Finding the Face of Your Data

... As well as new team members with specialized skillsets

SOURCES 1 IDC, “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,” December 2012. 2 Rick Smolan and Jennifer Erwitt, The Human Face of Big Data (Against All Odds Productions, 2012). 3 Kelly, J. Big Data Market Forecast by Component. Retrieved July 31, 2013, from http://wikibon.org/wiki/v/Big_Data_Vendor_Revenue_and_Market_Forecast_2012-2017. 4 NoSQL Market Forecast 2013-2018, http://www.marketresearchmedia.com/?p=568 (September 11, 2012). 5 Derrick Harris, All aboard the Hadoop money train, http://gigaom.com/2012/05/07/all-aboard-the-hadoop-money-train (May 7, 2012). 6 Google Trends, http://www.google.com/trends, accessed August 7, 2013. 7 Gil Press, A Very Short History Of Data Science, http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science (May 28, 2013). 8 Thomas H. Davenport and D.J. Patil, Data Scientist: The Sexiest Job of the 21st Century, http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century (October 2012).

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The right balance of people, data, and computing power can reveal questions that previously couldn’t be answered – or even asked – to enhance data-driven business decisions and actions on insights

For more information please visit www.deloitte.com/us/techtrends2013.

BOTTOM LINE

There’s been an explosion in data assets

Enterprise expectations are as big as the data

deloitte on technology

Harvard Business Review recently called the data scientist the “Sexiest Job of the 21st Century.”8 But, finding data scientists and data professionals with both IT and line-of-business knowledge can be difficult. The diagram above shows the career paths, industries, and educational backgrounds of 12 leading data professionals aggregated with publicly available social network information.

Traditional and non-traditional value in data

A profile of 12 leading data scientists

Using these new tools and techniques may require skills such as data science, creative design, and cultural anthropology, which you may not already have in the enterprise. New team members with these capabilities should represent a blend of technology and business domain expertise. The new job title with the most fanfare has been the data scientist.

In order to extract both types of value from data, new techniques and tools are likely required. They may sound esoteric and academic, but they are enterprise-caliber and now fundamental.

An IDC forecast taking into account software, maintenance, and software-as-a-service revenue predicts the Hadoop and MapReduce ecosystem software market will reach almost $813 million by 2016. However, other experts say this estimate is conservative, underestimating growth in cloud-based offerings and not fully considering all the positive externalities – that “every sale made, fraud thwarted or page view generated thanks to Hadoop means a healthier economy.”

The worldwide NoSQL market is expected to reach $3.4 billion by 2018. The market has shifted from community- to application-driven as venture capital funding, mergers, and product offerings increase.

Growth of the “digital universe”1

IDC estimates that “tagged” information accounts for only about 3% of the digital universe, with analyzed information at 0.5%. The value of big data technology lies in exploring the “untapped pools.”

Data overload in context2

The amount of information generated by humanity during the first day of a baby’s life today is equivalent to 70 times the information contained in the Library of Congress.

2009 2020

40,000

30,000

20,000

10,000

Exabytes

Big data spending forecast by component3

According to a Wikibon study, big data spend will shift from infrastructure and middleware to value-add services and software during the next five years. Infrastructure, middleware, and technical services will likely become increasingly commoditized as they mature and common standards are adopted. We also note that this study did not include the costs associated with the business and domain experts’ time – a critical element of actionable insight.

2011 2013 20142012 2015 2016 2017

10

20

30

40

50

BillionDollars

Compute

Storage

Networking

Infrastructure Software

SQL Database Software

NoSQL Database Software

Application Software

Professional Services

Xaas

31% CAGR

2011 2012

10

20

30

40

50

BillionDollars

31% CAGR

1 EB = 1 billion gigabytes

IND

UST

RY

CU

RR

ENT T

ITLE

PR

EVIO

US

JOB

EDU

CA

TIO

N

C

Letters represent individuals, colors represent current titles, circle size represents number of individuals.

Data divingIn taking advantage of new data assets – from internal, external, structured, and unstructured data – and analytics tools, the most common form of value is realized through exploiting deeper detail for new and better answers to current questions.

Pattern findingThe other side of that same coin, seeking and patterning for previously unasked and unanswerable questions, is less common, but potentially more important to the enterprise.

HealthEntertain-ment

Telecomm.

Business analyst

Director of data &analytics

Software engineer

Data scientist

Business analyst

Director of data &analytics

Product manager

OwnerResearcher,Director ofResearch

Consulting

Business

Economics& Finance

Information systems

Computer science

C

K

K

M

M

L

L

D

C

CA

D

A B

B

D

Internet

K

L

M

J

J

J B

E

E F

F

G

G

H

H

F

H

G

Taking advantage of data requires new tools ...

Expanding the data analytics toolbox NoSQL market4

Visualization Natural language processing

Machine learningOntology discovery

Quantitative modeling Text analytics

2013 2014 2015 2016 2017 2018

Hadoop & MapReduce ecosystem software market5

201320122011 2014 2015 2016

1000

750

4

3

2

1

500

250

Milliondollars

Billiondollars

60.2% CAGR

21% CAGR

Data scientist

A

E

Engineering

Chemistry& Physics

Statistics& Math

x70

Google Trends: Searches for “data scientist”6

January 2011 July 2013

100Peak

80

60

40

20

Searchinterest

LinkedIn: Analytics & data science job growth7

1990 2010

0.1

0.08

0.06

0.04

0.02

Percentage ofjob starters