or big data

sta are higher or bothpermanent and contract

positions

Permanent positions

(median annual salary)

Contract Positions

(median daily rate)

Big

Data

All IT Delta Big

Data

All IT Delta

Developers 47,500 40,000 19% 400 380 7%

Architects 72,500 67,500 7% 500 480 5%

Analysts 43,250 40,000 8% 450 380 20%

Administrators 47,500 38,750 23% 390 320 21%

Designers 46,000 38,000 21% 480 350 35%

Data Scientists 52,500 - - 500 - -

Project Managers 55,000 53,000 4% 500 430 18%

All Vacancies 52,500 43,500 21% 410 390 6%


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4 Demand Trends by Role

4.1 Overview of big data demand trends by roleAlthough Data Scientist may currently be the sexiest job in big data,ix the

recruitment o Data Scientists (in volume terms at least) appears relatively low

down the wish list o recruiters at this time. Instead, the openings most com-

monly arising in the big data eld (as is the case or IT recruitment as a whole)

are Development positions, which accounted or approximately 42% o all big

data related job adverts during the third quarter o 2012. In contrast, postings

or Data Scientists, by comparison were thought to account or just 1% o big

data jobs at this time and 0.02% o total demand or IT sta.

Figure 3: Demand or big sta by role Q3.12


4.2 Demand by role and contractual status

The ocus on Development sta is equally apparent in both the permanent

and contract markets, where 40% and 44% o advertised positions respec-

tively in Q3.12 were or development positions. Variations in demand patterns

do exist between these two markets, however, and whilst 7% o permanentbig data jobs were or Analysts in Q3.12, the gure or contract positions was

almost double this level at 13%. Similarly, the proportion o permanent big data

Administrator jobs at 7% was more than twice that or contractors at that time.

Four in ten big data jobs

advertised are or developers

Big data recruiters o

permanent and contract

sta are seeking the same

specialisms

Developers

Administrators

Project Managers

Data Scientists

Designers

Analysts

Architects

Others

30%10%

42%

8%

1%1%

1%

6%


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Table 2: UK demand or big data sta by job title and contractual status Q3.12


The ocus on Development positions has not always existed, however, and ve

years earlier big data recruiters were more likely to have been seeking to ll

Analyst positions than Development posts, with the relative proportion o job

vacancies standing at 20% and 13% respectively during the third quarter o

2007 (i.e. compared with 8% and 42% during the latest quarter). The proportion

o big data jobs that were or Project Managers was also notably larger at that

time than at present (5% and 1% respectively) as illustrated within the chart

below.

Figure 4: UK demand or big data sta by job title status 20072012


Vacancy Numbers Vacancy ProportionsTotal Permanent Contract Total Permanent Contract

Developers 1,590 1,150 420 42% 40% 44%

Architects 380 300 80 10% 11% 8%

Analysts 310 190 130 8% 7% 13%

Administrators 240 210 30 6% 7% 3%

Designers 50 30 20 1% 1% 2%

Data Scientists 30 10 10 1% 0% 1%

Project Managers 50 30 20 1% 1% 2%

Others 1,140 910 250 30% 32% 26%

All Vacancies 3,790 2,840 950 100% 100% 100%

The initial recruitment ocus

or big data was or analyst

positions

Developers

Administrators

Project Managers

Data Scientists

Designers

Analysts

Architects

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%


17/36

| 17


The ollowing sections look at demand or these top level big data roles in more

detail, exploring in depth how demand has changed over the past ve years

both within the permanent and contract markets.

4.3 Big data Developers

i) Demand trends

Demand or big data Developers has risen by an estimated 3363% in total over

the past ve years (Q3.0712) the equivalent o an average annual growth

rate o 673% pa. As with demand or big data sta as a whole, this exponential

growth has been observed within both the permanent and contract job mar-

kets, which are characterised by related (average) annual increases o 584%

and 1093% respectively over the ve-year period.

By comparison, the demand or developers as a whole within the UK economy

is thought to have declined by around 41% in total or 8% per annum between

2007 and 2012, with a similar level o decline observed within both the perma-

nent and contract markets (down 8% and 9% per annum respectively).

Figure 5: Demand or Developers rom big data recruiters 20072012


ii) Common specialisms and key skills requirements

In a relatively small number o cases, big data recruiters will speciy a particu-

lar type o Development activity when advertising related positions and in the

third quarter o 2012 the most common o these were: Business Intelligence

Developer, Web Developer, Sotware Developer, Business Developer, Analyst

Developer, Applications Developer, Database Developer and Front-End

Developer (all eaturing in less than 10% o adverts or Developer jobs).

