Business opportunities: Big Data

Transcription

1 July 2013

2 Contents 1. Introduction 3 2. What is Big Data? 4 3. Big Data Adoption 5 4. Drivers and Barriers Opportunities for Digital Entrepreneurship Supply-side Business opportunities Demand-Side Business opportunities: Functions and Horizontal Business Processes Demand-Side Business opportunities: Operations and Vertical Specific Business Processes 22 2

3 1. Introduction Novel digital technologies (particularly Social, Cloud, Mobile and Big Data) are transforming the ICT industry and the way companies across all vertical markets can operate. They create new business opportunities for digital entrepreneurship both on the supply-side (to launch new services and/or establish new businesses) and on the demand-side (to optimize operations, reduce costs, improve services and/or launch new services along companies' horizontal business processes and vertical specific ones). This report focuses on Big Data. It assesses current adoption, plans of adoption, and drivers and barriers, and identifies business opportunities that EU companies can leverage by relying on Big data and advanced analytics.. Focus of the analysis is on the potential for new business value creation, driven by new or higher revenue, faster go-to-market, enhanced services, reduced costs, increased productivity or competitiveness. New opportunities are described as well as functional related ones, impacting R&D, production and operations, sales and marketing, customer support, financial and administrative functions. Industry specific opportunities are also identified. While the focus of the report is on raising awareness of the potential opportunities related to novel digital technologies, it should be stressed that these of course depend on their appropriateness for specific enterprises, the quality of their actual execution as well as the market and enterprise context. In short, opportunities always entail risks and barriers, which should be very carefully considered by enterprise managers, directors and shareholders. The analysis is part of a series of reports which assess the impact and business opportunities of other key novel digital technologies: mobile, social media and the cloud. 3

4 2. What is Big Data? Big Data is a term describing the continuous increase in data, and the technologies needed to collect, store, manage, and analyze it. It is a complex and multidimensional phenomenon, impacting people, processes and technology. From a technology point of view, Big Data encompasses hardware and software that integrate, organize, manage, analyze, and present data which is characterized by "four Vs": Volume: massive volume of data Variety: breadth of data sources and formats Velocity: speed at which information arrives, is analyzed and delivered Value: referring to both the cost of technology and the value derived from its use IDC describes Big Data technologies as a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis. The Big Data technology stack includes: Infrastructure, such as storage systems, servers, and datacenter networking infrastructure Data organization and management software Analytics and discovery software Decision support and automation software Services including business consulting, business process outsourcing, IT outsourcing, IT project-based services, and IT support and training related to Big Data implementations. 4

5 3. Big Data Adoption Big Data technologies are still far from widespread among European organizations, which are in large part not ready for it from a technical point of view or from a business point of view, as many of them still don't see significant added value in Big Data investments. Penetration of Big Data technologies in Europe lags behind compared with North America, where datasets tend to be larger and more homogeneous, lacking the complexities generated in Europe by language differences and variations in regulations across countries. A recent IDC survey 1 revealed that 29% of European companies (excluding very small organizations) considered themselves ready for Big Data, while over a half stated they were not, and the rest were undecided on their readiness. The level of awareness and readiness was significantly higher among very large companies but even in that segment Big Data-ready companies still remained a minority. The survey confirmed the relative immaturity of the market, as short-term intentions to increase usage of Big Data technologies were concentrated mainly among very large companies, and in sectors that were already at the forefront of adoption, such as large retailers and financial services organizations, while the majority of respondents did not have any immediate plans for these technologies. In addition, survey respondents affirmed that they used Big Data to extend or accelerate what they were already doing, rather than to generate completely new use cases. In fact, in terms of needs addressed and core functionality, Big Data can be seen as an evolution of business analytics and data centre technologies which have been in use for over a decade among larger enterprises, particularly in sectors such as financial services, telecommunications, and energy. These same enterprises are among those experiencing the most pressing need to go beyond their existing information management systems and reach a new level of performance by making full use of the amount of data they hold, as they are hitting the roof of what their current technologies can deliver. Therefore, although from a technology perspective Big Data comprises a set of genuinely new technologies (such as Hadoop or advanced data visualization tools) and is far more than an evolution of existing solutions, its adoption and near-term growth are still driven primarily by large enterprises, and by a selection of sectors that are more ready than others to recognize the opportunities that Big Data can open up. Nevertheless, the need to extract more value from the huge amount of largely unstructured data, which has become available thanks to new technologies, and to the expansion of social media, is strong across sectors and company sizes. So, as the market matures, and the benefits of Big Data technologies become increasingly available as a service, penetration is set to increase also among smaller businesses and in sectors that have traditionally lagged behind in technology adoption. While the growing expansion of available data is a recognized trend across countries and market segments, and the majority of companies are collecting, storing, and to a certain extent analyzing data, only a minority of companies have started to get valuable insights and real business value 1 Source: IDC European Enterprise Software Survey

