Trends Identified

Augmented reality
Augmented-reality-based systems support a variety of services, such as selecting parts in a warehouse and sending repair instructions over mobile devices. These systems are currently in their infancy, but in the future, companies will make much broader use of augmented reality to provide workers with real-time information to improve decision making and work procedures.
2015
Nine Technologies Transforming Industrial Production
Boston Consulting Group (BCG)
The center of economic gravity is shifting east and south, propelled by high-growth emerging economies and globally competitive companies
Emerging economies led by China and India have accounted for almost two-thirds of global GDP growth and more than half of new consumption in the past 15 years. Among emerging economies, our research has identified 18 high-growth “outperformers” that have achieved powerful and sustained long-term growth—and lifted more than one billion people out of extreme poverty since 1990. Seven of these outperformers—China, Hong Kong, Indonesia, Malaysia, Singapore, South Korea, and Thailand—have averaged GDP growth of at least 3.5 percent for the past 50 years. Eleven other countries (Azerbaijan, Belarus, Cambodia, Ethiopia, India, Kazakhstan, Laos, Myanmar, Turkmenistan, Uzbekistan, and Vietnam) have achieved faster average growth of at least 5 percent annually over the past 20 years. Underlying their performance are pro-growth policy agendas based on productivity, income, and demand, and often fueled by strong competitive dynamics. The next wave of outperformers now looms as countries from Bangladesh and Bolivia to the Philippines, Rwanda, and Sri Lanka adopt a similar agenda and achieve rapid growth. The dynamism of these economies has gone hand in hand with the rise of highly competitive emerging-market firms, which are increasingly taking on incumbents in advanced economies. On average, outperformer economies have twice as many companies with revenue over $500 million as other emerging economies. In addition to driving economic growth at home, they now play a disproportionately large role on the global stage: while they accounted for only about 25 percent of the total revenue and net income of all large public companies in 2016, they contributed about 40 percent of the revenue growth and net income growth from 2005 to 2016. More than 120 of these companies have joined the Fortune Global 500 list since 2000, and by several measures, they are already more innovative, nimble, and competitive than Western rivals. For example, our surveys show that they derive 56 percent of their revenue from new products and services, eight percentage points more than their peers in high-income economies, and make important investment decisions six to eight weeks faster. They can also earn better returns for investors. Between 2014 and 2016, the top quartile of outperformer companies generated total return to shareholders of 23 percent on average, compared with 15 percent for top-quartile firms in highincome countries (Exhibit 1).
2019
Navigating a world of disruption
McKinsey
Globalization patterns are changing, with rapid growth in data flows and a larger role for highgrowth emerging economies
Much of the recent focus on globalization has been on trade pullbacks, rising protectionist measures, and public hostility. As a phenomenon, however, globalization has not gone into reverse; rather, it has shifted gears to become more data-driven and more focused on South- South flows. The seeming flattening of globalization that followed the 2008 financial crisis disguises new patterns of connectedness. While cross-border flows of goods and finance have lost momentum, data flows are helping drive global GDP. Cross-border data bandwidth grew by 148 times between 2005 and 2017, to more than 700 terabytes per second—a larger quantity per second than the entire US Library of Congress—and is projected to grow by another nine times in the next five years as digital flows of commerce, information, searches, video, communication, and intracompany traffic continue to surge. In line with its rising economic role, the developing world is now driving global connectedness. For the first time in history, emerging economies are counterparts on more than half of global trade flows, and South-South trade is the fastest-growing type of connection. In the MGI Connectedness Index, Singapore tops the latest rankings, followed by the Netherlands, the United States, and Germany. China has surged from number 25 to number seven. South-South and China-South trade jumped from 8 percent of the global total in 1995 to 20 percent in 2016. The shifting nature of the Chinese economy, toward a more R&D-intensive focus and away from low-cost manufacturing, plus China’s push through the Belt and Road initiative, may begin to create a new trade ecosystem with China at the core. By comparison, North-North trade and North-South trade have declined as a share of total trade, especially since the 2008 financial crisis. North-North trade is now 33 percent of the total, versus 43 percent in 2005 and 55 percent in 1995. Amid these shifts, our latest research suggests that China’s relationship with the world may be at a turning point. By 2017, China accounted for 15 percent of world GDP. It overtook the United States to become the world’s largest economy in purchasing power parity terms in 