In the main, however, the generic title o developer is normally employed

together with a detailed description o the specic technical/related skills

required or the post and it is this description that denes the specic type

Demand or big data

developers has risen by an

estimated 673% per annum

over the past fve years

The top three skills required

o big data developers are

NoSQL, Java and SQL

Average number ovacanciesper quarter

2007 60

2008 100

2009 200

2010 410

2011 880

2012* 1,460

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0


18/36

| 18


o development activity undertaken. In the third quarter o 2012, the techni-

cal skills most oten cited by recruiters in adverts or big data Developers (in

order) were: NoSQL (MongoDB in particular), Java, SQL, JavaScript, MySQL,

Linux, Oracle, Hadoop (especially Cassandra), HTML and Spring all o which

eatured in 20% or more o adverts or big data Developers.

In addition, the generic areas o technical knowledge/competence oten

requested at this time included (in order) TDD, CSS and Agile Sotware

Development, all o which again eatured in 20% or more o adverts or big data

Developers10.

4.4 Big data Architects

i) Demand trends

On average, demand or big data Architects has increased by 157% per annum

over the past ve years (784% in total) with comparative increases o 143%

and 240% per annum respectively reported in the number o vacancies or

permanent and contract sta. By comparison, demand or IT Architects as a

whole was seen to all by 4% per annum on average over the period despite a

small increase in demand or contract sta to work in such positions (up by 3%

per annum or 13% in total compared with gures o -6% and -31% within the

permanent market).

As illustrated in the chart below, the increase in demand or contractors work-

ing as big data Architects tends to have outstripped that or permanent sta

(overall increases o 1200% and 716% respectively over the past ve years)

though the share o adverts or big data Architects has remained relatively

unchanged at around one in ten adverts in total.

Figure 6: Demand or Architects rom big data recruiters 20072012


10 See glossary for common acronyms.

Demand or big data architects

has risen by 157% per annum



2007 40

2008 30

2009 40

2010 120

2011 220

2012* 460

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

600

500

400

300

200

100

0


19/36

| 19



During the third quarter o 2012, the most common specialism or Architects

sought by big data recruiters were: Solutions Architects, Data Architectsand Business Intelligence Architects (specied within 26%, 17% and 12% o

adverts or big data Architects respectively).

More specically, however, applicants or these positions were required to hold

skills in a range o technical disciplines including (in order): Oracle (BI EE in

particular), Java, SQL, Hadoop and SQL Server (all eaturing in 20% or more o

adverts or big data Architect positions), whilst the main generic areas o tech-

nical knowledge/competence required were: Data Modelling, ETL, Enterprise

Architecture, Open Source and Analytics (all eaturing in 10% or more o related

job adverts).

4.5 Big data Analysts

i) Demand trends

Demand or IT Analysts to work with big data was seen to rise by 327% over

the past ve years (i.e. an annual average o 65%) with demand or permanent

sta increasing by 290% and that or contractors by 400% over the same peri-

od. Demand growth, though high, was less than that associated with the other

key big data occupations, however, and as a result the proportion o overall big

data demand accounted or by Analyst jobs has allen rom around one in ve

to one in ten vacancies over the past ve years.

Within the wider IT recruitment market by comparison there has been a decline

in demand or IT Analysts o around 10% per annum over the past ve years

with a similar change occurring within both the permanent and contract sectors

(i.e. 11% and 8% per annum respectively).

Though demand growth or contract big data Analyst jobs has been stronger

than that or permanent sta in recent years, the proportion o contract jobs

has remained largely unchanged at around 60% o the total a distribution

broadly in line with that o analyst positions as a whole (i.e. 41% o all adverts

or IT Analysts in Q3.12 were contract posts).

The top three skills required o

big data architects are Oracle,

Java and SQL

Demand or big data analysts

has risen by 65% per annum



20/36

| 20


Figure 7: Demand or Analysts rom big data recruiters 20072012



When considering demand or big data Analysts in more detail, it was ound

that the major proportion o related vacancies in the third quarter o 2012 were

or: Business Analysts, Data Analysts, Business Intelligence Analysts and

Support Analysts (30%, 20%, 19% and 11% o all big data Analyst positions

respectively), whilst the associated technical skills most in demand at this time

were: Oracle (particularly BI EE and Reports), SQL and Java (eaturing in 20%

or more o related adverts).

Particular process/methodological skills required rom applicants or Analyst

positions were primarily in respect o: Data Modelling, ETL, Analytics and Data

Analysis (all appearing in 10% or more o related adverts).

