Our CTO Tim Cassidy does, occasionally, find the time to get out and about in between his largely client and R&D focussed engagements. Over the last 12 months he has sought to view the technology sector we are in through various prisms, and through industry events and meet-ups. Over and above the much touted rise and rise of AI / ML, he has found four common themes occupying thoughts and discussions at events ranging from Data in Motion last year to Big Data London just last month.
One all-pervading topic has been the degree to which so many individual tools and technology are absolutely necessary, but not in themselves sufficient at Enterprise level. This is often reflected in longer term programmes increasing, rather than reducing in complexity over time, as the challenges of enterprise-scale modernisation in a ‘brownfield’ application landscape and the layering across many tools become apparent.
One of the more impactful discussions within this area has been how to build data observability into an integrated enterprise data strategy and infrastructure upfront, as opposed to the more ubiquitous Modern Data Stack approach. Ultimately, data may well be the ‘new oil’ but that data needs to be trusted to be exploited to full potential.
Speed is another pervasive theme. There is much discussion around the need to use Kafka to implement streaming architecture using an event sourcing / CQRS approach, and how to do this at scale. A further pre-requisite to speed is data and AI-centric security (and privacy) as opposed to just security at a cloud / infrastructure layer level. As the move towards decentralised and multi-cloud approaches to data and AI gathers speed, a Zero-Trust approach across the Enterprise is increasingly essential; when done well and embedded from the outset actually enables innovation and speed of implementation.
The highest priorities across the enterprise are almost universally data-centric and data-driven, particularly as focus on AI business applications evolves from trying to save costs and make efficiencies, to driving additional revenue streams. Recent surveys have frequently cited data management as the top technical inhibitor to AI / ML deployments, and aging data infrastructure and legacy architectures in turn impact sustainability and performance. Mission Critical endeavours, requiring the highest levels of operational resilience, require a different approach, and modern and resilient platforms rather than reliance on a single data centre or cloud provider, particularly in an era of sensitivity around cost and classification.
Aker, born during the inexorable rise of Cloud and AI/ML, has from the outset focussed on building modern technologies that share the attributes of real-time, secure, mission-critical, and trusted. These technologies are enabling enterprise data modernisation and the exploitation of AI / ML application across a number of classified and critical data environments today.