Driving Business Evolution: Big Data Innovation for EMEA in 2015
Transcript of Driving Business Evolution: Big Data Innovation for EMEA in 2015
Alys WoodwardPhilip Carter
Analyze the future
Big DataInnovation in
EMEA in 2015
An IDC White Paper sponsored by SAP and Intel
Business Evolutionvia Big Data and Analytics
copyright IDC 20152Analyze the future
IDC OPINION
German retailer Otto saved €40 million by
using analytical demand-based forecasting
in perishable goods. Danish pump
manufacturer Grundfos AB estimates that
the usage of advanced analytics solutions
applied to its quality management process
reduces the rate of product returns due to
defects by between 20% and 30%. In the
U.K., Thames Water saved up to 25% of its
spending on chemicals in water treatment
and filter beds, due to improved forecasting
based on analytics. These organizations
have two things in common — they are
mature and they are innovative in their use
of Big Data and analytics.
However, the headlines belie the long
journey of trial and error that underpins the
vast majority of Big Data and analytics
success stories. This journey led to the
resulting maturity, and the maturity meant
that the organization gained these big wins.
Many organizations never achieve this type
of headline result because early failures —
or modest wins where great wins were
expected — discourage them and deter
them from further investment. Conversely,
early success can drive greater
achievement, with the desire for improved
visualization, better access to information,
and the output from analytical processing
spreading from department to department
in an organization. Gaining value from
information across an entire enterprise is a
journey of many steps; like in any race, a
good start leads to accelerated progress,
while a stumble can lead to slowing down or
grinding to a halt.
Maturity in Big Data and analytics means
that these organizations were competent in
five areas: the people, the process, the
technology, the data, and the intent
surrounding Big Data and analytics (BDA).
IDC has defined a maturity model that
evaluates organizations in order to track
BDA maturity across each of these
dimensions.
The maturity evaluation process is in the
form of a series of questions. IDC surveyed
978 organizations across 15 countries in
EMEA in order to evaluate Big Data
maturity in the region. The 38 organizations
with the highest scores are identified as
"Big Data Innovators": these companies
represent the highest level of maturity in
EMEA, and it is these organizations we look
towards to see exactly what lessons the
rest of the region can learn.
IN THIS WHITE PAPER
This IDC White Paper describes IDC's Big Data and Analytics Maturity Model, and a survey
conducted in two parts to evaluate Big Data and analytics maturity. The survey was
conducted in October 2014 across France, Germany, the Netherlands, the Nordics, and the
U.K. It was extended in February 2015 across 10 more countries (Italy, Spain, Portugal,
Saudi Arabia, Kuwait, Oman, UAE, Kenya, Nigeria, and South Africa).
The survey base consists of 978 respondents across 15 countries in Europe, the Middle
East, and Africa (EMEA). Part of the objective of this paper is to identify the key
characteristics driving Big Data innovation, and how these differ by subregion within EMEA.
copyright IDC 20153Analyze the future
SITUATION OVERVIEW — LEARNING FROM THE BIGDATA INNOVATORS
WHERE ARE WE NOW WITH BIG DATA?
The early years of Big Data, from 2005 to
2012, were about trying to define what Big
Data actually is — what technologies,
practices, and business benefits relate to
this new area. Three years ago, the market
moved on to piloting and prototyping in a
big way; according to IDC research around
20% of organizations in Europe conducted
Hadoop pilots in 2015.
IDC describes Big Data as both revolution
and evolution. Big Data is revolutionary in
the sense of the availability of new
technology platforms, such as in-memory
databases, parallelized advanced analytics
engines, and distributed high-volume
processing platforms like Hadoop.
Revolution applies to the economics of the
technology as well as the technology itself;
for example, many high-end predictive
analytics engines are now available in a
cloud service, making them far more
accessible for trials, sandboxing, and
intermittent use than when organizations
had to set up expensive infrastructure,
provision their own systems, and invest in
management staff and tools.
However, in order to successfully integrate,
store, and deploy information to business
users, organizations need to retain the
practices from their traditional business
analytics and data warehousing teams.
When successful, these teams know about
how to deploy information, what interfaces
are likely to work for what end users, how to
deliver quick wins to the business for
information-related systems, and how to
align business requirements with
technology systems in a way that keeps IT
and the business in close contact. They
also have a good understanding of the data
that is available to the organization, its
quality, and how it is used in business
decisions. Too much focus on the
revolutionary technology aspects of Big
Data means that the new Big Data team
has to learn all these lessons again from
scratch, which would increase the time to
value considerably.