4.6 Big data Administrators

i) Demand trends

Demand or IT Administrators by recruiters seeking to ll big data posts has

increased by an average o 186% per annum over the past ve years (Q3.07

Q3.12) compared with a gure o -10% or IT administrators as a whole. Despitehaving one o the highest average annual growth rates, however, the proportion

o all big data vacancies arising in this eld has remained airly static over the

past ve years at around one in eighteen o all big data positions on oer (6%).

Unlike many o the other big data roles investigated, demand growth or perma-

nent sta in this area has tended to exceed that or contractors (averaging at

188% and 173% per annum respectively between Q3.07 and Q3.12) and, as a

result, the proportion o adverts or big data Administrators that are or perma-

nent sta has increased signicantly to around nine in ten (88%) jobs adver-

tised (compared with around just seven in ten or IT Administrators as a whole).

The top three skills required o

big data analysts are Oracle,

SQL and Java

Demand or big data

administrators has risen by186% per annum over the past

fve years

Average number o

vacanciesper quarter

2007 80

2008 90

2009 70

2010 150

2011 250

2012* 280

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

400

350

300

250

200

150

100

50

0


21/36

| 21


Figure 8: Demand or Administrators rom big data recruiters 20072012



Approximately 58% o Administrator positions advertised in the third quarter

o 2012 were or Systems Administrators (most oten specialising in Linux or

Unix) and 42% were or Database Administrators (most commonly working on

Oracle, SQL, Teradata and/or MySQL databases).

In general the technical skills most oten requested by employers rom big data

Administrators at that time were: Linux (79% o big data Administrator posi-

tions), MySQL (46%) and Puppet (39%), Hadoop (35%) and Oracle (31%), whilst

the process/methodological competences most oten requested were in the

areas o: Conguration Management, Disaster Recovery, Clustering and ETL

(appearing in between 10% and 21% o adverts).

4.7 Big data Project Managers

i) Demand trends

It is estimated that 1% o all big data vacancies in the third quarter o 2012 were

or Project Managers, a gure well below that or the IT recruitment market as a

whole (6%) at this time.

As with other big data roles, demand or Project Managers to work in this eld

has grown signicantly over the past ve years (36% per annum on average

and 178% as a whole) whilst demand or project managers more generally

has been in decline in the period (down by 51% in total or 10% per annum on

average).

Overall growth in demand or permanent and contract sta in this eld has

been similar at 32% and 44% per annum respectively and, by the third quar-

ter o the year, permanent vacancies accounted or 68% o the total demand

or big data Project Managers (compared with 53% within the IT recruitment

market more generally).


o big data administrators are

Linux, MySQL and Puppet

Demand or big data project

managers has grown by 178%


Average number o


2007 20

2008 10

2009 10

2010 40

2011 90

2012* 240

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

300

250

200

150

100

50

0


22/36

| 22


Figure 9: Demand or Project Managers rom big data recruiters 20072012



The specic types o Project Manager most oten required by big data recruit-

ers in 2012 to date11 have been: Oracle Project Managers, Technical Project

Managers and Business Intelligence Project Managers, which were cited within

25%, 14% and 12% o related big data vacancies (i.e. or Project Managers)

over the Q1Q3.12 period.

Aside rom Oracle (and in particular BI EE, EBS and EBS R12), which was spec-

ied in over two-thirds o all adverts or big data related Project Management

posts, other technical skills oten needed by applicants or this type o position

were: Netezza, Business Objects and Hyperion (eaturing in 9% or more o

related adverts). Process/methodological skills commonly required included:

ETL and Agile Sotware Development (cited in 30% and 10% o related adverts

respectively) together with a range o more business ocused skills, i.e.

PRINCE2 (22%) and Stakeholder Management (15%).

4.8 Big data Designers

i) Demand trends

As with Project Management positions, the number o big data vacancies or

Designers is relatively small at just 1% o the total, although, in this case, the

proportion was ound to be equal to that within the IT recruitment market as a

whole at the time o data collection (i.e. Q3.12).

