Big Data Innovation in EMEA in 2015

Transcription

1 Big Data Innovation in EMEA in 2015 Business Evolution via Big Data and Analytics An IDC White Paper sponsored by SAP and Intel Alys Woodward Philip Carter

2 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. 2

3 SITUATION OVERVIEW LEARNING FROM THE BIG DATA 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 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. 3

4 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 IDC's Big Data and Analytics Maturity Model identifies five stages and five critical measures 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: 1 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. 2 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. 3 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. 4 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. 5 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. 4

5 Drivers of Business Evolution in EMEA by Region Big Data and analytics is often a response to change in business environments. Internal organizational changes and external market changes mean that management needs greater 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. Changing 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 represented 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-related 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. 5

6 Figure 1 Drivers of Business Evolution in EMEA, by region Q. What are the top three drivers that are forcing your organization to evolve its business? EMEA N. Europe S. Europe Middle East Africa 1st Improve operational efficiency (36%) Improve operational efficiency (39%) Improve operational efficiency (40%) Increased competition in the market (33%) Customer Centricity (31%) 2nd Improve profitability (30%) Improve profitability (33%) Changing customer/ consumer demands (37%) Changing customer/ consumer demands (26%) Changing customer/ consumer demands (30%) 3rd Increased competition in the market (29%) Increased competition in the market (29%) Increased competition in the market (31%) Improve operational efficiency (24%) Improve profitability (28%) 4th Changing customer/ consumer demands (29%) Customer Centricity (27%) Need to drive innovation (30%) Need to build/ maintain market leadership (24%) Need to drive innovation (24%) 5th Customer Centricity (27%) Need to drive innovation (26%) Improve profitability (29%) We are entering new markets (24%) Improve operational efficiency (24%) Note: n=978 Source: IDC, 2015 IDENTIFYING THE 'BIG DATA INNOVATORS' 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". 6

7 Figure 2 Identifying the Big Data Innovators Ad hoc Opportunistic Repeatable Managed Optimized High Low Low High Note each dot represents one of the 978 EMEA organizations interviewed. Each red dot represents one of the 38 Big Data Innovators. Source: IDC, 2015 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 THE SUBREGIONS 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). 7

8 Figure 3 EMEA Big Data Innovators Source: IDC, 2015 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 organizations 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. 8

9 Figure 4 Big Data Maturity in EMEA, by region Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Ad Hoc Opportunistic Repeatable Managed Optimized 54% 53% 50% 45% 39% 50% 47% 47% 47% 42% 11% 6% 2% 3% 2% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% EMEA N. Europe S. Europe Middle east Africa Note: n=978 Source: IDC,

10 Key characteristics of the Big 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. 1 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. 10

11 Figure 5 Budgets 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? 38% 10% 10% 28% 15% 25% 22% 12% 32% 8% Big Data Innovators Others With ad-hoc, unbudgeted funds reallocated from other sources With project by project budgets as individual opportunities With business unit level budget set across several projects With annual enterprise wide budget With annual enterprise wide budget supplemented with ad hoc funding for special projects Source: IDC Big Data Survey for SAP and Intel, 2015, n=978 11

12 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. 12

13 Figure 6 Time to ROI for Big Data & Analytics Projects: Big Data Innovators vs. Others 50% 45% 45% 40% 36% 39% 35% 30% 25% 20% 26% 21% 15% 10% 5% 0% 13% 3% 3% 5% 10% 3 months or less 3 to 6 months 6 to 12 months over 12 months undetermined Big Data Innovators Others Source: IDC Big Data Survey for SAP and Intel, 2015, n=978 3 Big Data Innovators have higher adoption levels of real-time and predictive analytics Big 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. 13

14 Figure 7 Adoption of real-time and advanced analytics: Big Data Innovators vs. Others Big Data Innovators 63% Others 42% 0% 10% 20% 30% 40% 50% 60% 70% Source: IDC, 2015 USE CASES FOR REAL-TIME AND PREDICTIVE ANALYTICS 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 analytics-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. 14

15 Figure 8 Use Cases for Real-time and Predictive Analytics in EMEA Source: IDC,

16 4 Big Data Innovators show higher adoption levels of in-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 evolutionary 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 organizations 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. 16

17 Figure 9 Data Management Platforms Q. What types of data management approaches are utilized in your organization for Big Data? EMEA Northern Europe Southern Europe Middle East Africa 1 st RDBMS (48%) RDBMS (44%) RDBMS (54%) RDBMS (54%) RDBMS (59%) 2 nd In-memory databases (34%) In-memory databases (41%) Database appliances (32%) Graph databases (34%) Database appliances (32%) 3 rd Columnar databases (32%) Columnar databases (35%) In-memory databases (29%) Open Source Big Data Platforms (33%) NoSQL databases (31%) 4 th Graph databases (27%) NoSQL databases (27%) Columnar databases (27%) Columnar databases (32%) NewSQL databases (28%) In-memory databases 9th (20%) 9th (14%) Note: n=978 Source: IDC,

18 Future outlook and recommendations IDC 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 replicated 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 executive in order to drive interest, impetus, and funding. It should also embrace non-executive 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 involvement 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 strategies and operations. The role of IT is to put the right governance model and integration 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. 18

19 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. 19

20 Conclusion In 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: 1. Big Data innovators are more likely to have an enterprise budget in place 2. Big Data innovators have shorter time to ROI 3. Big Data innovators have higher adoption levels of new data management technologies and predictive analytics 4. Big Data innovators show higher adoption levels of in-memory databases 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%). 20

21 About IDC International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications and consumer technology markets. IDC helps IT professionals, business executives, and the investment community make fact-based decisions on technology purchases and business strategy. More than 1,100 IDC analysts provide global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries worldwide. For 50 years, IDC has provided strategic insights to help our clients achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology media, research, and events company. Copyright Notice Global Headquarters 5 Speen Street Framingham, MA USA idc-insights-community.com This IDC research document was published as part of an IDC continuous intelligence service, providing written research, analyst interactions, telebriefings, and conferences. Visit to learn more about IDC subscription and consulting services. To view a list of IDC offices worldwide, visit Please contact the IDC Hotline at , ext (or ) or sales@idc.com for information on applying the price of this document toward the purchase of an IDC service or for information on additional copies or Web rights. Copyright 2015 IDC. Reproduction is forbidden unless authorized. All rights reserved.