Big Data must take into account the
technology revolution but also the practice
evolution to leverage the knowledge the
organization already possesses about how
it can best use information.
Gaining value from information is a journey
of many steps. Some of these individual
steps will not be successful — they may
deliver less ROI than expected, or no ROI
at all; insights that were expected in data
may not be found; business requirements
may change during the project so that,
through no fault of the technology team, the
project does not answer the question that
the business need to answer.
Organizations should therefore approach
Big Data and analytics with a growth
mindset; expect to progress, for your
second project to be more complex and
challenging than your first, and expect your
knowledge and experience to grow
dramatically in the course of the journey.
The transition to Big Data compared to
traditional business analytics and data
warehousing affects every technology
domain: software, infrastructure, services,
storage, and networking.
Organizations need to do two things: see
progress as a journey of multiple steps, and
evaluate progress so far and focus on
applying resources to the right part of the
journey.
copyright IDC 20154Analyze the future
IDC's Big Data andAnalytics Maturity Model
In order to help organizations do this, IDC has developed a Big Data and Analytics Maturity
Model. The model has two key functions: it helps organizations prioritize their Big Data and
analytics investments and activities in order to achieve balance across five elements
(people, process, technology, data, and intent), and it provides a path along which to
advance, making it easier for organizations to learn from the most mature and successful
organizations about what constitutes best practice and how to implement it.
IDC's Big Data and Analytics Maturity Model identifies five stages and five critical meas-
ures as well as the outcomes and actions required for organizations to effectively move
through the maturity model stages. The five measures against which the model assesses
organizations' competencies are: people, process, technology, data, and intent.
The five maturity stages are ad hoc, opportunistic, repeatable, managed, and optimized.
The stages are described below:
Ad Hoc: The primary BDA goal of organizations at the ad hoc stage is to provide decision makers with
access to information. This can involve the use of query, reporting, dashboard, and search software
simply to expose a defined data set to end users. The systems lack integration, dedicated technology,
and broad adoption.
Opportunistic: Organizations at the opportunistic stage are mainly focused on providing data analysis,
but the data will typically lack support from appropriate data preparation and management technology
and will be based on incomplete historical data. The analysis typically involves the use of
multidimensional analysis, query, reporting, and content analytics tools.
Repeatable: Organizations at the repeatable stage are involved in recurring, budgeted, and funded BDA
projects with business-unit-level stakeholder buy-in. They are aiming to provide comprehensive insights
based on data from multiple internal and external structured, semi-structured, and unstructured sources.
The analysis can involve the use of multidimensional analysis, query, reporting, content analytics, and
predictive analytics tools and the underlying information management technology.
Managed: Organizations at the managed stage experience the emergence of BDA program standards.
Their primary BDA goal is to provide actionable insight to a range of decision makers within the
organization. BDA capabilities are utilized to answer what happened and why.
Optimized: Organizations at the optimized stage ensure continuous and coordinated BDA process
improvement and value realization. They have an enterprisewide, documented, accepted BDA strategy,
executive support, and budgeted as well as ad hoc funding (to address unforeseen opportunities). They
are able to provide foresight to decision makers throughout the enterprise and to relevant external
stakeholders. Analytics continue to be deployed operationally through business processes, resulting in
predictive capabilities to capitalize on new opportunities and to mitigate risk.
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3
4
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Drivers of Business Evolution in EMEA by RegionBig Data and analytics is often a response to change in business environments. Internal
organizational changes and external market changes mean that management needs great-
er transparency into cause and effect, and asks for better information and analytics. IDC
asked the survey respondents to indicate three drivers that were forcing their businesses to
change and evolve.
In both Northern and Southern Europe, the top driver for business evolution is the need to
improve operational efficiency (38% of respondents cited this driver). The focus in Northern
Europe is on efficiency, removing cost, and optimizing business processes, rather than
expansion and innovation. Big Data can play a key role for businesses with this focus by
improving visibility into business processes to demonstrate where costs can be eliminated.
By contrast, the top driver for business evolution in the Middle East is the increased level of
competition in the market, with 33% of Middle East organizations citing this driver. Chang-
ing consumer/customer demands are driving change in just over a quarter (26%) of
respondents in the region. Improving operational efficiency and the need to build/maintain
market leadership are joint third at 24%. In line with the dynamic emerging markets repre-
sented in this region, we see that organizations in the Middle East are more focused on
expansion-related drivers than on efficiency-related drivers. Big Data and analytics can be
hugely helpful when organizations are expanding, giving insight into customer behavior as
it evolves, and exposing causal relationships between organizational activities and
outcomes.