11 A yearly total (Q1Q3.12) was employed owing to the small sample available for this role.


or work in big data project

management are Oracle,

PRINCE2 and Netezza

Demand or big data designers

has grown by 329% on average

in each o the past fve years

Average number o


2007 10

2008 20

2009 10

2010 40

2011 50

2012* 60

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q

3.0

7

Q

4.0

7

Q

1.0

8

Q

2.0

8

Q

3.0

8

Q

4.0

8

Q

1.0

9

Q

2.0

9

Q

3.0

9

Q

4.0

9

Q

1.1

0

Q

2.1

0

Q

3.1

0

Q

4.1

0

Q

1.1

1

Q

2.1

1

Q

3.1

1

Q

4.1

1

Q

1.1

2

Q

2.1

2

Q

3.1

2

70

60

50

40

30

20

10

0


23/36

| 23


An analysis o demand trends or related big data positions over the past ve

years12 shows an associated annual average growth gure o 329% and overall

growth o 1643% or the period (compared with an overall all o 49% and an

annual average all o 10% when considering positions or IT Design roles as a

whole).

During this period, demand or permanent Design vacancies has exceeded

that or contract sta by around 60% per annum on average (with associated

growth rates o (360% and 305% per annum respectively) and the proportion

o all big data Design vacancies that are permanent now stands at around six

in ten advertised positions (as is the case or design positions more generally in

the IT recruitment market).

Figure 10: Demand or Designers rom big data recruiters 20072012



As with Project Management positions and those or Data Scientists, due to

the limited number o vacancies advertised or big data Designers, the analysis

o related skills has been undertaken using combined gures or the rst three

quarters o the year. This analysis shows the most commonly requested techni-

cal skills associated with such posts to have been: Oracle (particularly BIEE)

and SQL (which both eatured in over one quarter o related adverts) ollowedby Netezza, SQL Server, Inormatica, MySQL and UNIX (apparent in 15% or

more o adverts).

Common process/methodological skills needed over the rst three quarters

o the year were: ETL, Data Modelling, Analytics, CSS, Unit Testing, Data

Integration and Data Mining, whilst more general knowledge requirements

related to the need or experience/understanding o: Business Intelligence,

Data Warehouse, Big Data, Migration and Middleware (cited in 10% or more

adverts in each case).

12 Data for the entire five-year period are not available.

The top three technical skills or

big data designers are Oracle,

SQL and Netezza


2007 -

2008 10

2009 10

2010 30

2011 30

2012* 40

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

60

50

40

30

20

10

0


24/36

| 24


4.9 Data Scientists

i) Demand trendsDemand or Data Scientists (as a denitive job title) was near non-existent prior

to 2011 and, despite the extremely high level o associated demand growth

recorded over the past year (i.e. 350% between the third quarter o 2011 and

that o 2012), the number o vacancies observed or this niche occupation

remain extremely small (i.e. less than 20 per quarter on average during 2012).x

A comparison o permanent/demand data (again or the rst three quarters o

2012) shows that around two thirds o Data Scientist positions advertised will

be o a permanent nature and this gure relates both to big data/the wider IT

sector being that all Data Scientists were thought to be working on big data

projects.

Figure 11: Demand or Data Scientists rom big data recruiters 20072012



The core technical skills needed to secure a position as a Data Scientist (based

on an analysis o all vacancies or the rst three quarters o 2012) were oundto be: Hadoop (Pig in particular), Java, NoSQL and C++ (all o which eatured in

30% or more o advertised vacancies).

As was the case or other big data positions, adverts or Data Scientists oten

made reerence to a need or various process/methodological skills and

competences. Interestingly however, in this case, such reerences were ound

to be much more commonplace and (perhaps as would be expected) most

oten ocused upon data and/or statistical themes, i.e. Statistics, Analytics and

Mathematics were all cited within 30% or more o adverts during the rst three

quarters o the year whilst Data Analysis, Articial Intelligence and Data Mining

were present within 20% or more related adverts during this period.

Demand or data scientists

has grown by 350% over the

past year

The top three technical skills

or data scientists are Hadoop,

Java and NoSQL


2007 -

2008 -

2009 -

2010 2

2011 3

2012* 16

* Average or

Q1-Q3 only

Contract vacancies

Permanent vacancies

Q3.0

7

Q4.0

7

Q1.0

8

Q2.0

8

Q3.0

8

Q4.0

8

Q1.0

9

Q2.0

9

Q3.0

9

Q4.0

9

Q1.1

0

Q2.1

0

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

30

25

20

15

10

5

0


25/36

| 25


5 Demand Trends by Skill

5.1 Overview of demand for related skills needsAs illustrated within the previous section, employers seeking to recruit sta to

big data jobs will typically speciy a need or a number o specic technical

skills along with a wide range o process/methodological skills.

Although the skill sets required will typically vary according to the occupa-

tion/job title in question, it was thought useul to provide an overview o how

demand or specic skills and competences has changed within the big data

labour market in recent years and, in particular, highlight changes in demand

or two o the newest yet perhaps most important emerging skill sets in this

eld, i.e. NoSQL and Hadoop.

