The rapidly evolving digital economy has created an unprecedented demand for analytical computing and processing from literally every corner of a potential customer market. As customers continue to expand their digital lives, the amount of data generated is also growing, making it nearly impossible for enterprises to handle it with traditional centralized cloud capabilities. This is the reason why edge computing is fast becoming a major player in the enterprise digital space.
By 2030, the global market size for edge computing is estimated to grow to nearly $139.58 billion. By bringing the cloud (computational processing and storage) to where data is, it becomes easier for businesses to offer their customers faster experiences with increased security and lower operational costs.
The Role of Data Science
Technologies like the Internet of Things (IoT) can truly leverage the power of edge computing to deliver amazing experiences to consumers. But for this to happen, the amount of high-end computer processing that needs to take place at the edge is significant. This is where the magic of data science comes into play.
Collecting, cleaning, and organizing data to fit into established computational models helps enterprises drive valuable insights from the data present in their digital landscape. With edge computing, the benefits are no less different when data science is used to unleash a new wave of power to devices and applications located at the edge.
Let us explore four ways in which data science can re-define edge computing today:
Real-Time Analytics on the Edge
Data science helps enterprises build highly scalable analytical models that can process large volumes of data in parallel, irrespective of the number of sources. By taking this to the edge, it becomes easier to have a real-time analytics capability that instantly processes data created by devices at the edge. The low latency of edge computing helps in this case as it allows data to be instantly leveraged by analytical software to derive real-time insights.
Autonomous Decision-Making
Taking a cue from the previous feature of real-time analytics, data science can bring about a whole new dimension of self-managed edge capabilities. With analytical capability available at the edge, edge networks can be programmed to be self-reliant and can make autonomous decisions based on insights obtained.
For example, smart routing systems for power or gas distribution networks can re-direct supply in the event of faults or repairs based on localized decision-making. They do not have to wait for control instructions from centralized stations.
Improved Reliability with Automated Failure Rectification
Edge computing works efficiently when there is a seamless transfer of data between nodes at all edges. However, it often suffers from point failures wherein a faulty node in the network prevents control signals or insights from reaching nodes further in the network. Many a time, the situation gets solved only when the faulty node or device is rectified manually.
With the introduction of data science, point failures will be easy to avoid in the case of edge networks. Intelligent analytics can quickly determine the best alternative route to skip the faulty node and continue the transactional or operational processes being carried out in the network. Taking a cue from the autonomous decision-making abilities outlined earlier, edge networks can turn into highly reliable and fail-proof systems thanks to self-healing abilities.
Often, the issues that plagued a device or node could be due to some minor bugs or technical faults. Equipping nodes with data-driven intelligence makes it easier for them to identify the root cause of the failure and rectify it on their own when all it needs is a software-driven change or reconfiguration. Such self-resilience enables edge networks to truly benefit in areas like security firewalls, connected smart vehicle communication systems, etc.
Improve Security at the Edge
With user traffic growing at the edge, it is natural that cybercriminals will begin to target vulnerable points in large edge networks to cause damage. With data science, however, it becomes easier to equip such edge networks with integrated intelligence to identify suspicious behavior of connected entities.
Regular scanning of the network’s touch points through AI-enabled systems can easily point out any vulnerability, which can then be rectified to prevent any damage.
The Future of Intelligence Is at the Edge
As edge computing becomes a mainstream technology worldwide, the race for supremacy among businesses in the edge domain will largely be on the basis of who manages to build the most intelligent network. As explained, data science holds the key to unlocking this high level of intelligence and analytical decision-making in edge networks. This will allow it to be a truly global medium to explore the power of IoT devices.
Additionally, the adoption of 5G technology will ensure that deeper digital convergence will happen in areas like industrial automation, robotics, etc. Data science will be the key pillar of establishing a trusted and sustainable growth foundation for the edge networks of tomorrow.
However, empowering your forays into the world of edge computing with intelligence is not an easy task. It requires a profound understanding of which data management models to choose, how to build associated data pipelines, and how to establish governance policies. This is where an experienced partner like Rubiscape can be your biggest asset. Get in touch with us to know more.