2014, according to International Monetary Fund data—for the first time since 1870. (In nominal terms, China’s GDP was 64 percent of US GDP in 2017, making it the secondlargest economy in the world). Behind these headline numbers lies a less-noticed shift: over the past decade, even as its economy has grown, China’s exposure to the world, as measured by the magnitude of flows of trade, technology, and capital with the rest of the world relative to its economy, has declined. At the same time, the world’s exposure to China (the magnitude of flows with China relative to the global economy) has increased since 2000. Metrics used to measure exposure include China’s importance as a market and supplier of goods and services; the importance of Chinese technology exports for global R&D spending; and China’s importance as a supplier of financing (Exhibit 2). Global value chains are also evolving. They are being reshaped in part by technology including automation, which could amplify the shift toward more localized production of goods near consumer markets. And they are changing along with global demand, as China and other developing countries consume more of what they produce and export a smaller share. As emerging economies build more comprehensive domestic supply chains, they are reducing their reliance on imported intermediate inputs. The result is that goods-producing value chains have become less trade-intensive, even as cross-border services are growing briskly—and generating more economic value than trade statistics capture, according to our analysis. Trade based on labor-cost arbitrage has been declining and now makes up only 20 percent of goods trade. Global value chains are becoming more knowledge-intensive and reliant on high-skill labor. Finally, goods-producing value chains (particularly automotive as well as computers and electronics) are becoming more regionally concentrated as companies increasingly establish production in proximity to demand.
2019
Navigating a world of disruption
McKinsey
The pace of technological progress is accelerating, bringing significant opportunities to create value even as it redefines the future of work
Digital technologies have been reinventing the way we live, work, and organize. Smartphones, the mobile internet, e-commerce, and cloud-based services have opened the door to more mobility and convenience as well as to greater competition. Businesses have been harnessing advanced analytics and the Internet of Things to transform their operations, and those in the forefront reap the benefits: companies that are digital leaders in their sectors have faster revenue growth and higher productivity than their less-digitized peers. They improve profit margins three times more rapidly than average and are often the fastest innovators and the disruptors of their sectors. The forces of digital have yet to become fully mainstream, however. On average, industries are less than 40 percent digitized, despite the relatively deep penetration of these technologies in media, retail, and high tech. Now comes the next wave of innovation, in the form of advanced automation and artificial intelligence (AI). An explosion in algorithmic capabilities, computing capacity, and data is enabling beyond-human machine competencies and a new generation of systemlevel innovation. Machines already surpass human performance in areas like image recognition and object detection, and these capabilities can be used to diagnose skin cancer or lip-read more accurately than human experts. Combining these capabilities is leading to system-level innovation, for example the driverless car, which takes advantage of innovations in sensors, LIDAR, machine vision, mapping, satellites, navigation algorithms, and robotics. Our research finds that companies in the forefront of adopting AI are likely to increase employment rather than reduce it, as innovationfocused adopters position themselves for growth, which tends to stimulate employment. These technologies still have limitations, and deployment can be complex. Nonetheless, productivity gains across sectors are already visible, with AI use cases in functions such as sales and marketing (e.g., “next product to buy” personalization), supply chain and logistics, and preventive maintenance. Our analysis of more than 400 use cases across 19 industries and nine business functions found that AI could improve on traditional analytics techniques in 69 percent of potential use cases. Deep learning could account for as much as $3.5 trillion to $5.8 trillion in annual value, or 40 percent of the value created by all analytics techniques (Exhibit 3). For the global economy, too, AI adoption could be a boon. A simulation we conducted showed that AI adoption could raise global GDP by as much as $13 trillion by 2030, or about 1.2 percent additional GDP growth per year. AI could also contribute to tackling pressing societal challenges, from healthcare to climate change to humanitarian crises; a library of social good use cases we collected maps to all 17 of the UN’s Sustainable Development Goals. Yet AI is not a silver bullet. Significant bottlenecks, especially relating to data accessibility and talent, will need to be overcome, and AI presents risks that will need to be mitigated. It could introduce or exacerbate social challenges, for example through malicious use or abuse, bias, privacy invasion, or lack of transparency.