6 from the available data. A recent worldwide survey 2 commissioned by Cisco showed that while 60% of respondents agreed that Big Data will help improve decision making and increase their competitiveness, with even higher scores in BRIC and emerging countries, only 28% of companies globally reported that they are currently generating strategic value from their data. IDC's European Vertical Markets Survey provides a pulse of current end-user attitudes and future investment plans regarding Big Data technologies. In particular, the survey demonstrates that very few European companies still believe that the fast expansion of (primarily unstructured) data will have limited effect on their business, and although the vast majority believes they can deal with it by expanding their storage capacity, a significant portion of companies believes they need to re-assess their current information management processes in order to meet the challenge of data growth. Moreover, larger enterprises are considerably more concerned with the impact of Big Data, and prepared to overhaul their existing processes, while SMEs expect a more limited impact, but they are not unaffected. Among vertical markets, the survey allows to identify a number of sectors in which data explosion is most likely to trigger actions, including telecom, media, transport and storage, and financial services, but also some manufacturing segments such as pharmaceuticals and equipment and machinery, while a wait and see approach is more widespread in most manufacturing sectors, including electronics, food beverage & tobacco, and other manufacturing segments, particularly those that are characterized by a larger number of small businesses. Current adoption of Big Data technologies, according to the same survey, is still relatively low (6.9% of business sector European companies with more than 10 employees 4 ), and concentrated in larger enterprises and in verticals such as oil and gas processing, financial services, telecom, where business analytics and datacenter technologies have been widely adopted in the past (Figure 1). Some manufacturing sub-sectors such as computer & electronics, and automotive & aerospace also include a relatively large share of early adopters of Big Data technologies, as shown in Figure 1, particularly among larger businesses. Conversely, penetration is well below average in most other vertical markets, and is still very low among SMEs: only 6.2% of companies with between 10 and 250 employees have already adopted Big Data technologies (Figure 2), against just over 30% penetration among companies with more than 250 employees. A gap in adoption between large enterprises and SMEs will continue to exist, nearly half of SMEs stated they are not likely to adopt, and a significant 15.3% share is undecided but penetration is set to increase significantly in the SME segment as well over the next two to three years, reaching over one third of companies with less than 250 employees. Among larger businesses, penetration of Big Data technologies is deeper, already nearing 31%, and is set for 2 Source: 2012 Cisco Connected World Technology Report, commissioned by Cisco and run among IT professionals in 18 countries worldwide, including 4 European countries: U.K., France, Germany, and Netherlands 3 Source: IDC estimates based on the IDC European Vertical Markets Survey Data refer to companies with 10+ employees. 4 Source: Data in survey are weighted by number of enterprises 6

