The study aims to define, assess and measure the European data economy as well as build a genuine stakeholders’ ecosystem. Find us on http://datalandscape.eu and @eudatalandscape
3. Volume
Variety
Velocity
Value
Big Data technologies
describe a new
generation of
technologies and
architectures designed
to economically
extract value from very
large volumes of a
wide variety of data by
enabling
high-velocity capture,
discovery, and/or
analysis.
What is Big Data?
7. 7
Big Data Adoption in Europe Will Accelerate in 2014
Big Data is becoming mainstream in North America, but
Europe lagged behind due to
• Size factor: smaller organizations and smaller data sets
• Expensive, scarce data analytics skills
• Economic crisis, cautiousness in new investments
IDC survey data indicates that the most mature
geographies are starting to ramp up in adoption: the U.K.,
Germany, and France.
The analytically mature market sectors will lead: business-
to-consumer (B2C) industries and large enterprises.
Big Data helps European companies to “think global, act
local” and manage diversity
8. 23%
7%
49%
14%
7%
Already adopted Plan to adopt in the next 24 months
Not adopted and no adoption plans Not familiar
Don't know
Value from
unstructured
data
Data
explosion
Decision
support and
forecasts
Velocity
Better and
faster
answers
Big Data of WE companies will adopt Big Data by
year-end 2015
8
30%
WE companies using a Big Data solution
appreciate the benefits in terms of velocity in
analyzing data.
2o ut off3
%
Adoption
Base: all sample n = 1,651
Source: IDC European Vertical Markets Survey, October 2013
Big Data Adoption in the EU main MS
- (DE, ES, FR, IT, UK)
9. 9
Big Data Adoption by Industry: Telecom/ media and
Financial services lead but Healtcare has big plans
Financial
services
Healthcare
Discrete
manufacturing
Top 3 sectors by
Investment Plans by 2015
Base: all sample n = 1,651
Source: IDC European Vertical Markets Survey, October 2013
0% 10% 20% 30% 40% 50% 60%
Government/ education
Healthcare
Process manufacturing
Professional…
Utilities/oil and gas
Total
Retail/wholesale
Discrete manufacturing
Financial services
Telecom/media
Sectors Ranking by % enterprises
users of Big Data
10. 10
Big Data Use Cases
Non-analytic
workload
(e.g., websites or
email) Analysis of
transactional
data from
sales
systems
Analysis of
machine or
device
data
Discrete
manufacturing
Algorithmic
trading
Sentiment
analysis and
brand reputation
Influencer
Analysis
Customer
profiling,
targeting, and
optimization of
offers for cross-
selling
Financial services
n = 477
Base: companies adopting or planning to adopt a Big Data solution by year-end 2015
Source: IDC European Vertical Markets Survey, October 2013
Network
optimization
Customer
center and call
center
efficiency
Customer
scoring and
churn
mitigation
Location-
based services
using GPS
data and
geospatial
analytics
Telecom
Readmissions
management
Illness/disease
progression
Elective
surgeries
Complications
management
Healthcare
14. MICROECONOMIC
IMPACTS
Costs savings
Increased flexibility
thanks to timely and
improved decision
making
New
products/services
Improved customer
services
Increased revenue
MACROECONOMIC
IMPACTS
GDP growth
SMEs and jobs
creation
Data-driven
competitiveness of
EU industry
Data collection and
creation
Storage,
aggregation,
organization
Analysis,
processing,
marketing and
distribution
DATA VALUE CHAIN
Framework Conditions of development of the European Data Economy
Policy/ Regulatory Framework Conditions Non Regulatory Framework Conditions
DataPrivacy
Data
Ownership
Copyright
Security
Skills
infrastructure
s
Interoperabilt
y,Standards
Accesstorisk
capital
Primary use
Re-use
A preliminary view of the Data Value Chain and
ecosystem
15. Data Market Taxonomy
Type of data,
Type of stakeholders,
Type of skills,
Type of Technologies, Tools, Applications, Services
Indicator 1
Number of
Data
workers
Description
Measurement
approach
Data sources
Indicator 2
Number of
Data-related
companies
Description
Measurement
approach
Data sources
Indicator 3
Revenues of
Data-related
companies
Description
Measurement
approach
Data sources
Indicator 4
Data Market
size
Description
Measurement
approach
Data sources
Indicator 6
Citizen’s
Data
Economy
Description
Measurement
approach
Data sources
Indicator 5
Data workers
skills gap
Description
Measurement
approach
Data sources
European Data Market Monitoring Tool
Design of the Data Value Chain
19. Website objectives
To give visibility to the data companies in EU
To aggregate information about the existing data
communities (meta-community)
To facilitate discussion about EU policies related
to the data economy
To disseminate data snippets from the study
22. Help us out
www.datalandscape.eu (available mid-june)
#eudatalandscape
@eudatalandscape
https://groups.diigo.com/group/eudataeconomy
Call for animators: http://www.open-
evidence.com/call-for-animators-on-big-data/
23. IDC in Europe and Big Data
Alys Woodward – Research Director,
European Software (Big Data, Advanced
Analytics, Enterprise Social)
Donna Taylor – Research Director
European Storage
Big Data & Social Business Practice Co-Leads
Gabriella Cattaneo
IDC European Government Consulting
gcattaneo@idc.com
Editor's Notes
The available storage capacity will decrease from 33% of the digital universe to only 15%
Connected things from 20 to 30 billions from 7 to 15% of connectable things
Copyright 2009 IDC. Reproduction is forbidden unless authorized. All rights reserved.
Big Data – Introduction
Source: IDC European Vertical Markets Survey, October 2013
n = 1,651 – Base: all sample
Western Europe Big Data Adoption by Vertical Market
Q. Are you familiar, do you already adopt, or are you planning to adopt a Big Data solution?
Source: IDC European Vertical Markets Survey, October 2013
n = 1,651 – Base: all sample
Current adoption of Big Data technologies is still relatively low and concentrated in larger companies. An exception — related to the low adoption rates — is the telecom/media sector, where data explosion led half of the companies to invest in Big Data for modernizing business analytics and data analysis. Other verticals show homogeneous adoption rates, with financial services and discrete manufacturing slightly above average.
Financial services, discrete manufacturing, and healthcare are ripe for increasing investments in Big Data in the short term, while other verticals such as government/education show a wait-and-see approach.
It is interesting to see how Big Data is not a familiar term yet for many companies (particularly the smallest ones), and/or they are not aware how this technology could add real value to their businesses.