Top Technology Trends that CTOs can blindly follow

1) ComposableArchiteture

Composable Applications allow polyglot microservices-based packaged-business capabilities (PBCs) or software-defined business objects. PBCs — for example representing a patient or digital twin — create reusable modules that the IT-Business fusion teams can self-assemble to rapidly create applications, reducing time to market. Champion composable architectural principles in all new technology initiatives, including application modernization, new engineering, and the selection of new vendor services. Buy standard PBCs on application marketplaces and integrate using APIs. According to Gartner, by 2024, the design mantra for new SaaS and custom applications will be “composable API-first or API-only,” rendering traditional SaaS and custom applications as “legacy.”

2) Data Fabric/ Data Platform

The value of data has never been more valuable. But often, data remains siloed within applications, so it’s not being used as effectively as possible. Data fabric integrates data across platforms and users, making data available everywhere it’s needed.

Within inbuilt analytics reading metadata, the data fabric is able to learn what data is being used. Its real value exists in its ability to make recommendations for more, different, and better data, reducing data management by up to 70%.

Identify priority areas to introduce data fabric solutions by using metadata analytics to determine current data utilization patterns for ongoing business operations. Prioritize areas with significant drift between actual and modeled data.

3) Cybersecurity Mesh

Using a cybersecurity mesh approach, you can integrate multiple data feeds from distinct security products to better identify and respond more quickly to incidents. Digital business assets are distributed across cloud and data centers. Traditional, fragmented security approaches focused on enterprise perimeters leave organizations open to breaches.

A cybersecurity mesh architecture provides a composable approach to security based on identity to create a scalable and interoperable service. The standard integrated structure secures all assets, regardless of location, to enable a security approach that extends across the foundation of IT services.

4) Privacy-Enhancing Computation

The real value of data exists not in simply having it, but in how it’s used for AI models, analytics, and insight. Privacy-enhancing computation (PEC) approaches allow data to be shared across ecosystems, creating value but preserving privacy. Approaches vary, but including encrypting, splitting, or preprocessing sensitive data to allow it to be handled without compromising confidentiality is the art of PEC. PEC platform uses homomorphic encryption so users can conduct data searches against its extremely sensitive data, with both the search and the results being encrypted

Investigate key use cases within the organization and the wider ecosystem where a need exists to use personal data in untrusted environments or for analytics and business intelligence purposes, both internally and externally. Prioritize investments in applicable PEC techniques to gain an early competitive advantage.

5) Cloud-Native Platforms

According to Gartner, By 2025, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021.

Lift-and-shift cloud migrations focus on taking legacy workloads and placing them in the cloud. Because these workloads weren't designed for the cloud, they require a lot of maintenance and don't take advantage of any of the benefits. 

Cloud-native platforms use the core elasticity and scalability of cloud computing to deliver faster time to value. They reduce dependencies on infrastructure, freeing up time to focus on application functionality instead.

Typical use cases are to build a cloud-native platform to create a portfolio of new digital services. For example, a bank can reduce the time to open an account to 5 minutes and add instant digital payments when using a well-architected technology platform. Deployment microservices architecture enables the integration of services such as savings, virtual debit card, and credit card services, allowing the system to easily scale to over 3.5 million transactions in two months.

6) AI/ML/Metaverse/ AR/ VR/Computer Vision

Distributed enterprise is a virtual-first, remote-first architectural approach to digitize consumer touchpoints and build out experiences to support products. While AI engineering is the discipline of operationalizing AI models, using integrated data and model and development pipelines to deliver consistent business value from AI, the use of NFT/blockchain-based metaverse builds on Web 3.0 principles to enable 'play to earn' gaming, AR/VR enabled retail e-commerce, real estate, hospitality, corporate training, induction on manufacturing shopfloors to aircraft engine simulations has seen a major boost in government and private investment.

Spectrum of SRE Implementation Models

 

A Spectrum of SRE Implementation models

A significant aspect of SRE implementation at Enterprise is around the model that will enable governance and growth at pace. In this article we will look at the Hub and Spoke model as an approach to solve the SRE scalability challenges towards applying a product management life approach that enables rapid, repeatable SRE practices that are cross-pollinated from the spokes that are usually across business domains.

 

 

The Hub & Spoke model not only decentralizes the implementation of solutions, but it also allows for rapid innovation / sharing of ideas across the organization, while centralizing research for latest best practices. It helps attract, develop, and retains scarce SRE talent, allowing for flexible allocation of resources to keep employees challenged with new perspectives.

 

A few considerations are as below, as SRE capabilities mature, the governance model will evolve with more talent sitting in the spokes, meaning more work completed by the business sectors & the hub acting as a champion available when needed.

 

 

The SRE HUB exists to enable self-service model for the spokes. Typically built using the in-source model that builds embedded governance that is fit for purpose, the cross-pollination from spokes is key to following the product-based approach by the HUB.

 

The Evolution of the Mainframe

 

Mainframe and its evolution with Cloud Computing

The key attributes associated with mainframe computing are high resilience, high manageability, and scalability. Despite the momentum driving public cloud adoption, there remain workloads that cannot easily be migrated to the public cloud. Whether it is deemed too risky to migrate or reworking legacy code is cost-prohibitive, mainframe computing remains an integral part of IT ecosystem. There is a growing demand for reworking some mainframe workloads to run cloud natively on cloud infrastructure. But the risks associated with this often mean the core back-end mainframe system remains untouched in many organizations. APIs are used to provide external connectivity in order to enable enterprise developers to build modern functionality, combining the best the public cloud can offer with reliable transaction processing embodied in the mainframe.

Over the last few years, Cloud computing has evolved to the point where it is now promising the same level of scalability, flexibility, and operational efficiency that mainframe systems have long provided. In fact, in terms of scalability, it exceeds mainframe scalability. With scalability, throughput, operational efficiency, and arguably even resilience and failover, the cloud has arguably caught up with the mainframe of the 1990s or early 2000s. It is fair to say that cloud providers have made great strides in security and privacy, but, the mainframe is still recognized as the gold standard, with security baked into every layer in the systems stack.

The mainframe ecosystem and the z/OS operating system have evolved too and IBM has introduced specialty processors to run Linux workloads and support encryption, greatly increasing the flexibility of mainframe systems. Cloud providers offer support for specialist workloads for non-x86 hardware, such as graphics processing units (GPUs) for machine learning and AI. But the introduction of the latest addition to the z-series mainframe family, the z16, offers what IBM claims is the gold standard for highly secured transaction processing.

The mainframe environment is getting bigger with announcements such as those made at the recent launch of the IBM z16. These include quantum-safe cryptography to protect against the development of Quantum computers able to decrypt current encryption standards, on-chip AI acceleration to boost ML and AI execution and flexible capacity combined with on-demand workload transfer across multiple locations to further reduce the chance of service disruption.

On workload optimization, the two environments are developing in different ways. For example, the mainframe strives to deliver a consistent environment that can handle a wide range of workloads but is managed through the same set of frameworks and tools. The cloud, on the other hand, allows you to spin up dedicated specialized environments, e.g. for AI or analytics. Also, IBM Cloud’s ambition to make "mainframe as a service" available from its IBM Cloud and available across data centers, brings the mainframe capabilities closer to cloud-native offerings.

The modern mainframe, particularly LinuxOne version and the new Z16, it's pretty clear any claims of the mainframe being out of date or legacy stem from a fundamental lack of awareness. Indeed, the mainframe has continued to lead the way in many critical areas, delivering IT cost-effectively, and is far away from becoming obsolete.