2019
Navigating a world of disruption
McKinsey
Aging populations are forcing developed regions worldwide to rely more on waning productivity and greater migration to propel growth
Labor productivity growth has waned and is near historic lows in the United States and much of Western Europe, despite a job-rich recovery after the global financial crisis. Productivity growth averaged just 0.5 percent in 2010–14, down from 2.4 percent a decade earlier. This productivity growth weakness comes as birth rates in countries from Germany, Japan, and South Korea to China and Russia are far below replacement rates and working-age population growth has either slowed or gone into reverse. In some countries with declining populations, such as Japan and Germany, some cities are shrinking. Among their other effects, these demographic trends put a greater onus on productivity growth to propel GDP growth; over the past 50 years, just under half of GDP growth in G-20 countries came from labor force growth, while productivity growth accounted for the remainder. Digitization, often involving a transformation of operating and business models, promises significant productivityboosting opportunities in the future, but the benefits have not yet materialized at scale in productivity data because of adoption barriers and lag effects as well as transition costs. Our research suggests that productivity could grow by at least 2 percent annually over the next 10 years, with 60 percent coming from digital opportunities. However, while crisis-related aftereffects are diminishing, long-term drags on demand for goods and services may persist and hold back productivity, a result of changing demographics, declining labor share of income, rising income inequality, polarization of labor markets, and falling investment rates. In terms of consumption, the aging population in many developed countries (that is, the retired and elderly over 60) are increasingly important drivers of global consumption. The number of people in this age group will grow by more than one-third, from 164 million today to 222 million in 2030. We estimate that they will generate 51 percent of urban consumption growth in developed countries, or $4.4 trillion, in the period to 2030. That is 19 percent of global consumption growth. The 75-plus age group’s urban consumption is projected to grow at a compound annual rate of 4.5 percent between 2015 and 2030. In addition to increasing in number, individuals in this group are consuming more, on average, than younger consumers, mostly because of rising public and private healthcare expenditure. Retirees and the elderly in developed economies today have per capita consumption of around $39,000 per year. In comparison, the 30-to-44 age group consumes on average $29,500 per year. Healthcare spending by those aged 60 and older is projected to grow by $1.4 trillion in the period to 2030. With low fertility in the developed world, migration has become the primary driver of population and labor force growth in key developed regions worldwide. Since 2000, growth in the total number of migrants in developed countries has averaged 3.0 percent annually, far outstripping the 0.6 percent annual population growth in these nations. First-generation immigrants constitute 13 percent of the population in Western Europe, 15 percent of the population in North America, and 48 percent in the Gulf Cooperation Council countries. Besides contributing to output today, immigrants provide a needed demographic boost to the current and future labor force in destination countries. Improving the old-age dependency ratio is of critical importance to countries like Germany, Spain, Canada, and the United Kingdom, where most public pensions have a pay-as-you-go structure and worsening dependency ratios threaten to make many plans unsustainable.
2019
Navigating a world of disruption
McKinsey
Technologies are converging across multiple industries
"The financial services industry pioneered technology that enables very high speed marketplaces and machine-to-machine communications. Some of these technologies are now being applied in other industries as well as in non-financial markets everywhere. Take general-purpose time series databases and Field Programmable Gate Array (FPGA), software embedded on hardware to allow a much more deterministic speed, as examples. These technologies have been essential to financial services firms for some time. Now cloud providers are deploying both, and automakers are using FPGA within their vehicle systems. Simultaneously, the financial services industry is looking at how other industries are leveraging technology to increase efficiency, reduce risk, improve customer service and gain competitive advantage. To illustrate, the Internet of Things (IoT) is taking machine-to-machine communications to the next level in many areas including agriculture and supply chain management. GPUs used in gaming are enabling machine learning, which is being applied across all industries. Financial firms are moving away from technology islands and leveraging technology architectures and designs in their core infrastructure that are similar to those used in other industries. Examples include distributed global connectivity solutions used in telecommunications, and global networks and platforms that have been within the purview of Google and Facebook. Tech convergence is all about applying technology in creative ways to solve problems, accelerate innovation and meet customer needs. Nasdaq is working constantly to spot new trends and exploring opportunities to adopt new technologies where appropriate. "