7 steady increases over the next few years, as over 70% of companies with over 250 employees expect to start using them by Among vertical markets, adoption rates are likely to remain stronger in the sectors where current adoption is higher financial services, oil and gas processing, telecom, computers and electronics - while adoption in manufacturing sectors will increase, but still lag compared with others. Alongside these selected verticals, relatively strong increases in penetration are also expected in media and transport & storage in the very near term and over the longer period. Strong increases are expected in the short to medium term in professional services and equipment and machinery. As discussed, there is significant variation across vertical markets in current technology penetration and future adoption plans for Big Data, as well as in the underlying needs for data analysis to support the business. Key survey results are summarized below by vertical market. Telecom companies are among the forerunners for Big Data adoption, while media companies reveal a strong propensity to catch up in the very near term and over the longer period. Indeed, both sectors are among those where data explosion driven by dataintensive applications such as call data records, network traffic monitoring and digital content and asset management, is most likely to trigger action in terms of organizational change as well as increases in storage capacity. In both sectors, an above average share of respondents are oriented to re-asses their information management processes as a consequence of data explosion, and very few affirmed that it will have limited or no effect. Moreover, among the respondents affirming that they can deal with increasing amounts of data by expanding storage capacity, a good portion already have a Big Data solution in place. Current adoption of Big Data technologies in telecom neared 40% in October 2012, with close to 40% of respondents planning to adopt them within the next three years. A similar percentage of European media companies is expected to invest in big data technologies over the next three years, with adoption rising from 8% to close to 46%. The energy sector where the amount of available data increased exponentially in recent years, for example with the introduction of smart meters and digital seismic sensors, and where the potential impact of better data usage on business performance is critical is also among the forerunners in Big Data adoption. Adoption of Big Data technologies appears particularly widespread in the oil and gas processing sector, reaching 46%, but is also significantly above average among utilities. Plans for new technology adoption are less strong than in other verticals, and the percentage of respondents in both energy sectors that stated they are not likely to adopt Big Data technologies is considerably high. Considering the relative maturity of the energy sector compared with other verticals, combined to very few companies that believe they won't be affected by data growth, it is fair to presume that a good portion of the energy companies that are not planning to adopt Big Data technologies will still have a strategy related to Big Data, but will leave the data crunching part to other companies within their group or to external service providers. 7

8 Financial services is among the sectors in which data explosion is most likely to trigger action. The focus in this sector is primarily on data that can be gathered through internal sources and cross-analyzed in order to produce information for behavioral profiles, risk assessment, or investment opportunities, while the pressure from the use of social media is still not very strong in finance. Current adoption of Big Data technologies is above average, 29.6%, and plans for adoption are strong particularly among banks and in the midsize segment. The share of finance companies that are not likely to adopt Big Data technologies in the short to medium term is below average but still significant (31.1%), and considering the relative maturity of this sector we believe that non-adopters of Big Data technologies will still be users of the information generated through Big Data for them by internal or external service providers. Distribution, Hotels and Restaurants companies overall are relatively close to the market average in terms of adoption and plans for Big Data technologies, and in terms of attitude to data growth, but there are significant differences between the retail and wholesale sectors and across company size classes. Retail companies, particularly the very large ones, are more oriented toward a reassessment of their information management processes, and relatively few believe they won't be affected by data growth. In contrast, among wholesalers, only a small minority is prepared to change their information management processes, with the vast majority appearing unconcerned. Likewise adoption (6.8% overall) is negligible among wholesalers and retail SMEs, with below average plans in both segments, and highest among very large retailers. Around 28.8% of companies in the distribution, hotels and restaurants sector plan to adopt Big Data technologies within the next three years, and this percentage is significantly higher in companies with 1000 or more employees in this sector. Nearly half of distribution, hotels and restaurants entrepreneurs are not likely to invest in Big Data technologies in the short to medium term. Manufacturing subsectors reveal striking differences among them both in terms of attitude to data growth and adoption of Big Data technologies. In terms of adoption, the aggregated sector appears as a laggard, with some 6.2% of current adopters, a further 26.4% planning to invest, and the majority not likely to invest in the next three years (52%) or unsure about their plans (14.7%). In reality, most of the key manufacturing sub-sectors, and particularly the SME portion of them could be classified as laggards for Big Data technologies, for example food beverage & tobacco, equipment and machinery and selected segments of other manufacturing and process manufacturing, all revealing low current adoption and to some extent lower than average plans. But other sectors such as automotive & aerospace or computers & electronics reveal significantly more interest for Big Data technologies. 8