The most commonly cited technical skills appearing in adverts or big data sta

during the third quarter o 2012 were NoSQL, Oracle, Java and SQL, each o

which eatured within at least 30% o associated recruitment adverts at that

time.

Figure 12: Demand or specifc technical skills rom big data recruiters

2007201213


As illustrated in the chart above, Oracle had been the most commonly required

skill or big data sta up until the second quarter o 2012, at which point

demand or related skills was exceeded by that o NoSQL. The chart also

shows how SQL, though appearing in a similar proportion o big data adverts

throughout the past ve years, is, like Oracle, now being surpassed by the

demand or NoSQL and other core skills associated with big data develop-

ments (i.e. Java on which Hadoop, or example, is based).

13 Note that percentages will not total 100% as vacancies may reference more than one skill.

Overall, NoSQL is now the

technical skill most oten

demanded by big data

recruiters

NoSQL

Oracle

Java

SQL

Linux

Hadoop

MySQL

JavaScript

UNIX

Python

Q

3.0

7

Q

4.0

7

Q

1.0

8

Q

2.0

8

Q

3.0

8

Q

4.0

8

Q

1.0

9

Q

2.0

9

Q

3.0

9

Q

4.0

9

Q

1.1

0

Q

2.1

0

Q

3.1

0

Q

4.1

0

Q

1.1

1

Q

2.1

1

Q

3.1

1

Q

4.1

1

Q

1.1

2

Q

2.1

2

Q

3.1

2

70%

60%

50%

40%

30%

20%

10%

0%


26/36

| 26


5.2 NoSQL

NoSQL (Not Only SQL) databases are next generation databases, oten non-

relational, distributed and open-source as well as being horizontally scalable.xi

The NoSQL database has emerged as a core requirement or employers seek-

ing to develop their capacity or big data and amongst the 150 or more variants

o NoSQL14, the two most commonly eatured within adverts or big data sta

in the UK during the third quarter o the year were MongoDB and, to a lesser

extent, CouchDB.

Figure 13: Demand or NoSQL skills rom big data recruiters 20102012


Over the past two years the increase in demand rom big data recruiters or

NoSQL skills has been phenomenal. Even in the case o CouchDB, which

exhibited the lowest rate o growth over the 2010-12 period, a 650% increase in

demand was recorded whilst or NoSQL as a whole an increase o 1600% was

observed. Even this gure was dwared, however, by the increase in demand

or MongoDB, which rose by 4200% between Q3.10 and Q3.12.

5.3 Hadoop

Another integral aspect o many big data developments is the adoption/integration o Apache Hadoop15 and related sub-components/projects, i.e.

Avro, Cassandra, Chukwa, HBase, Hive, Mahout, Pig, ZooKeeper, etc. Apache

Hadoop is an open-source sotware ramework that supports data-intensive

distributed applications running on large clusters o commodity hardwarexii

and, as such, it provides organisations with a cost-eective means o imple-

menting a scalable distributed computing solution to help address their big

14 http://nosql-database.org/

15 The Apache Hadoop project is open-source software (http://hadoop.apache.org/) allowing for thedistributed processing of large data sets across clusters of computers using simple programmingmodels. It is designed to scale up from single servers to thousands of machines, each offering localcomputation and storage.

Demand or NoSQL skills rom

big data recruiters has risen by

1600% in the past two years

Demand or Hadoop skills rom

big data recruiters has risen by

700% in the past two years


2007 -

2008 -

2009 -

2010 110

2011 400

2012* 1,200

* Average or

Q1-Q3 only

NoSQL

MongoDB

CouchDB

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

1,600

1,400

1,200

1,000

800

600

400

200

0


27/36

| 27


data development needs. For this reason alone Hadoop has quickly become a

core requirement or individuals pursuing a career in the eld o big data.

Like NoSQL, demand or Hadoop has increased dramatically in recent years

and, whilst there were only around 55 big data vacancies citing a requirement

or Hadoop in the third quarter o 2010, the number has increased by 700% by

the third quarter o 2012 to 820 positions in total. This said, demand or Hbase

(the associated NoSQL database component o Hadoop) has increased by an

even greater rate, rising by 2370% over the past two years.