2019
NASDAQ DECODES: TECH TRENDS 2019 -The technology trends that are driving the world of markets forward
Nasdaq
Open source is enabling community problem solving and differentiation
Often a problem is widely experienced by many different firms. Instead of solving it individually and sub-optimally, it makes sense to band together and solve it as a community. The open source model enables companies to tap into a community dedicated to building modern software, and to align with vibrant, active projects. As such, companies can accelerate innovation on the differentiating parts of their platform while leveraging the underlying foundational innovation of the broader open source community. The open source model lowers costs and in some cases achieves vendor independence. Notably, the cloud providers’ embrace of open source is leading to lower cost for additive cloud services as well as more robust competition. Open source also helps to attract the next generation of talent, who want to work on cutting-edge projects and have a positive impact on the world. Linux, an open source solution that modernized and replaced an outdated alternative, is a great success story. But not all projects achieve that level of success, and it is important to identify which ones are likely to remain vibrant and viable. One indicator is when the founders remain involved and the project is growing, as in the case of Confluent and Databricks with Apache Spark and Apache Kafka. Another positive sign is when open source projects are widely adopted across the major cloud providers, such as Docker and Apache Spark. Perhaps one misperception is that the acquisition risk is lower with open source technology. IBM bought Red Hat recently, and VMWare bought Heptio. As a result, companies that have decided to migrate toward an open source technology may find themselves bound to a large incumbent vendor once again. If this trend continues, the full benefits of tapping into open source may not be long-lasting. Moreover, some new license frameworks prohibit companies from reselling what they have built on open source. Given these trends, Nasdaq plans to contribute to a select group in the open source community. In particular, open source makes it easier for exchange customers to access data and derive insights from it in real time. If market participants handle data in a common way and with a common set of tools, individual firms do not have to devote resources to building those tools. Importantly, data can be shared in a way that does not compromise security and integrity to the benefit of all.
2019
NASDAQ DECODES: TECH TRENDS 2019 -The technology trends that are driving the world of markets forward
Nasdaq
Innovation in the cloud is prolific
Innovation in cloud product offerings has been prolific as cloud providers compete to gain market share. Two significant advances over the past year are the integration of time series databases and the introduction of parallel streaming in milliseconds, giving companies a comprehensive view of activity like never before. Specifically, Apache Spark is a fast, in-memory data processing engine with development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast, iterative access to datasets. Apache Kafka is a community distributed streaming platform capable of handling trillions of events per day. Both technologies are available in the cloud, and will be foundational for next generation surveillance, risk management and generally keeping up with the high-speed information on trading and clearing systems. It is notable that the cloud providers are embracing and supporting open source alternatives in addition to the enterprise software and proprietary solutions that are available currently. Importantly, customers are benefitting in terms of better availability and cost effectiveness of product. Some cloud providers have conceived of products that extend their offering to the customer’s premises. Other offerings allow large customers with many accounts the ability to give their employees autonomy while still maintaining control. Regulatory compliance is a key consideration for companies, and concerns about data residency are driving some global players toward a true multi-cloud offering. One implication of GDPR, for example, is that companies may not be willing to cross borders with their products and data if a cloud provider has not built out in Europe and in the company’s region. In many firms, the multi-cloud strategy is still taking shape, and the fear of traditional vendor lock-in is ever present. That said, open source foundational technologies as well as emerging ones such as Apache Kafka may be adopted across all major cloud providers. For now, firms appear to be adopting the leading cloud provider in their region plus a second one, but the right cloud strategy is a matter of perspective. For technology providers, having a multi-cloud strategy is important for product distribution and customer reach. Many financial firms, however, are still operating in a hybrid cloud mode, focusing on connecting to one cloud provider as well as their own data centers. Nasdaq will continue to monitor progress in this area.