9 Professional services companies overall are close to the market average in terms of attitude to data growth, though with a lower share of companies believing they won't be impacted, and are characterized by a higher than average adoption of Big Data technology, with just above average future plans and a slightly lower than average share of companies having already written off technology adoption. However, there are large variations across subsectors. IT services and software companies are much less oriented toward the reassessment of their information management processes, and a larger share among them plans to deal with data growth by expanding storage capacity. Penetration of Big Data in this sector is 14.4% against an average of 6.8% and is set to increase to close to 40% in the next three years. Among other subsectors, audit and tax, and engineering & architecture are the most oriented toward a change in their information management processes; current adoption is higher than average among business consulting and audit & tax services companies and a leap in adoption is expected in the short term in the legal sector. Transport and storage companies are among the laggards in current adoption of Big Data technologies, with only 3.1% of adopters, although the sector is set for a strong jump within the next two years, as nearly 36.4% of respondents revealed adoption plans. New adopters will be concentrated among larger transport companies, particularly in the short term, but midsize companies will follow in the next two or three years, while small businesses will continue to lag. In terms of needs the sector is also largely polarized. Overall, a large share of respondents consider the reassessment of their information management processes, and a similar high percentage would deal with data explosion through additional storage capacity, while only a small percentage of transport companies believe data growth will have no impact on them. But while very few SMEs in this sector are prepared to change their organizational processes due to data growth and the majority are oriented towards an increase in storage capacity or are unsure of the impact, the vast majority of large transport companies see a need to reassess their processes. 9

10 Figure 1: European Companies' Current and Planned Adoption of Big Data by Vertical, 2012 (Source: IDC on IDC European Vertical Markets Survey 2012) Q.: Making use of the available data requires new tools and methodologies. To what extent have you adopted or expect to adopt any of the following methodologies and tools? Big Data technologies e.g. video analytics, social analytics Oil&Gas processing Finance Telecom Computers and Electronics IT (software and services) and other information services Automotive & Aerospace Utilities Mining Professional services Equipment and machinery Media Business sector (10+) Distribution, Hotels and Restaurants Process manufacturing Other manufacturing Food, drink and tobacco (CPG) Construction Transport and Storage 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Already adopted Will start using within 18 months Likely to start using within 2 3 years Not likely to adopt it Don t know Note: Data refer to companies with 10+ employees and are weighted by number of enterprises Figure 2: European Companies' Current and Planned Adoption of Big Data by Size, 2012 (Source: IDC on IDC European Vertical Markets Survey 2012) Q.: Making use of the available data requires new tools and methodologies. To what extent have you adopted or expect to adopt any of the following methodologies and tools? Big Data technologies e.g. video analytics, social analytics Business sector LEs (250+) Business sector SMEs (10 249) 0% 20% 40% 60% 80% 100% Already adopted Likely to start using within 2 3 years Don t know Will start using within 18 months Not likely to adopt it Note: Data refer to companies with 10+ employees and are weighted by number of enterprises 10

11 4. Drivers and Barriers The key trigger for the strengthening impact of Big Data on businesses across all sectors, and for the development of Big Data technologies, is the overwhelming data explosion that we have all witnessed over the past few years. IBM 5 states that we currently create 2.5 quintillion bytes of data each day, so much that 90% of the data in the world today has been created in the last two years alone. A large part of this data is made of unstructured and complex content, and from data sources that did not exist or were in their early introduction stage only a decade ago, from social media, to digital imaging and video, digital sensors, smart meters, and other nontraditional networked "smart devices", or from machine-to-machine communication in automated factories. A large portion of this data is more valuable when actionable information is created and used in real-time, and data ages quickly in today's interconnected world. New technologies developed and introduced by IT vendors now provide the tools to collect, store, analyze, and exploit an increasing share of this amount of available data. Adoption of these technologies is still in early stages, but the smarter exploitation of data sources can commence even before the implementation of a complete end-to-end Big Data solution, and information production and usage can proceed alongside technology adoption, as long as technology adjusts to evolving business needs. On the demand side, customers and consumers in particular are increasingly connected and demanding in terms of the availability and accessibility of readily-usable information, and new generations are increasingly willing to give up some of their rights to privacy to commercial entities in exchange for more personalized services. Commercial use of artificial intelligence will widen as biometric, audio, video, and image recognition software, Big Data analytics, and commercialized high-performance computing infrastructure will combine to create intelligent sense-and-respond systems, and intelligent data collection and question and answer systems will be present across consumer industries, operating within call centers, in retail stores, on the web and mobile applications, but also in less traditional environments, such as cars and domestic appliances. Going forward, Big Data analytics technology deployments using real-time monitoring and analysis techniques will change the way the public views privacy, existing legislation will be outpaced by technological advances, and a new generation of legislators might find privacy issues less pressing. A number of obstacles to the full embracement of Big Data technologies still exist, and will persist in the near future. First of all, in order to fully embrace Big Data, organizations need to be dedicated and determined to embrace a more information-led culture. Cultural and organizational barriers range from a resistance to share personal or proprietary data and information, to doubts related to the nature of the trend and the technologies involved, as a minority but still significant share of companies, in Europe in particular, still think that Big Data is just a new buzzword for old stuff. IDC's recent surveys revealed that over one-quarter of European respondents were skeptical toward the term "Big Data" and considered it as just a new 5 IBM public marketing material on Big Data 11

