Nasdaq Decodes: Tech Trends 2020
Discover the technology trends that are driving the global markets forward
Companies across all industries are going digital.
For some, digital transformation relates to marketing and communications. For others, it is about reimagining ways to bring together people, data and processes to help engage clients, empower employees, optimize operations and fundamentally transform products and services.
But no matter the end goal, at the core of this innovation lies data creation and the ability to process data at scale and in real time to create more connected, intelligent processes, products and platforms.
Our fourth annual tech report identifies three key technology trends that are bringing these concepts to life and reshaping markets everywhere:
Machine-to-Machine Communication (M2M): The creation of data through machine-to-machine communication and the Internet of Things (IoT) without human intervention, allowing businesses to scale by ingesting and computing large volumes of data in real time
Business in Real Time: The ability to source and process valuable data consistently and efficiently is allowing businesses to go real time, leading to an overall better customer experience. Streaming technology that broke ground in the world of social media and entertainment is now being utilized in the capital markets.
The Platform Economy: Platforms and APIs are enabling M2M-generated data to be integrated in unique ways, establishing a foundation for innovation and the ability to deliver a differentiated and distinct customer experience.

M2M communication produces data in 'software-defined' ways, where the software has full control.
For this, no human intervention is needed, there are no hardware-specific dependencies and functionality can be added or modified easily. It gives businesses the ability to ingest and compute large volumes of data in real time, so they can perform functions and scale quickly. In addition, they can be more predictive and bring automated decision-making closer to the edge.
Technology that Accelerates
Edge Computing
Edge computing reduces latency by processing data closer to the source of capture such as laptops, tablets and smartphones.
Artificial Intelligence
AI chips are a new generation of microprocessors that are designed to process AI tasks faster, using less power
5G Technologies
5G is the latest iteration of cellular technology, engineered to greatly increase the speed and responsiveness of wireless network.
Driving Innovation in Autonomous Vehicles
One example of how these powerful technologies are accelerating innovation can be seen in the autonomous vehicle space by streamlining communications and enabling vehicles to navigate in real-time for example.
Autonomous vehicle models use telemetry data for driver assist, but it is still necessary to have human drivers in vehicles. But ML is being run on years’ worth of telemetry data that has been collected, and that data will get the automobile industry to the next phase, where vehicles are truly driverless.
The way they make decisions will change, and the sensors that they use to make those decisions will change, too.
The ability to source and process valuable data is allowing businesses to go real time.
Edge computing and IoT are generating huge amounts of data, and exchanges and financial firms need the capacity to store it and transport it from point of source to point of interest.
Technology such as MQ Telemetry Transport (MQTT) can provide an M2M/IoT connectivity protocol. It has been used in sensors communicating to a broker via satellite link, over occasional dial-up connections with healthcare providers, and in a range of home automation and small device scenarios. It is also ideal for mobile applications because of its small size, low power usage, minimized data packets and efficient distribution of information to one or many receivers.
However, MQTT has limitations that are being addressed by other technologies such as Apache Kafka.
Robotic Process Automation
Robotic process automation (RPA), also known as chatbots, is enabling business in real time as well. RPA systems develop an action list by watching the user perform a task in the application’s graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI. RPA helps financial firms achieve better results from the datasets collected in real time.
In financial services, many tasks are performed by humans, but in most cases they do not require special handling or complex problem solving. There is an opportunity to automate those processes, therefore allowing people to focus on exceptions. RPA can be applied to market operations functions that need to be done at scale and automated, including surveillance and compliance case management.
Automation results in a better outcome for the markets as a whole, and allows exchanges and financial firms to use resources more efficiently. It also helps them to achieve a consistent, predictable, reliable, scaled operational model.
RPA has gained wider acceptance in the financial markets over the past year. Some exchanges and financial firms are leveraging tools such as ServiceNow to deliver workflows, and then fitting AI-based RPA from vendors such as Kryon on top. There are use cases in the customer facing space, especially as exchanges and financial firms scale out and implement software-as-a-service (SaaS) models.
The improved efficiency and consistency RPA brings leads to a better customer experience. It could transform the way exchanges and financial firms work, and bring customers closer to the core of their value proposition. The technology enables several domains to be handled without requiring layers of organization between the customer and the product teams. In addition, it helps to tackle some of the fixed overhead and reduce costs while maintaining full compliance and efficiency. On the other hand, exchanges and financial firms need to demonstrate that RPA supports and is consistent with the regulatory environment, and they must be able to explain to regulators why a process, decision or steps were taken.

Platforms and APIs are enabling M2M-generated data to be integrated.
Platformification is having an impact on financial services and other industries. Products are being bundled together and monitored to see how clients interact with them, and that information is used to deliver a differentiated client experience. Platforms are also enabling markets and marketplaces to become fully digitized and connect all the parts of their business.
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