African customers and consumers are changing rapidly, and the top two drivers for
business evolution in the region are the need to become customer-centric and changing
consumer/customer demands; 31% of organizations cited each of these drivers. The third
most popular driver is the need to improve profitability (28%), and driving innovation comes
in fourth (24%). So we see that African organizations are strongly focused on customer-re-
lated drivers, with some level of concern about efficiency improvements. Big Data and
analytics is ideal to support organizations in becoming more customer-centric, supporting
data collection, the observance of patterns, and ultimately the prediction of how individual
customers will respond to marketing outreach.
Figure 1 shows the drivers of business evolution for EMEA overall and by region.
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Figure 1Drivers of Business Evolution in EMEA, by regionQ. What are the top three drivers that are forcing your organization to evolve its business?
Note: n=978Source: IDC, 2015
With the goal of evaluating Big Data and analytics maturity across EMEA, IDC interviewed
978 organizations across the EMEA region that have adopted or intend to adopt some form
of Big Data and analytics technology. The interview questions covered all five dimensions
of the model (people, process, technology, data, and intent) and the responses were
translated into maturity "scores".
1st
2nd
3rd
4th
5th
EMEAImprove operational efficiency (36%)
Improve operational efficiency (39%)
Improve operational efficiency (40%)
Improve operational efficiency (24%)
Improve operational efficiency (24%)
Improve profitability(30%)
Improve profitability(33%)
Improve profitability(28%)
Increased competition in the market (29%)
Increased competition in the market (29%)
Increased competition in the market (31%)
Increased competition in the market (33%)
Changing customer/consumer demands (29%)
Changing customer/consumer demands (37%)
Changing customer/consumer demands (26%)
Changing customer/consumer demands (30%)
Customer Centricity(27%)
Customer Centricity(27%)
Customer Centricity(31%)
Improve profitability(29%)
Need to drive innovation (26%)
Need to drive innovation (30%)
Need to drive innovation (24%)
We are entering new markets (24%)
Need to build/maintain market leadership (24%)
N. Europe S. Europe Middle East Africa
IDENTIFYING THE 'BIG DATA INNOVATORS'
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Figure 2Identifying the Big Data Innovators
High
LowLow
In order to understand the best practices from the most successful organizations, IDC
extracted the 38 highest scoring respondents, and identified them as "Big Data
Innovators". Responses from this group were considered separately to other responses in
order to answer the question "What do the Big Data Innovators do better?"
Figure 2 shows the respondent base in terms of Big Data and analytics maturity, how they
map to the maturity levels, and how the Big Data innovators compare with the broader
respondents.
BIG DATA INNOVATION AND MATURITY– HOW DO THESUBREGIONS COMPARE?
In assessing the data from survey respondents there are notable differences in terms of the
Big Data maturity across the subregions, particularly in terms of the geographic spread of
the Big Data innovators:
33 of the 38 Big Data innovators in EMEA were from Northern Europe (France, Germany, the
Netherlands, Norway, and the United Kingdom).
5 were from Southern Europe (France, Italy, Portugal, and Spain)
There were no Big Data innovators from either the Middle East (KSA, Kuwait, Oman, Qatar, and
UAE) or Africa (Kenya, Nigeria, and South Africa).
Ad hoc Opportunistic Repeatable Managed Optimized
High
Source: IDC, 2015
Note each dot represents one of the 978 EMEA organizations interviewed.Each red dot represents one of the 38 Big Data Innovators.
copyright IDC 20158Analyze the future
Figure 3EMEA Big Data Innovators
Source: IDC, 2015
7
119
6
5
This is not to say that we do not see examples of innovative Big Data projects in these
emerging markets. In fact, developing regions are at an advantage when it comes to Big
Data and analytics; they can be less impeded by legacy architectures. Once they have
sufficiently automated business processes to feed Big Data and analytics systems, Middle
Eastern and African organizations may "leapfrog" the older companies of the developed
markets by moving straight to modern architectures and the latest version of best practices.
However, relatively speaking, Europe is a more advanced and relatively mature market for
Big Data and analytics. This is particularly the case in Northern Europe, where awareness
and skills linked to the tools and technologies is higher, and the vast majority of organiza-
tions think they should be doing more in Big Data and analytics. Generally in this region,
projects are recurring, budgeted, and funded mainly by line of business (LoB) heads.