12 name for their current activities. While in some cases this response came from sectors that are relatively mature in their use of advanced storage and analytics technologies, in other cases it was a sign of low familiarity with the trend, or of a defensive attitude toward change. Lack of trust in the data collected and in the results of the analysis can represent a further barrier to Big Data adoption: some data is inherently uncertain, as it is heavily conditioned by unpredictable natural, economic, or other forces and wild cards, or because it could be (willingly or accidentally) manipulated within social media or other human environments. The first obstacles to overcome therefore relate to the need for companies to understand how data and information can be used, and to build a compelling business case for Big Data technology adoption. Lack of budget and lack of time and resources to study the trend and its potential opportunities and threats are another key barrier, particularly in European countries where economic and business conditions and outlook remain downbeat. Each use case requires different combinations of software, hardware, and services to be most effective, therefore, particularly in the early stages of adoption, the costs related to identifying and implementing an appropriate solution for a new use case are higher. In addition, in Europe language differences add to the complexity of the data that needs to be analyzed, and therefore add to the costs of Big Data implementations, particularly for companies operating across different countries, that are also impacted by additional complexity due to differences in national regulations regarding data privacy and data usage. Additional barriers might come from the industry structure and interrelations among sectors, in particular between data owners and data users. In a number of possible use cases, the data needed to improve productivity in a particular industry exists, but it lies in domains where data usage is heavily regulated or is owned by companies that might be potentially damaged by the use of their data by other market players. The first case regards the use of any personal or corporate data of a sensitive nature that would be profitably used in other sectors, for example health-related data would be in demand by pharmaceutical and personal care manufacturers, data related to financial transactions and payments would potentially be of interest for targeted marketing campaigns for retailers, manufacturers, and personal service providers. The second case is more connected to each industry's structure and to the relationships among partners along the value chain, the strength of reciprocal trust, and the premium placed on cooperation versus competition. Detailed information on end-clients collected by retailers would be highly valuable for manufacturers, particularly in consumer facing sectors and in those where the distribution channel is largely multi-tiered, but channel partners might feel threatened in their market position should they give up part of their customer intimacy to their partners. Likewise, sharing highly detailed information on engineering plans and manufacturing processes surely increases suppliers' productivity but also enables them to apply what they learnt to the components they provide to competing manufacturers, or to become a competitor themselves. However, probably the strongest concerns around Big Data are linked to security and to the lack of the appropriate skills. Data security is a major concern among European companies across sectors, as consistently highlighted by IDC's surveys in recent years. Sound security, 12

13 governance, and risk management processes need to be put in place, as Big Data adds legal, ethical, and regulatory considerations to data analysis efforts, and introduces new risks when data is made public or personal data is used, expanding the potential for public missteps which could bring about fines and permanent damages to the company's image and respectability. The lack of skills relates both to IT staff able to implement and maintain the appropriate advanced technologies, and to the insufficient number of analysts able to correctly interpret the data and generate actionable information for the business creating value. In particular, a key element of Big Data is the newest technology involved, Hadoop, an open source processing framework that allows large analytical queries to be broken down to many small queries that can be run in parallel, and then reassembles the results into one dataset. Hadoop skills are in short supply and experience of building, implementing and managing Hadoop environments are even rarer, although the ecosystem is expected to grow very quickly. Currently, the vast majority of Hadoop projects are pilots and proof of value, and there are very few live production environments in Europe. In addition to technical skills, IT professionals need to be trained in specialized areas, and Big Data projects need to span multiple lines of business requiring a strong attitude to collaboration. Both business and IT people are needed with the special skill set and creativity to imagine and realize data's full potential, in addition to business analytics strategy and project management capabilities. 13