Figure 14: Demand or Hadoop skills rom big data recruiters 20102012


5.4 Overview of process/methodological skills demanded

As mentioned throughout the last section, aside rom specic technical skills

requirements, big data employers will oten make reerence to the need or

technically related process/methodological skills/knowledge/experience which,

in the main, refect the act that a sizeable proportion o big data positions

advertised are or Development posts. Hence, it is unsurprising to nd that the

most common skills o this nature in the third quarter o the year related to Agile

Sotware Development and Test Driven Development (TDD).

Agile Sotware Development

skills cited in around 14% o all

adverts or big data sta


2007 -

2008 -

2009 -

2010 90

2011 230

2012* 730

* Average or

Q1-Q3 only

Hadoop amily

Cassandra

HBase

Hive

Pig

Q3.1

0

Q4.1

0

Q1.1

1

Q2.1

1

Q3.1

1

Q4.1

1

Q1.1

2

Q2.1

2

Q3.1

2

900

800

700

600

500

400

300

200

100

0


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Figure 15: Common process/methodological skills demanded by big data

recruiters Q3.12


5.5 Overview of generic/functional knowledge requirements

At a still higher level, applicants to big data positions will need an understand-

ing o broad principles involved with various business unctions/activities, and

an analysis o related vacancy data would suggest that this would be most

likely to arise with respect to Business Intelligence and big data in general.

Figure 16: Common unctional knowledge/skills demanded by big data

recruiters Q3.12


Business Intelligence

Big Data

Data Warehouse

Web Services

Analytics

E-Commerce

Internet

Social Media

Cloud Computing

CRM

SaaS

0% 5% 10% 15% 20% 25%

24%

20%

17%

12%

10%

7%

5%

3%

3%

3%3%

Agile Sotware Development

TDD (Test Driven Development)

ETL (Extract Transorm Load)

CSS (Cascading Style Sheets)

OO (Object Orientated)

MVC (Model-View-Controller)

Continuous Integration

Unit Testing

Project Management

0% 2% 4% 6% 8% 10% 12% 14% 16%

5%

6%

7%

8%

10%

12%

13%

14%

14%


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6 Future Demand

6.1 Forecasting overviewHaving developed an operational denition o big data within an employment

context and then using this as a basis or a detailed analysis o demand trends

to date, we then sought to develop a series o orecasts setting out the likely

uture demand in the UK or big data related labour and skills over the coming

ve years.

This component o the research exercise was carried out in association with

Experians Economics Group, who has integrated our big data demand data

with a series o bespoke orecasts o IT & Telecoms (IT&T) sta16 commis-

sioned specically or this project and, as a result, has been able to work with

us to generate a set o dedicated demand orecasts or big data occupations.

6.2 Methodological details

The initial element o the orecasting activity ocused on the generation o

related employment orecasts (i.e. IT&T employment) or the 20122017 period

based on an occupational denition derived rom relevant components o the

ONS Standard Occupational Classication system (SOC2010).

To produce these orecasts, Experians Regional Planning Service (RPS) rst

creates output and employment orecasts or the 38 industry divisions dened

by the ONS Standard Industrial Classication coding system (SIC2003), i.e. at2 digit SIC level. Using Index o Production (IOP) data rom the ONS, estimates

o consumer demand and intermediate demand and related trend data, a shit

share methodology is then employed to extrapolate results at a more detailed

level (i.e. SIC industry class/4 digit level).

The 4-digit orecasts are anchored to the higher level industry estimates to

increase robustness/ensure consistency and are then disaggregated by regions

using ocial employment data. The resulting regional estimates are then also

anchored at the broader industry level to increase robustness. The end result is

a set o 4-digit orecasts or each region that are ully consistent with Experians

broader industry orecasts, which are then subject to a SIC converter (rom

ONS) to produce equivalent orecasts using the latest version o the industry

classication system, i.e. SIC2007.

To translate these industry orecasts to occupation orecasts (SOC2010),

Experian has developed a dynamic matrix system, which maps industry

employment to occupations or the current/previous years. This matrix can

be extrapolated orward to 2017 using past trends and has been adjusted to

account or shits in occupational distributions observed between 2002 and

16 It was necessary to forecast IT & Telecoms employment as a whole, as available data from ONS(upon which forecasts are based) does not easily differentiate the two distinct groups of technicalspecialists. It was considered that this would not have a major effect upon related outputs, in part dueto the relative shares of employment but also due to the continued blurring of boundaries betweenassociated roles.

Bespoke orecasts o IT

employment have been

produced to guide the

generation o uture demand

estimates


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2010 under previous ONS classication systems, i.e. SOC2000 (which encom-

passes a longer time series). Hence, by applying this matrix to the regional

industry orecasts previously generated, a series o estimates or uture

employment by IT/other occupation can be derived covering the subsequent

ve-year period.