However, there is opportunity to progress towards projects with more cross-department
standardization and more awareness of what causes changes in the business — what
happened, and why.
Southern Europe has some mature organizations with understanding of the benefits of Big
Data and analytics. However, uptake and advancement in maturity has been impeded in the
last seven years due to the fallout of the economic downturn, which has hit the region hard.
Figure 4 shows Big Data maturity for EMEA broken down by four subregions.
copyright IDC 20159Analyze the future
Figure 4Big Data Maturity in EMEA, by region
Note: n=978 Source: IDC, 2015
45%
39%
54%
50%
53%
47%50%
42%47% 47%
6%
11%
2%
0% 0%1%
2%3%
1%0% 0% 0% 0% 0% 0%
Ad Hoc Opportunistic Repeatable Managed Optimized
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
EMEA N. Europe S. Europe Middle east Africa
1
copyright IDC 201510Analyze the future
Key characteristics of theBig Data Innovators
IDC compared the Big Data innovators' interview responses with those of the other
respondents and identified a range of activities that differentiate between the two groups.
These activities constitute best practices and areas where other organizations can learn
from the Big Data innovators. Below we describe the three most significant characteristics
of the group.
Big Data Innovators are more likely to have an enterprise budget in place
Often, when organizations are getting started with Big Data and analytics, budgets will be
discretionary and fragmented. As organizations mature, their budgets are set in an
increasingly planned, centralized, and strategic way. The most mature organizations set an
enterprise budget for Big Data and analytics projects and supplement it with discretionary
budget as required. This need for discretionary additional budgets is an important
difference between information-related projects and infrastructure- and application-related
projects. Information requirements are not static; they change as business requirements
change. Gaining the best value from Big Data and analytics means combining
enterprisewide budgets and planned rollouts with the ability to spin up new projects for new
requirements in the short term.
38% of Big Data innovators fund their Big Data and analytics projects with an annual
enterprisewide budget supplemented with ad hoc funding for special projects, compared
with only 11% of the other respondents. This way of funding projects was the most popular
for the Big Data innovators, while for the other respondents the most popular funding
method was "with project by project budgets as individual opportunities," showing a far
more fragmented approach.
Figure 5 shows how budgets are set for Big Data and analytics projects in EMEA, for Big
Data innovators and others.
copyright IDC 201511Analyze the future
Figure 5Budgets and Funding for Big Data & Analytics Projects:Big Data Innovators vs. Others
Q: How does your organization fund and budget its Big Data & Analytics activities?
Source: IDC Big Data Survey for SAP and Intel, 2015,n=978
With ad-hoc, unbudgeted funds reallocated from other sources
With annual enterprise wide budget
With annual enterprise wide budget supplemented with ad hocfunding for special projects
Big Data Innovators Others
With project by project budgets as individual opportunities
With business unit level budget set across several projects
10%8%
25%
22%
32%
12%
10%
28% 15%
38%
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copyright IDC 201512Analyze the future
Big Data Innovators deliver shorter time to ROI
All IT systems strive to improve the time to ROI; bringing benefits to the business more
quickly improves every financial justification of any technology project. However, it is even
more important for Big Data and analytics that the system delivers rapid benefits, due to the
dynamic nature of information requirements; they appear urgent, and sometimes are, and
they need to be fulfilled.
A Big Data and analytics system that takes too long to deliver insights will soon be
replaced, but not by newer more dynamic systems, unless the organization has specific
understanding of why the system didn't deliver. Rather, systems that can't respond in time
could be replaced by shadow IT where business users have created their own solutions,
which likely exclude important IT considerations like scalable infrastructure, data quality,
and data consistency. Worse, they can be replaced by "gut feel" or instinct-driven decisions,
which can lead to less transparency, less logic, and less repeatability in decision-making
processes.
The more mature organizations found faster ROI on average; 45% achieved ROI in three to
six months, compared to 26% of the lower maturity organizations. A fifth of the lower
maturity organizations took over 12 months to show ROI, but only 3% of the Big Data
innovators took this long.
To launch a successful Big Data and analytics project, IDC recommends the identification
of a clearly defined project with measurable KPIs. Companies should focus on achieving
slightly quicker ROI with successive projects to demonstrate improvement in the speed to
value.