2019
NASDAQ DECODES: TECH TRENDS 2019 -The technology trends that are driving the world of markets forward
Nasdaq
Alternative data, machine learning and artificial intelligence are a powerful combination
Enterprises need to become data driven to succeed in the current business environment. The ability to make both structured, unstructured and alternative datasets actionable can be a significant differentiator. In some cases it is necessary just to stay relevant. This is true across all industries, including finance. Traditionally, investor analysis involved looking at a company’s 10Ks and 10Qs, market data and the technical analysis of the trading activity. Nowadays, investors see an opportunity to use alternative datasets from sources such as Quandl to make better decisions. For example, they might look at month-over-month sales and compare those figures to the company’s peer group, and track the company’s supply chain for insight into future production flows, sales and sources of risk. Clearly, alternative datasets, analytics, and machine learning/ artificial intelligence (AI) are a powerful combination. The advancements in AI are coming rapidly. New techniques such as reinforcement learning as well as generative adversarial networks (GANs), which are a type of deep learning neural network, are starting to attract attention. They are also extending capabilities beyond what was possible with standard machine learning and deep learning algorithms. GANs for instance will allow AI to compete with itself to come up with an optimal model in real time, resulting in greater accuracy. A potential application is in risk management. All these technology enhancements have not brought us much closer to having a generalized AI (capable of super-humanlike intelligence across any subject). However, companies are achieving success by focusing on narrow AI applications where an algorithm can be trained to do one thing extremely well, surpassing the capabilities of what a human could do on their own. Financial firms are doing this to detect spoofing behavior or risky trading activity. For example, they use these narrow AI algorithms to build applications that are much more sophisticated and accurate than their traditional counterparts. Generalized AI – the ability for a machine to successfully perform any intellectual task that a human being can – is still about a decade away. Yet it is becoming easier to interact with Siri, Alexa and Google Assistant, and every question people ask is another narrow AI application. Before long, it will be possible to put together millions of questions and answers, drawing us farther down the path to generalized AI – especially as the technology improves.Until then, the greatest opportunity and challenge is knowing the right narrow AI applications to develop. Commercial success is dependent on having a clear understanding of how, when and why people will use something new rather than relying on their tried and tested human intelligence. Behavioral science methods are becoming recognized as the differentiator to deliver this understanding, and the way forward could be through “collaborative intelligence”, involving a reimagining of people and machines working together. Achieving this requires behavioral scientists to do a new depth of analysis of clients’ cognitive and manual working processes. This ensures the best of human and machine capabilities can be leveraged to deliver this new way of working. In the meantime, Nasdaq’s strategy is to build a community of data suppliers and connect them with a community of data consumers, and then provide the services they need to make the data actionable. As we build up our data repositories, and we connect them to Nasdaq Financial Framework, those datasets and technologies will become available to an array of market participants.
2019
NASDAQ DECODES: TECH TRENDS 2019 -The technology trends that are driving the world of markets forward
Nasdaq
Blockchain projects produce early results
Many financial institutions and blockchain/ distributed ledger technology labs have been working on proof of concept (PoC) and pilot projects, and some are starting to produce early results. A goal of this work is for parties to form consortia and set up commercial networks on shared infrastructure based on the blockchain. Multiple blockchain technologies are now emerging with different implementations and consensus algorithms. As certain projects may require interoperability between these different implementations, Nasdaq recently completed a PoC with the Singapore Exchange (SGX) and the Monetary Authority of Singapore (MAS) demonstrating cross-blockchain settlement. This indicates that blockchain can provide a supporting role in the next generation CSD and the transfer of digital asset ownership. The emergence of different types of tokens is another trend. Some link directly to a fiat currency, while others are tokenized assets. In response, regulators worldwide are trying to build a legal framework for payment, security and utility tokens. Going forward, blockchain will likely be used as a solution for managing new types of financial and non-financial assets in markets everywhere – potentially including real estate, insurance and loyalty points. The token ecosystem will support the entire life cycle of the asset – from issuance and price discovery to execution and settlement, and perhaps corporate actions. Payments will either be done on the same network, via a link to an external payment network such as T2S or Swift, or via a utility settlement coin. Yet, some important questions remain unanswered. Who will take on the custodial aspects of dematerialized physical assets and digitized tokens on a blockchain, and manage know your customer and anti-money laundering compliance? The traditional custodians could assume that role, but disrupters could appear in markets that do not have custodians. Another question is who will be the arbitrator if and when a smart contract goes wrong? The smart contract hype cycle has nearly peaked, and the trough is about to begin. Technologists need to think about where smart contracts make sense and whether the programming languages should be Turing complete so they can run any program given enough time and memory. In the next few years, expect to see the major cloud providers supply the infrastructure for blockchain, and blockchain software companies consolidate as funding becomes more difficult.
2019
NASDAQ DECODES: TECH TRENDS 2019 -The technology trends that are driving the world of markets forward
Nasdaq