There is no doubt that the pandemic has altered the way we work, live and in turn the way we consume services. While in the past two years companies had to rapidly adapt or lose business, with adoption of remote work spreading or the rollout of extensive digital services tools to reach out to their customers, the process to update and improve existing infrastructure has been so far understandably both agile and reactive. I believe that we are now at a crossroad where CIOs are realising that in order to survive and thrive in the new era, they must rethink how their organizations can strategically evolve and leverage data, technology and processes to enable the delivery of customer value in a more self-sufficient, autonomous and scalable manner. While many capabilities are ultimately required to support this shift towards a "self-driving" enterprise, there are in my opinion 3 fundamental trends that need to be considered in planning for a successful transition journey.
The data gravity megatrend is accelerating a shift to a distributed data-centric architecture and moving data processing to the edge
As digitally-enabled interactions become the norm, supported by new technologies such as 5G and IoT devices, not only enterprises generate an increasingly growing amount of data, but this data gets mostly generated by latency-sensitive systems outside of data centers or the public cloud. In addition, recent advances in analytics and machine learning have allowed enterprises to embed workflow intelligence into their digital solutions, which also fuels further data production through data enrichment, aggregation and integration. Looking at this trend, Gartner estimates that currently organizations produce and process only around 10% of their data outside of such centralized facilities but predicts that this figure will rise to 75% by 2025[ ]. As a result, it becomes increasingly difficult and costly to move data, with data traffic flows inverting and increased data processing and storage happening at the edge. This data gravity trend requires a data-centric architecture supported by a modernized, hybrid IT infrastructure strategy extending the cloud towards connected data exchanges at the edge[ ] and closer to the point of presence, while leveraging a consistent operating model across to ease the rapid transition. In Asia-Pacific in particular, the adoption of edge computing is expected to increase massively in the coming years, at an accelerated growth of 31.1% annually[ ] for a total addressable market cap of $45.32 billion over 2021-2030, driven in good part by the modernization of the manufacturing sector and the advanced digitization of Financial Services in the region.
Fast data and AI/ML are supporting a shift towards smart hyper automation with AIOps
With business operations moving to the edge, more value can be extracted from raw streaming data in real time and turning it into actionable insights. Organizations willing to redesign their workflows and processes can apply advanced technologies including artificial intelligence (AI) and machine learning (ML) to increasingly automate processes and augment humans. This applies not only to innovatives processes for customer engagement and delivery, but also to major internal supporting functions such as IT Operations, Finance, Human Resources and Legal & Compliance. In the field of IT Operations in particular, a single AI-powered platform supporting convergence of automation across disciplines (ITOps, DevOps, DataOps, MLOps) can support sophisticated, integrated, self-learning automation covering tasks such as capacity management, storage and backups, security management, application configuration management, and code deployment. This in turns allows reducing human interaction and improving the level of service quality as well as process scalability towards managing increasingly complex and distributed IT environments.
Everything-as-code is helping to enable self driving continuous compliance
Traditionally compliance to external regulations and internal policies has been achieved through rather manual and complex, human-driven processes involving a mix of documented guidelines, checklists, operations playbooks and partial automation through configuration management and DevOps pipelines, and tend to involve multiple functions across the enterprise. With an approach of everything-as-code[ ] organizations seek to extend the application development approach to all aspects of technology operations by defining and codifying infrastructure, software delivery pipelines and application services management. For example, software supply chains, now increasingly targeted by cyber attacks, can be secured with automated verification, packaging and built-in attestation. Or compliance rules can be developed, specifying what good looks like, so that the state of relevant systems can be continuously monitored by self-correcting processes, allowing tremendous efficiency gains by the IT organization.
The upcoming years should be a turning point in the hybrid cloud ecosystem conversation as enterprises extend their technology environment towards the edge through a data-centric architecture approach. Emerging open source technologies and standards enabling intelligent hyper automation via a managed approach, through continuous compliance, can help CIOs deploy technology everywhere in a standardized manner, thereby enabling and scaling digital innovation by getting to the stage where business users have an end-to-end, real-time view of operations from internal systems. This will help successful, self-driving organizations to increasingly automate operational decisions and focus on strategic corporate decisions augmented by data, and reach much greater operational efficiencies to deliver superior services to their customers.