14 5. Opportunities for Digital Entrepreneurship As the attitude toward open data, or making data publicly available, changes among businesses, as well as consumers and the public sector, new opportunities for entrepreneurs will open up. The huge amounts of publicly available data will trigger new needs for data analysis and the creation of readily-usable information, and will stimulate new business ideas for the deployment of innovative services and the development of specific applications. As such, it is possible to categorize opportunities in different areas, both supply and demand side: On the supply side, opportunities will be linked to the launch of new services and/or the establishment of new businesses offering Big Data related services/technologies On the demand side, opportunities will be linked to the value generated in both horizontal and vertical specific processes of EU companies across all vertical markets Supply-side Business opportunities The shortage of IT skills, particularly analytics and Big Data technology skills, and the scarcity of budget for high-end technology implementations, will create opportunities for service providers offering cloud and hosted solutions. Opportunities lie ahead for the development and provisioning of content-enabled applications, case management and related services. Beyond technology, advanced analytic capabilities are required in order to extract value from data, so more companies will look to outsource analytic services, and data as a service solutions offered by analytics service providers will expand rapidly. "Data scientists", combining analytical and statistical skills with an appropriate level of business understanding, will be in high demand, as analytic service providers will be required to join an in-depth knowledge of statistical tools and techniques with the business acumen to apply their findings to the organization, and with the communication skills to explain the concrete implications of their findings to business executives in creative and visual ways. Many end-users will want more than just technology or data analysis, and new consulting opportunities will arise for business analytics architects, particularly for SMBs and less mature sectors, that typically don't have a business analytics strategy, as companies will look for advice on what data to track, how to analyze it to respond to specific business issues, and how to influence action and drive effective change based on the result of the analysis. The possible use cases of Big Data cover a broad range of functionalities, and in theory each specific use case could spur a set of new opportunities for digital entrepreneurs willing to offer services or develop applications in specific niche areas. Although the best known use cases are related to online media and social networking, early deployments have been strongly focused on addressing customer-centric objectives, based primarily on available internal data sources, and 14

15 the potential is much broader and diversified. A recent global study by IBM 6, with around onequarter of respondents in Europe, found that only a small percentage of respondents defined Big Data as mainly social media data, and fewer than half of respondents with Big Data initiatives reported collecting and analyzing social media data. Instead, most respondents used existing internal data sources, particularly those related to transactions and interactions with customers, in their current Big Data efforts. Big Data, through the analysis of data from transactions, multichannel interactions, social media, loyalty cards, and other customer-related information, provides companies with the ability to obtain a complete picture of their customers' preferences and demand, and to better understand and predict customer behaviors, and by doing so, improve the customer experience. Through this deeper understanding, organizations of all types are finding new ways to engage with existing and potential customers, across sectors such as retail, telecommunications, banking and finance, and consumer products where end-consumers are involved, but also in business-to-business interactions among partners and suppliers. Therefore most of initial supply-side opportunities will be found around market and customers' analytics. However, as described above, other areas will be interesting going forward. They include (Figure 3): Enhanced ecommerce and web market-places. A twofold opportunity; on the supply-side, these actors can offer Big Data services to companies participating to the marketplace. Always on the supply-side, new technology companies can offer dedicated services based on advanced algorithms to web market-places operating in different areas (ecommerce, real estate, travel, entertainment, etc). On the demand-side, Big Data will facilitate the emergence and enhancement of existing marketplaces thanks to the personalized and enhanced offerings. Analytics software (beyond customer analytics) and related services. Of course the supplyside opportunity will be linked to the Big Data demand/unmet need in different business processes and vertical markets. An analysis of these opportunities is presented in the following chapters. Big Data outsourcing, hosting, and as-a-service offerings, related to different Big Data opportunity areas. These opportunities will be evident for ICT providers, but also for other large companies that, by nature, deal with large amount of data, such as already mentioned web market-places, retailers, banks, etc. These companies can become Big Data analysis providers to the benefit of the many EU SMEs that cannot afford developing in-house an advanced analytic strategy. Big Data consulting. Similarly to the above, Big Data will create opportunities for players offering consulting services on how to leverage it in the most effective way. Data scientists will be also in demand at large organizations willing to deploy effective in-house analytic strategy, thus creating opportunities for new jobs not necessarily just among ICT players. Remote monitoring services in specific industries. 6 Source: the IBM Big Work Survey conducted in mid-2012 among 1,144 professionals from 95 countries, with 23% of respondents in Europe 15