6.3 Forecast employment of IT&T staff 20122017

ONS estimates rom the Labour Force Survey (LFS) suggest that in 2011 there

were approximately 1.1 million people working in IT&T roles in the UK and

that, in total, IT&T sta accounted or approximately 3.8% o all employment

in the UK. The number o people working in IT&T positions is thought to have

increased by approximately 53,000 people over the past ve years (20062011

using annual comparisons) and the annual average growth rate (1.0%) is in

stark contrast to the decline exhibited or the UK as a whole (-0.2%).

Figure 17: Employment o IT&T sta17 in the UK 20072017

Source: e-skills UK/Experian

Over the 2012-2017 period, growth in IT&T employment is orecast, on average,

to increase at a more rapid pace (2.5% per annum) and by 2017 it is anticipated

that there will be approximately 1.3 million people employed in IT&T roles in

the UK (by comparison, growth in employment overall is orecast to be around

0.8% per annum or the UK labour market as a whole).

Near term employment growth or IT&T specialists is anticipated to be higher

or more senior roles, i.e. senior level managers and proessionals, whilst the

number o people employed in lower skilled IT&T positions will continue to con-

tract or, at best, remain static over the period.

17 By staff we mean both permanent and contract workers.

IT&T employment is orecast to

grow by 2.5% pa on average

over the next fve years

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

1,400,000

1,200,000

1,000,000

800,000

600,000

400,000

200,000

0


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6.4 Forecasting demand for big data positions 20122017

The second orecasting component o this project was the generation o

demand orecasts or big data positions based upon an analysis o histori-

cal recruitment (advertising) combined with the results o the dedicated IT&T

employment orecasting exercise.

When developing these orecasts, we have assumed that, given the skills

requirements or big data, related jobs should be captured within the IT&T

employment estimates/orecasts discussed and that advert vacancy statis-

tics can reasonably be employed to quantiy the gross number o big data job

opportunities arising in the uture.

We use the term gross job opportunity in the understanding that an advertised

position may arise as a result o a) a new post being created (growth) or b)someone leaving a job (replacement), e.g. to take up another post or to exit the

labour market entirely. When considering the two eects together (i.e. growth +

replacement), the result equals total gross job opportunities (job vacancies).

When developing our orecasts, we were also cognisant o the act that gross

job opportunities created by replacement tend to be much more numerous

than those created by expansion (our research shows a ratio averaging at

around 6:1 or IT&T positions) and that the net change in employment can be

either positive or negative. Lastly, and perhaps most signicantly, it is worth

bearing in mind that the uture growth in the number o big data job opportu-

nities may not continue at the growth rates we have seen beore historical

demand series and adoption rates tend to relate to very limited time periods

and/or specic circumstances (e.g. actions o major employers) and, hence,

it is by no means certain at what point o the adoption curve companies, as a

whole, are likely to have reached. With this in mind we have decided to produce

three growth scenarios:

1) A high growth scenario, which assumes the big data industry is still at the

early majority stage where adoption o the new technology will continue to

rise or a urther two years beore reaching the late majority stage where

growth in adoption rate is expected to slow.

2) A low growth scenario, which assumes that the industry has moved urtheralong the adoption curve and, as such, adoption rates and employment

demand will slow considerably in comparison with the recent past.

3) A medium growth scenario, which ollows a path o growth midway

between the two cited above.


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Figure 18: Actual/orecast demand (vacancies per annum) or big data sta

20072017

Source: e-skills UK/Experian

Under the high growth scenario, big data demand (vacancies) is expected to

grow by 117% over the coming ve years the equivalent o an annual average

growth rate o 23% cent per year. Accordingly, the gross job opportunities or

big data related jobs would be approximately 32,000 per annum by 2017 and

over the whole orecast period there will have been around 146,000 big data

gross job opportunities created in the economy.

Under the low growth scenario, job vacancies would be expected to grow at a

rate o 13% per year on average (65% in total) with gross job opportunities at

a rate o 24,000 per annum by 2017. Over the whole orecast period, under this

scenario there would be around 118,000 gross job opportunities in total created

within the economy between 2012 and 2017.

Under the medium growth scenario (the avoured o the three scenarios

presented), job vacancies would be expected to grow at a rate o 18% per

year on average (92% in total) with gross job opportunities at a rate o 28,000

per annum by 2017. Over the whole orecast period, under this scenario there

would be around 132,000 gross big data job opportunities in total created in the

economy between 2012 and 2017.