Figure 6 below shows the time to ROI for Big Data and analytics projects in EMEA, for Big
Data Innovators, and others.
copyright IDC 201513Analyze the future
Figure 6Time to ROI for Big Data & Analytics Projects: Big DataInnovators vs. Others
Source: IDC Big Data Survey for SAP and Intel, 2015, n=978
3 months or less
3 to 6 months
6 to 12 months
over 12 months
undetermined0%
50%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Big Data Innovators Others
13%
45%
36%
3% 3%5%
26%
39%
21%
10%
Big Data Innovators have higher adoption levels of real-time and predictive analytics3Big Data innovators show significantly higher adoption levels of advanced analytics
technologies (real-time and predictive). The use of this type of analytics puts some level of
pressure on Big Data architecture; it needs to be flexible enough to support real-time
information. Data needs to be of good enough quality to work well for predicting the future
as well as analyzing the past, and users need to be skilled enough to work with predictive
models and understand their ramifications for business.
This data also shows that Big Data innovators are generally doing more, using wider
ranges of different technologies, and presenting more varied front-end tools to their end
users. The days of the single enterprise data warehouse are over; Big Data is a range of
technologies to address a wide set of modern information needs.
Figure 7 shows the adoption of real-time and advanced analytics for the Big Data
innovators, and the others.
copyright IDC 201514Analyze the future
Figure 7Adoption of real-time and advanced analytics: Big DataInnovators vs. Others
Source: IDC, 2015
USE CASES FOR REAL-TIME AND PREDICTIVEANALYTICS IN EMEA
Increased use of real-time and predictive analytics correlates with greater Big Data and
analytics success and greater value delivered to the business from information- and analyt-
ics-related projects. It can be challenging for organizations that are new to real-time and
predictive analytics to understand and articulate the potential business benefits because
these are specific to individual use cases. For this reason, IDC surveyed the use of
real-time and predictive analytics by presenting each respondent with appropriate options
for their industry.
Figure 8 shows the use cases for real-time and predictive analytics in EMEA by industry.
Others
Big Data Innovators
10% 20% 30% 40% 50% 60% 70%0%
42%
63%
copyright IDC 201515Analyze the future
Figure 8Use Cases for Real-time and Predictive Analytics in EMEA
Source: IDC, 2015
4
copyright IDC 201516Analyze the future
Big Data Innovators show higher adoption levels ofin-memory databases
A key element of the revolution of Big Data is the proliferation of data management platforms
and technologies to support different types of data and different user access requirements. No
longer do we try to fit all reporting information into a central data warehouse. Having a range
of data management platforms shows an organization is embracing the variety of Big Data, it
is not a sign of immaturity and insufficient standardization, as it would have been perceived in
the traditional business analytics world.
The most widely used data management platform is the relational database (RDBMS), with
48% of respondents in EMEA stating they use this platform for Big Data. This shows the evolu-
tionary nature of Big Data; the much-maligned incumbent platform is still the most popular
choice! 34% of respondents are using in-memory databases, with penetration far higher in the
developed subregions. Older and larger organizations have higher data volumes on average,
and developed regions have more real-time information requirements, hence the high level of
penetration for in-memory databases. Columnar and graph databases also have more than a
quarter of organizations across the whole of EMEA, indicating willingness to use new data
platforms for new types of data
In Northern Europe, penetration of in-memory databases at 41% is almost as high as
RDBMS usage for Big Data (44%). Columnar databases are just over one-third (35%),
and NoSQL is in fourth place with 27%. This shows the wide range of databases and
data types that mature, information-rich organizations are dealing with. Similarly in
Southern Europe, in-memory databases are growing in terms of adoption, and they
rank as the third most popular data management platform.
By contrast, in the emerging markets of the Middle East and Africa, the level of adoption
of in-memory databases is much lower (coming in as the ninth most popular platform).
The RDBMS remains the platform of choice and highlights the focus on traditional data
management platforms in these markets which needs to evolve in order for organiza-
tions to move into the innovation phase of the usage of Big Data technologies.
Figure 9 shows the usage of data management platforms in EMEA broken down by four
subregions.
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Figure 9Data Management PlatformsQ. What types of data management approaches are utilized in your organization for Big Data?