16 Support enhanced citizen and patients services based on data insights, as the public sector can largely benefit from the reliance on Big Data. Moreover, like with any new technology, Big Data opens up opportunity for R&D companies, public institutions, education institutions and academics to study advances in the core Big Data solutions and techniques to collect, manage, analyze, visualize, and extract useful information from large, diverse, distributed and heterogeneous data sets in different vertical markets. Figure 3: Supply-side Big Data/Analytics Business Opportunities E-commerce/webbased market places Market, customer and other analytics Big Data outsourcing, hosting, cloud and/or consulting - Consulting opportunities for business analytics architects, particularly to SMEs and less mature sectors that typically don't have a business analytics strategy, as many endusers will want more than just technology or data analysis. - Advice on what data to track, how to analyze it and how to influence decisions and drive effective change based on the results. - Cloud and hosted solutions driven by shortage of skills and scarcity of budgets for high end implementations Remote monitoring services in specific industries - Providing new services to companies willing to reduce costs and improve their services in such vertical markets as energy, banking, security, healthcare, etc - Monitoring and analyzing remotely collected data from people/patients, vehicles or things/sensors - Analyzing data to monitor adherence to pre-set criteria. Support enhanced public services - Dedicated access to advanced analytics for niche retailers/players in the marketplace (which otherwise would have not the resources to leverage them) - Internet display advertising - Personalized services/offers. - Searchable product listings from different vendors/players including also consumer generated reviews - Real time or nearly real time pricing. - Development and provisioning of analytic content-enabled applications, case management and related services. - Development of horizontal and industry specific analytics (cloud based or traditional) See following chapter for a top level description of most interesting processes to watch - Leveraging governments' move to open data and other information from sources such as social media to design new services - Supporting government in monitoring initiatives to understand efficiency of programs and decide accordingly. - Segmenting population to create effective services - Creating new algorithms to support decision making and address specific public sectors' needs (including fraud detection, analysis of productivity levels of agencies, etc). 16

17 5.2. Demand-Side Business opportunities: Functions and Horizontal Business Processes Data sources to be leveraged through Big Data technologies are very diverse in type, origin, format, and content, and even more diverse are the uses that companies and innovative entrepreneurs can imagine for them. Use cases can be identified along three dimensions: activity, business process, and industry. From the activity point of view, examples include: Information access (e.g., search-based access to information, normalization, and access across content and data sources) Analytics (e.g., data mining, multi-dimensional analysis, data visualization) Operations (e.g., platform for online social networking, gaming, retail transaction processing, large dataset write backs) Big Data technologies are being deployed in support of processes, and the challenges and problems they are called to meet are not Big Data challenges but rather business or organizational challenges that are impacted by Big Data. Therefore Big Data technology opportunities can open up around typical horizontal business processes such as: Customer relationship management (sales, marketing, customer service, etc.) Supply chain Operations Administration (focused on finance and accounting, human resources, legal, etc.) Research and development Information technology management Risk management As already highlighted above, most of current opportunities focus on customer relationship management. A recent Skytree survey 7, run in January 2013 among nearly 500 companies, confirms this. The majority of the respondents identified themselves as Data Scientists or BI/ Data Architects (42%) and indicated Marketing as the business process with the most to gain from Machine Learning/advanced analytics projects (54%- Figure 4). 7 Source: Skytree. Big Data Analytics

