The next phase of cloud: How AI, analytics, and the edge are redefining data management

The data landscape is undergoing a significant transformation. Advances in analytics, artificial intelligence (AI), and edge computing are not just reshaping how we think about data, but are also heralding a new era in how organisations use the cloud.

Cloud is changing. It has always been seen as a flexible, agile way to outsource data storage. But now it’s becoming the basis for entire business strategies. Cloud has the power to transform processes across a huge range of industries, but it’s also presenting new challenges around how organisations manage data. The only way to get full value from innovations such as AI and edge analytics is to underpin them with a modern data architecture.

Hybrid cloud strategy in the AI era

As organisations look to move AI from the lab to production, having the right hybrid cloud strategy in place is becoming critical. Decisions over whether data sets are best suited for on-premise or cloud environments are no longer IT decisions, but business decisions.

Due to its dynamic nature and ‘pay-as-you-go’ pricing, organisations can use cloud to offset the cost of optimising AI for real-life use cases. Once testing is over, the cloud-based model can simply be switched off and applied to an organisation’s data in production. By contrast, the up-front cost of running the same process on-premise is vast. Just a single cutting-edge GPU – which is designed specifically for running large language models (LLMs) – costs around $30,000. Organisations may need thousands of these to train and run a model, which quickly racks up a bill in the tens of millions.

However, with the impending introduction of AI-specific legislation and increasing data regulations, sectors such as banking and telecommunications, will feel obliged to run their AI models on-premise through necessity.

Workload analytics and gaining a deeper understanding of data can help to address these challenges. But with the right hybrid cloud strategy in place, running AI can be both compliant and cost effective.

Real-time decisions made at the edge

Beyond AI, cloud will play a vital role in supporting edge analytics. The proliferation of IoT devices across business sectors combined with analytics at the edge is revolutionising how businesses drive value from data. Edge analytics has already showcased its potential in critical applications, such as enabling the automatic dispatch of ambulances for patients with smart pacemakers or enhancing the safety of autonomous vehicles.

However, we are just scratching the surface of what edge analytics can achieve. As data volumes grow exponentially, the ability to process and analyse it close to the source is invaluable. This shift not only reduces latency but also paves the way for more immediate and impactful decision-making.

But the amount of data being reported back from IoT devices varies, making on-premise provisioning a challenge. Organisations would also need to build data centres close to the edge, across multiple locations across the network. This would add to both up-front and long-term costs. So, public cloud is a better option for edge analytics.

Driving real-time value from data at the edge can only be achieved if organisations can harness the power of their data. Collecting this data is one thing, but deriving actionable insights and rapidly delivering results is another.

Modern data architecture is crucial

The success of AI and edge analytics hinges on how effectively organisations can integrate them into their data ecosystems. So, organisations must adapt their cloud strategies to accommodate these business-critical advancements.

Laying the foundations of a modern data architecture must be the first step here. Whilst cloud will be the bedrock of utilising AI and edge analytics, some data will still need to remain on-premise. Organisations will also need to consider the scale of data under management. To show real value, AI and edge analytics both require and produce huge quantities of data. Naturally, the more data you have, the harder it becomes to manage.

By unifying their data via a holistic data platform, organisations will lay the foundations required to usher in this new era of cloud usage, enabling them to drive value from all of their data, regardless of whether it sits on-premise or in the cloud.

Heading into a new cloud era

As cloud continues to evolve in tandem with technologies such as AI and edge analytics, it is opening up new use cases for data. These innovations have the power to reshape the business landscape, enabling split-second actions or enhanced decision-making. But for data to continue to exert innate change in organisations, the way it is managed must also advance.

Those organisations that have built their cloud strategy around a modern data architecture will be well-placed to take advantage of the evolving tech landscape, paving the way for innovative data use cases.