Alexander Shevchenko is CEO of guavus (a Thales company), a pioneer and leader in telecom AI-driven analytics.
From market insights to industry 4.0 manufacturing to unraveling the scientific mysteries of the universe, big data underpins much of the world’s activities. As more aspects of everyday life become digital, data analytics applications that leverage artificial intelligence (AI) and machine learning (ML) to keep society functioning safely and on time are becoming increasingly important.
Big data, AI and ML automatically find patterns and use them to generate insights and possible actions. For example, a metro transportation system can predict traffic anomalies that could affect timetables and automatically reroute vehicles. The municipality could also use the data to find an optimal balance between deploying enough buses or trains so that traffic runs smoothly and not using so much that the safety risks increase unnecessarily.
While it’s become second nature to do something like pull out our cell phones to scan a check and deposit it into a bank account, behind the scenes banks and GPS are constantly correlating millions of variables to safely complete each transaction and optimize it. calculate routes. Likewise, we are shielded from the complexity of sophisticated algorithms that manage the national power grid, traffic lights and air traffic control systems.
As a leader in AI-driven analytics, below is how I see access to big data to power the trains — figuratively and literally — and how you can best take advantage of similar synergies to power your business.
Getting good data – and a lot of it
Finding data-driven solutions to major problems such as transportation management and bank fraud requires massive amounts of accurate, up-to-date data. Many large companies collect and generate big data themselves. But by partnering with other data-intensive global organizations, such as mobile network operators, important information can be added to the mix for greater success.
For example, consider the potential of combining your business data with mobile data stored in 5G networks. 5G is designed to enable dynamic applications that rely on real-time information and insights based on the data they generate. It can be merged with data from other sources and then stored, analyzed and correlated for new applications.
Much of the 5G data relates to the more than six billion smartphone users worldwide and mobile carriers’ own network operations. But ever larger portions are being made on the ‘edge’ in highly distributed Internet of Things (IoT) and other machine-to-machine (M2M) devices and sensors. An article by Tanweer Alam on ResearchGate estimates that there are more than 35 billion IoT devices today and expect them to multiply to 75 billion by 2025.
Managing the Airways
Traffic control is a good example of how this data can be used. Early this year, there were panicked reports of airline safety risks resulting from: possible interference between new 5G networks and air traffic control’s radio altimeter; among other solutions, however, there are promises in AI/ML programs to anticipate interference and automatically roll back the strength of 5G signals temporarily to avoid a collision.
Similar capabilities prevent collisions between drones. In 2021, more than 873,000 unmanned aerial vehicles or drones were registered to fly in the US, according to NASA† NASA, along with the Federal Aviation Administration and more than 100 partners, has created a research platform to ensure package deliveries and entertainment equipment do not interfere with helicopters, airplanes and other drones.
Mobile network data can improve the accuracy and performance of such systems. For example, it can automate quality of service management mechanisms to prevent a particular drone from straying outside its approved coverage area.
Monetization and social improvements
We increasingly need protection from the downside of digital innovation, which in turn requires access to more data. Think of digital banking and the tendency to fraud. Financial institutions implement highly sophisticated systems that analyze and correlate petabytes of data every day to prevent unauthorized access. Data analyzed includes customer biometrics and behavior, including the type of computing equipment the account holder typically uses, operating system, and mobile device behavior.
In the case of metro rail systems, data is collected not only from the turnstiles and entrances of train stations, but also from the ubiquitous networks of the 5G operators, which can help municipalities accurately anticipate the demand for optimal vehicle deployment.
Steps to use 5G data
1. Develop a data-driven business environment and culture, company-wide. This can happen on its own over time without top management guidance, but it can be slow and difficult to control. In a more intensive method, make sure it is a project that connects the company vertically and horizontally.
2. Use DevOps methodologies to help small, agile teams quickly develop new services and applications.
3. Inventory current data stores, data management processes and access rights, and ensure all key company stakeholders are aligned with the initiative.
Challenges for implementation
1. Data growth and outdated data infrastructure can be major hurdles. IDC predicts the amount of digital data created will almost double in five years. Organizations need flexible, distributed data sets that leverage hyper-converged infrastructure, software-defined capabilities, cloud models, etc.
2. Historically, access to corporate data has been siled and largely walled along departmental lines. Analyzes that rely on incomplete data may not yield the expected results. Deploying a data lake, which can store data in any format, is a primary solution.
3. Building and maintaining your own big data team is very expensive, time consuming and can take your focus away from your core business. Find the right partners and, in the case of 5G data, work with your mobile operator and ask them what services and analytics they can provide.
Working together to take advantage of data synergies and 5G
There is a synergistic effect of collaboration allowing the architects of modern analytics applications to access larger pools of data for better results. I encourage you to consider merging your data with the data of a 5G mobile operator or other type of partner with large data storage. The more data you have that is current and accurate, the smarter your AI/ML algorithms will be and the greater the opportunities for innovation.