Everything-as-Code, AIOps smart hyper-automation and shifts to edge data architectures can liberate organizations from inefficiencies and reliance on legacy resources.
In the past two years of pandemic challenges, organizations have had to rapidly adapt or lose relevance. The process to update and improve existing infrastructure has so far been both agile and reactive.
This year, CIOs are realizing that, 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.
This shift towards a “self-driving” enterprise has been fueled by three fundamental trends that need to be considered in planning for a successful transition journey.
Trends shaping the self-driving enterprise
First, the data gravity megatrend is accelerating a shift to a distributed data-centric architecture and moving data processing to edge computing.
- Shifts in data architecture
As digitally-enabled interactions become the norm—supported by new technologies such as 5G and IoT devices—not only do enterprises generate an increasingly growing amount of data, but the data are 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 organizations currently produce and process only around 10% of their data outside of such centralized facilities; but it 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. It extends 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 the Asia Pacific region, the adoption of edge computing is expected to increase massively in the coming years, driven in good part by the modernization of the manufacturing sector and the advanced digitalization of financial services in the region. - Shifts 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 turned into actionable insights.
Organizations willing to redesign their workflows and processes can apply advanced technologies including AI and ML to increasingly automate processes and augment humans. This applies not only to innovative 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 organizations to reduce human interaction, improve 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 with external regulations and internal policies has been achieved through manual and complex human-driven processes involving multiple functions across the enterprise. This usually consists of a mix of documented guidelines, checklists, operations playbooks, and also partial automation through configuration management and DevOps pipelines.
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 that are now increasingly targeted by cyberattacks can be secured with automated verification, packaging, and built-in attestation. Compliance rules can also 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 data-augmented self-driving enterprise
The three trends, powered by emerging technologies and open standards that enable intelligent hyper- automation via a managed approach, can liberate more enterprises to be self-driving as they extend their technology environment towards the edge through a data-centric architecture approach.
Through continuous compliance, their CIOs can deploy technology everywhere in a standardized manner. This enables and scales digital innovation by empowering business users with an end-to-end, real-time view of operations from internal systems.
When augmented by agile data and hyper-automation, self-driving organizations will make better operational decisions, improve their focus on strategic corporate decisions, and reach much greater operational efficiencies to deliver superior services to their customers.
