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.


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.


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.

1

2

3

4

5


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.


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 profitability(30%)



Increased competition in the market (29%)




Changing customer/consumer demands (29%)




Customer Centricity(27%)




Need to drive innovation (26%)



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'


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.


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.


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


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.


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%

2


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.


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.


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%


Figure 8Use Cases for Real-time and Predictive Analytics in EMEA

Source: IDC, 2015

4


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.


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%)


In-memory databases (29%)

Open Source Big Data Platforms

(33%)

NoSQL databases

(31%)

Graph databases

(27%)

NoSQL databases

(27%)



NewSQL databases

(28%)

9th(20%)

9th(14%)


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.


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.


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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Driving Business Evolution: Big Data Innovation for EMEA in 2015