Note: n=978Source: IDC, 2015
EMEA Northern Europe Southern Europe Middle East Africa
In-memorydatabases
1st
2nd
3rd
4th
RDBMS (48%) RDBMS (44%) RDBMS (54%) RDBMS (59%)RDBMS (54%)
In-memorydatabases (34%)
In-memory databases(41%)
Database appliances(32%)
Graph databases(34%)
Database appliances (32%)
Columnar databases (32%)
Columnar databases (35%)
In-memory databases (29%)
Open Source Big Data Platforms
(33%)
NoSQL databases
(31%)
Graph databases
(27%)
NoSQL databases
(27%)
Columnar databases (27%)
Columnar databases (32%)
NewSQL databases
(28%)
9th(20%)
9th(14%)
copyright IDC 201518Analyze the future
Future outlook and recommendationsIDC recommends the following actions and activities to organizations looking to improve
their adoption and maturity of Big Data and analytics.
Put in place a balanced, dynamic Big Data strategy.
The Big Data strategy needs to address all five dimensions — intent, data, people,
process, technology. A Big Data strategy also needs to be dynamic in the sense
that it is frequently updated with new input from a range of stakeholders (IT, the
analytics team, business executives, and users) across the organization. Best
practices from the most advanced department or business unit should be replicat-
ed into new areas, learning from past mistakes..
Balance the involvement of executive and non-executive
management.
The Big Data strategy needs to be visibly supported by a C-level business execu-
tive in order to drive interest, impetus, and funding. It should also embrace non-ex-
ecutive management as a key audience for driving broad adoption. One of the
characteristics of Big Data innovators that came out of the survey but is not
discussed in this document due to limited space is that they have greater involve-
ment from both executive and non-executive management in this way.
Balance IT and business involvement.
Both IT and lines of business need to be involved in Big Data and analytics strate-
gies and operations. The role of IT is to put the right governance model and integra-
tion capabilities in place up front. For example, in a recent discussion with a large
bank, it became clear that a successful Big Data analytics project focused on
risk-adjusted profitability for large corporate transactions could not be integrated
with its existing CRM system because IT had not been involved from the outset.
The role of the LoB stakeholder is equally critical; too much IT focus at the expense
of LoB often leads to a Big Data system that works perfectly well from an IT
perspective but delivers no value to the business. A leading U.K. telco recently
admitted that its €27.5 million spend on an information platform had yielded no
business value. Although neglecting LoB stakeholders has a different effect to
excluding IT, both IT and LoB involvement are equally vital.
copyright IDC 201519Analyze the future
Every Big Data project needs a clear desired business outcome.
Having an expected outcome agreed from the outset will shape many
decisions during the project. Some projects are justified with a business case
detailing what costs are expected to be reduced, or what revenue uplift is
expected. For some infrastructure-focused projects, the business outcome
may not be expressed in monetary form but could be expressed as faster
access to information for the business, or the ability to see two different types
of data together. Do not allow scope creep, as this can derail Big Data and
analytics projects; there is always more information that business units need,
but projects need to remain focused. Become accustomed to evaluating
information-related projects in terms that are more than monetary; learning
that a particular information source is of little value, for example, is a very
useful input for future projects, although it does not yield direct monetary
value.
copyright IDC 201520Analyze the future
ConclusionIn this document, we have identified 38 Big Data innovators that demonstrate the best
practices in Big Data and analytics across the EMEA region at the current time. In
summary, here are the four top characteristics of those organizations:
Big Data innovators are more likely to have an enterprise budget in place
Big Data innovators have shorter time to ROI
Big Data innovators have higher adoption levels of new data management
technologies and predictive analytics
Big Data innovators show higher adoption levels of in-memory databases
1.
2.
3.
4.
In the subregions of EMEA, there are some interesting regional differences in maturity, and
in the factors that are driving businesses to evolve in ways that could be underpinned by
Big Data and analytics. In summary:
In Northern Europe, the top driver for business evolution is the need to
improve operational efficiency (38% of respondents cited this driver),
followed by improving profitability (33%) and the increased level of
competition in the market (29%).
In Southern Europe, improving operational efficiency is the top driver for
business evolution (39% of respondents cited this driver), followed by
changing customer/consumer demands (36%) and the increased level of
competition in the market (31%).
In the Middle East, the top driver for business evolution is the increased level
of competition in the market, with 33% of organizations in the Middle East
citing this driver. Changing consumer/customer demands are driving change
in just over a quarter (26%) of respondents in the region.
In Africa, customers and consumers are changing rapidly, and the top two
drivers for business evolution in the region are the need to become
customer-centric and changing consumer/customer demands — 31% of
organizations cited each of these drivers. The third most popular driver is the
need to improve profitability (28%), and driving innovation comes in fourth
(24%).
Analyze the future
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