Look before you leap: Mitigating risk in cloud data deployments A guide to using AI in reducing the risk in migrating to the cloud Nowadays, cloud environments have become the de facto choice for data-driven applications. The maturity of cloud platforms and services, in addition to the increasing automation and resilience of cloud infrastructure, has lead to organisations transforming their cloud-first strategies into cloud-only ones.These advantages are already being recognised by mainstream, multi-billion dollar enterprises such as Capital One and Netflix who have both nearly entirely moved away from their own physical data centres. This approach is gradually being adopted on a wider-scale as more and more enterprises transition their data workloads to the cloud. This shift in attitude has resulted in enterprises treating cloud migration as a long-term investment rather than an update in IT. According to global market intelligence provider, IDC, total spending on cloud IT infrastructure in 2018 amounted to $65 billion with year-over-year growth of 37 per cent. In addition to this, IDC reported that quarterly spending on public cloud IT infrastructure had more than doubled in the past two years, reaching $12 billion in the third quarter of 2018, and growing 56 per cent year-over-year. It seems that, undeniably, there is a massive trend towards moving data deployments to the cloud. However, with the number of variables surrounding cloud migration, such as costs, visibility and dependency, streamlining this process can present a challenge to enterprises. As such, IT teams are looking to minimise the risks associated with migration such as; disruption to availability, lost data, reduced visibility and control. By investigating these areas before migrating, IT teams clear their path to the cloud as they can evaluate whether running data services in the cloud makes financial and operational sense for their business use case. What some companies are discovering is that, when planning a migration, intelligence and visibility are integral to maximising the benefits of the enterprise’s cloud investment as they have a great impact on reducing the friction of migration and minimising resource usage costs. Bridging the gap with predictive analytics It’s important to ‘look before you leap’ into the cloud. It helps to make the right choices for a migration with AI-driven insights. There are technologies, born with the cloud era, designed to provide those data-driven intelligence and recommendations so necessary for optimising compute, memory, and storage resources. These are the tools DevOps and DataOps teams should be selling into the wide business in order to make the transition a smooth and cost-effective one. Such tools aid the IT team in identifying which applications are the best candidates for migration and can provide detailed dependency maps to help all stakeholders understand the resource requirements before the migration kicks-off. It’s a real force-multiplier when the IT team can, for example, see the seasonality and ideal time of day to take advantage of the best prices for cloud services, spot instances, autoscaling, and a number of other tactics that enable them to make the most of their resources, be it time, money, or skills. On the cost side, the team should be looking to enable automatic application speedup, optimised resource usage, and intelligent data tiering as part of the migration tool chest at hand. – Read more
A guide to using AI in reducing the risk in migrating to the cloud