Machine Learning, Edge, and Decentralization to Drive Your Investment Decisions
Challenges to the business operating environment are mounting – a survey of over 200 executives conducted in 2021 revealed the resilience dimensions most important to their strategy and operations. Digital and technological resilience is rising to the top, just behind financial resilience and operational resilience as being most important to the business across a variety of sectors. Unfortunately, it is not just a matter of threats and vulnerabilities to enterprise systems – although these are many and in constant flux. The competitive and customer demands for flexible digital services contribute to the shifting corporate attention to achieving digital and technological resilience, and the innovation it requires to maintain it.
There are many paths forward when taking action to stay ahead in this digital and technological innovation race. Some find it helpful to orient their action around concepts like the Composable Enterprise, Privacy-by-Design, Zero Trust, and so on. Whatever your organization chooses to orient its future strategy and direction with, it must be accompanied by action – in other words, the direction your organization chooses to innovate in will have an influence on its investment decisions.
This post is not a guide, but a place to find inspiration. The technologies and concepts you find here may not directly fit your organization’s targets, but it may trigger the right conversation with your team on how to stay ahead of the curve in your industry.
Machine Learning
Machine Learning should be thought of as a concept rather than by use case – there are simply too many methods and inputs that could be utilized – classification tasks, regression tasks, focusing on images, words, speech, video, and so on. Therefore, take inspiration from the challenges that organizations are facing – the multiplying volume of data being generated and collected by organizations, the massive amounts of sensitive data that are spread across multiple clouds, the many points of interaction between employees and systems, employees and customers, and customers and systems.
Cybersecurity will be an important area that is influenced by machine learning. Read this Leadership Brief for an analysis on the scope and challenges of machine learning in cybersecurity, or the CISO Panel on Securing the Composable Enterprise in May at the EIC.
Data management and governance is a foundational element of machine learning, but also serves many other purposes in the organization. Data governance supports privacy programs, secures sensitive information and intellectual property during collaboration, and of course is the motor behind preparing and maintaining datasets of corporate data for business intelligence and machine learning projects. AI Service Clouds can play a role in facilitating good data governance and management throughout the development lifecycle, next-generation BI platforms often contain elements of machine learning to deliver recommendations, and this session at the EIC considers how to secure the hyper-connected, data-driven world
Edge
The edge expands the horizon of opportunities. This can be device-driven, linked tightly with machine learning that delivers insights from streamed data from and at the edge, or emerge as a defining aspect of cybersecurity architectures.
IoT usage creates a need for, perhaps even a dependency on edge computing. For more ideas on how to manage this changing reality, check out the IoT and Hyperconnected Enterprise track at the EIC. Moving servers and services to the edge corresponds with the rise of hybrid cloud infrastructure, as described in the context of hybrid cloud backup and disaster relief.
The secure access service edge (SASE) stands for a concept that integrates a range of cloud-native security services including cloud access security brokers (CASB), firewall as a service (FWaaS), secure web gateways (SWG), and zero-trust network access (ZTNA), with wide-area network (WAN) capabilities for delivering both directly to any edge computing location. SASE is an enabling concept, that can invite more usage of edge devices and locations while maintaining a secure environment. This Advisory Note gives more context to implementing SASE in the organization.
Decentralized
Relax. Decentralized doesn’t always mean blockchain. The work that is being done on enterprise use of the blockchain is just the initial steps in building interoperability between distributed, independent services while still maintaining security and trust. It is a foundational concept in composable enterprises, the topic of the EIC’s opening keynote. Concepts like the Global Assured Identity Network work on a decentralized concept – supporting assured identity providers utilizing the blockchain, but more importantly supporting many different methods of providing an assured identity for cross-border trust.
This collection of ideas just barely scratches the surface of innovative technologies being used in the enterprise. Whether your organization’s investment strategy focuses on digitalization, boosting cybersecurity, streamlining processes, or getting involved in industry networks, consider what’s happening on the cutting edge. Join us at the EIC in Berlin this May for thought leadership on identity, cloud, and beyond.
