Recent advances in analytics and machine learning have enabled enterprises to embed workflow intelligence into their digital solutions
By Vincent Caldeira
There is no doubt that the pandemic has altered the way we work, live and in turn, the way we consume services. We are now at the crossroads where CIOs are realising that in order to survive and thrive in the new era, they must rethink how their organisations 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 three fundamental trends that should be considered in planning for a successful transition journey.
One, 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 are enterprises generating an increasingly growing amount of data, but this data gets mostly generated by latency-sensitive systems outside of datacentres or the public cloud. In addition, recent advances in analytics and machine learning have enabled enterprises to embed workflow intelligence into their digital solutions, which also fuels further data production through data enrichment, aggregation and integration.
According to Gartner, by 2025, more than 50% of enterprise-managed data will be created and processed outside the data centre or cloud. As a result, it becomes increasingly difficult 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 modernised, 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 to ease the rapid transition.
Two, 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. Organisations 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, legal and compliance.
Three, everything-as-code is helping to enable self driving continuous compliance. With an approach of everything-as-code, organisations 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 instance, software supply chains, increasingly targeted by cyber attacks, can be made more secure with automated verification, packaging and built-in attestation.
The coming 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 standardised manner.
The writer is chief technologist (FSI), Red Hat
TheSpuzz .. Click here to join our channel and stay updated with the latest Biz news and updates.