In today’s world every company is becoming a technology company, with systems so complex that they cannot be administered manually. The solution is machine learning.
BUILT for digital business and the Internet of Things, intuitive networks promise a respite for organizations struggling with legacy networks and are a stepping stone toward a much bigger picture. There are an estimated 8.4 billion connected devices today and this number is racing ahead to reach hundreds of billions. According to latest forecasts, over 500 billion devices and objects will be connected by 2030. The present phase of digital transformation is more powerful and challenging than previous technological transformational phases. In the past, we have leapfrogged from mainframes to PCs and from the informational web to e-commerce driven Internet. At present, digital disruption is radically changing economies, cities, communities and the landscape of business.
The connected world has become too big and too complex for us humans to effectively administer by our own skills. The networks of tomorrow, with their dense array of devices, sensors, systems, appliances, applications, will not be manually administered. They will be so complex, so cumbersome and so fast changing that much more than manual administration will be required. Traditional networking models simply do not scale and perform to meet the expectations of this digital era.
With this spate of challenges, businesses in Saudi Arabia need a new networking framework that is simplified and more secure to use. Businesses that have invested in modern networks have improved their rate of growth in revenue, customer retention and profit by a factor of two to three. For digital organizations, the network is the foundation of their business and success.
In the connected world of today, every company is becoming a technology company. While connected devices present useful business opportunities, the complexity of managing the devices and the network itself, in an efficient and secure manner, is a challenge for present day technologies. This means every company needs to have security, on the top of its boardroom agenda. Organizations that make cybersecurity a critical foundation for their digital growth strategies will accelerate their innovation and reduce their time to market. Security is the most sensitive and critical component of digital transformation.
Increasingly industry analysts now agree that the way forward to tackle security challenges in digital organizations in Saudi Arabia is through the network. It is within the network that people, processes and data collide, and technology itself presents solutions to move forward.
From the field of analytics, machine learning can be used to build complex models and algorithms within networks that are capable of generating trends. Analytical models inbuilt inside networks can produce reliable and repeatable decisions and can uncover hidden insights through learning from historical relationships embedded in data. Machine learning can give networks the ability to learn without being programmed. This approach has evolved from pattern recognition and learning theory in artificial intelligence.
Networks with such inbuilt algorithms can learn and make predictions from data. With the help of these algorithms, networks can overcome the limitation of static programming and can make data-driven predictions and decisions, through building a model from data inputs. In the past data mining has been used to discover new trends in wide arrays of collected data. However, in the case of such intuitive networks built on machine learning, algorithms discover known trends that are prevalent in data as it is aggregated.
Networks with in-built machine learning and complex algorithms can establish a pattern of baseline behavior and can successfully flag deviations without supervision. One of the immediate benefits is the ability to successfully build models of optimal network behavior and proactively react without intervention to anomalies and intrusions within.
Such new intuitive networks shift from the traditional manual, time-intensive, static mode of operation, toward one that is capable of continuously learning from the data that it manages for an organization. The more volume of data it manages, the more it is capable of learning through analytics and adapting for automatic and efficient response. The intuitive network automates the edge of the network and embeds machine learning and analytics at a foundational level.
Three fundamentals characterize these innovative solutions. By enabling the ability to move from manual operations to automation, this network can scale to manage millions of devices. By assessing data continuously in context with the rest of the organization, the network can provide better insights leading to proactive security actions and efficiency. By virtue of the actions it takes and assessment of the results, the network can improve the results of its future insights and also its future action.
Where does all this lead? The first is, an intuitive network will gain the trust of business and IT executives who want to select a platform to build their digital business models of tomorrow. This will be on the basis that it is constantly learning and evolving to become highly secure and provide insights. And the second is, the intuitive network is just the stepping stone to a much bigger vision of creating intuitive technology infrastructure. But for now, the network remains the accelerator and enabler of this exciting end game.
Osama Al-Zoubi is chief technology officer of Cisco Middle East, part of the world’s largest networking company.
Q