18 Figure 4: Business Units benefitting from Machine Learning/Advanced Analytics (Source: SKYTREE Big Data Analytics 2013) Q.: Which business units (will) benefit from Machine Learning? Marketing 54% Operations 48% IT 44% Sales 39% Finance 25% Other 15% 0% 10% 20% 30% 40% 50% 60% Note: Multiple responses were allowed on this question This is not surprising since one of the clearest and fastest ways to see the immediate impact of Machine Learning is through predictive analytics as found in the survey (Figure 5), which enables organizations to better profile their customer segments and create more targeted marketing programs to accelerate growth and sales opportunities. Even though Marketing (54%) stood out as the biggest benefactor of Advanced Analytics, it was clear that ALL areas of the organization could benefit from Machine Learning. Operations (49%), Sales (39%), IT (44%), and Finance (25%) responded favorably to the immediate value and future impact of Machine Learning to their business needs. 18

19 Figure 5: Use cases for Machine Learning/Advanced Analytics (Source: SKYTREE Big Data Analytics 2013) Q.: What are your use cases of Machine Learning? Predictive Analytics 79% Optimization 49% Recommendation 49% Scoring 42% Text Analytics 39% Fraud/Risk Management/Security 34% Other 7% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Note: Multiple responses were allowed on this question Looking into companies' key horizontal processes, it is therefore possible to identify several areas of business opportunities. They include (but are not limited to): R&D, Idea generation and PLM (Product Life cycle Management Figure 6) Production, operations and delivery: smart production, distribution and logistics optimization, demand and supply chain management, inventory management and optimization (Figure 7) Sales and marketing: customer micro-segmentation, social & mobile media real-time data analysis, product cross selling to customers, dynamic price setting, location-based marketing and sales (Figures 8 A&B) Customer support: improved customer experience (Figure 8B) Administration and support: decision-support systems, labor planning optimization, fraud and fault detection (Figure 9) Although it is possible to identify opportunities in each business process, one shouldn't think at Big Data with a silos approach. An effective use of advanced analytics spans across the different processes, and links R&D, operations, sales and customer services to create real business value. 19

20 Figure 6: R&D Big Data/Analytics Business Opportunities Idea generation (Services industries) - Advanced analysis of users' needs, profiles and interests - Predictive analytics of buying behaviors - Socialitycs - Enabling experimentation at the conceptual stage - Crowdsourcing R&D (Process manufacturing industries) - Leveraging predictive modeling - Enhancing clinical trials design and analysis (pharma) - Building consistent interoperable, crossfunctional R&D along supply chain to enable rapid experimentation, simulation, and co-creation. PLM (Discrete manufacturing industries) - Creating product lifecycle management (PLM) platforms that can integrate datasets from multiple systems to enable effective and consistent collaboration - Providing a platform for co-creation, e.g., bringing together internal and external inputs to create new products - Enabling experimentation at the designing stage - Building interoperable, cross-functional product design databases to enable concurrent engineering, simulation, and co-creation. - Evaluating product performance based on "phone home" features to improve products' features. Figure 7: Production, Operations and Delivery Big Data/ Analytics Business opportunities Distribution and Logistic Optimization - Optimizing transportation by using GPS-enabled big data telematics (i.e., remote reporting of position, etc.) and route optimization to improve fleet and distribution management or enable a range of personal safety and monitoring services - Transportation analytics optimizing fuel efficiency, preventive maintenance, driver behavior, and vehicle routing - Embedding real-time, highly granular data from networked sensors in the supply chain (but also production processes) - Smart routing based on realtime traffic information Smart production Demand and supply management -Enabling real-time analysis of demand and stocks to enhance flexibility and responsiveness - Implementing advanced demand forecasting and supply planning across suppliers - Integrating data from different sources including promotion data (e.g., items, prices, sales), launch data (e.g., specific items to be listed/delisted, ramp-up/rampdown plans), and inventory data (e.g., stock levels per warehouse, sales per store) - Embedding Phone Home features in products using sensors that generate and transmit back data on actual product usage and performance Inventory management optimization - Implementing bar code systems connected to automated refill processes to minimize risks of running out of stock - Improving stock forecasting by combining proprietary data such as sales historicals, seasonal sales cycles, and non-proprietary data like weather forecasts for example - Relying on demand signals analytics, to maximize revenues and minimize sales losses due to merchandise stock-outs - Applying advanced analytics to create a digital model of the entire manufacturing process - Implementing lean manufacturing and digital factory models' to enhance process transparency, develop dashboards, and visualize bottlenecks - Implementing sensor datadriven operations analytics to optimize processes, maximize yield and/or improve throughput, and enable mass customization 20