Demand or big data sta is

expected to grow by 92% over

the next fve years.

Scenario 1: High growth

Scenario 2: Low growth

Scenario 3: Medium growth (preerred option)

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0


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Appendix A: SOC

The Standard Occupational Classication (SOC) system has been developed

by ONS to provide a common methodology or the classication o occupa-

tions in the UK based upon associated skill levels and skill content.

SOC is based on a hierarchical system, starting with 9 high level, single-digit

codes (SOC major groups) which are then sub-divided into 25 more detailed

two-digit classications (SOC sub-major groups), 90 three-digit codes (SOC

minor groups) and nally 369 our-digit (SOC Unit) codes.

When developing our orecasts o employment or IT&T occupations, we

dened this group at the most detailed level possible using the ollowing our-

digit unit codes:

1136 Inormation technology and telecommunications directors

2133 IT specialist managers

2134 IT project and programme managers

2135 IT business analysts, architects and systems designers

2136 Programmers and sotware development proessionals

2137 Web design and development proessionals

2139 Inormation technology and telecommunications proessionals n.e.c.

3131 IT operations technicians

3132 IT user support technicians

5242 Telecommunications engineers

5245 IT engineers


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Glossary and Terminology

1. The analysis o vacancies presented within this report is based upon

data provided by IT Jobs Watch (www.itjobswatch.co.uk) who tracks the

demand patterns or IT sta through the application o semantic analysis to

data obtained rom major IT recruitment sites. Where we have reerenced

specic groupings o skills in this report, e.g. process/methodological skills

o unctional knowledge/skills, these groupings have been drawn together

by e-skills UK only, and are not groupings developed or employed by IT

Jobs Watch (with the exception o Data Warehouse/Business Intelligence

cited on page 10 o the report).

2. Where gures are provided showing the number o advertised vacancies,

they have typically been rounded to the nearest 10 (i.e. unless specied

otherwise, and unless shown within related charts). As a result o thisrounding process, apparent discrepancies may appear in row/column total

(i.e. integers/percentages).

3. Various reerences have been made to specic technologies/processes

within this report, the more commonly used o which are set out below:

CSS Cascading Style Sheets

ETL Extract, Transorm, and Load

Oracle BI EE Oracle Business Intelligence Enterprise Edition

Oracle EBS R12 Oracle E-Business Suite (Release 12)

TDD Test Driven Development

4. In order to aid the reader, a number o abbreviations/shortcuts have been

employed when writing this report, the more commonly used o which are

set out below:

Sta Term used when reerring to individuals working

in stipulated positions irrespective o contractual

status (i.e. permanent or contract workers).

Current Term used when reerring to the third quarter o

2012 (unless otherwise stated), which was the

latest quarter or which a ull set o related datawas available.


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End Notes

i Big Data: The next rontier or innovation, competition, and productivity

ii 3D Data Management: Controlling Data Volume, Velocity and Variety, META

Group (now Gartner), February 2001

iii SAS High-Perormance Analytics: Transorming Big Data into Corporate

Gold, SAS, September 2012

iv It should be borne in mind that a megabyte was considered to be big

data in the not-so-distant past and that, today, whilst some would view a

petabyte as dening the bigness, i.e. volume element o big data, others

may instead avour a measure relating to terabytes (or multiples thereo) or

example.

v An adoption gure o 34%, or example, is provided by TDWI in their Best

Practices Report Q4.11, which is based on inormation rom 325 respon-

dents, 56% o which were based in the US and 74% o which were rom

rms with a turnover o $100 million or more. Aside rom the geographical

bias, putting these turnover gures in perspective using data rom the UK

rom the Oce or National Statistics, it can be seen that, in 2011, just 9%

o UK enterprises had a turnover o 1 million or more.

vi The current period or the purposes o this report is considered to be the

third quarter o 2012 in that this was the last ull quarter or which relevant

data is available.

vii Indexed gures are used to show the proportional change over time or

data series o dierent magnitudes. In this series the latest quarter (Q3.12)takes the value 100 and all other gures are presented as a related propor-

tion (and the shaded area at 100 represents no change). For clarity again,

other charts may be indexed to the third quarter o 2007. This variation is

or presentation purposes only and in no way aects the results reported

on within this document.

viii Although adverts may sometimes reerence an industry sector, it is not

common practice and, even where industry associations are made, they

are oten relatively vague. As such, this section is to be used as a broad

guide only.

ix Data Scientist: The Sexiest Job o the 21st Century, Harvard Business

Review, October 2012.

x To alleviate our conc

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