Consumers regularly engage with data-driven applications that provide personalized experiences and recommendations. Real-time data processing and intelligence are also becoming essential in the development of intelligent enterprise applications that deliver content and recommendations driven by predictive and generative AI to users responsible for customer experience, people analytics, finance analytics and marketing and sales.
The agility required to support these intelligent applications is provided by cloud computing architecture, the adoption of which has been the catalyst for digital modernization. Cloud architecture offers many advantages, including heightened agility, cost reduction, improved collaboration, enhanced security and accelerated innovation. New data platforms enable organizations to take advantage of cloud architecture to accelerate and transform business processes.
There is a wide spectrum in terms of adoption of cloud databases. Most organizations have at least migrated some databases to the cloud to take advantage of the benefits of avoiding upfront infrastructure costs and the agility of elastic scalability. Lifting and shifting existing workloads to cloud architecture is just a starting point, however. There are additional benefits to be had from the adoption of new cloud-native databases, both for the development of new applications and the modernization of existing applications.
While the data platforms market has been dominated for decades by relational databases, the need for agile development has encouraged many organizations to accelerate adoption of non-relational databases. In particular, the flexibility of the document model has driven developer enthusiasm and encouraged the adoption of document-oriented databases for the rapid development of new applications. NoSQL database vendors have also added support for ACID transactions and SQL compatibility in recent years, making their products increasingly suitable for applications that have traditionally been dependent on relational databases.
Many organizations have benefited from rewriting non-mission-critical applications to take advantage of cloud infrastructure, but the most advanced organizations have also embraced cloud-native databases for mission-critical applications. Doing so requires functionality to support reliability, security and performance, as well as a mature and strategic approach to cloud infrastructure and data platform selection. The strategic adoption of cloud-native databases requires organizations to understand and calculate the consumption and cost of cloud infrastructure and cloud services for data workloads. Cloud computing promises more efficient use of infrastructure resources, but lower costs are not guaranteed.
Most organizations continue to use on-premises infrastructure alongside cloud. As a result, enterprise IT architecture typically spans multiple cloud providers and on-premises data centers, as
The use of multiple cloud providers started inadvertently for many organizations, fueled by shadow IT as well as mergers and acquisitions. But for the most advanced, multicloud is increasingly a strategic choice that enables the use of products and services from multiple cloud providers as well as the potential to avoid cloud lock-in. Complexity and data egress charges mean that organizations may not want to move workloads between cloud providers in practice, but many want to be able to do so if necessary or desired. This is especially true in the financial services sector where multiple regulations now stipulate that banks have documented cloud exit strategies to mitigate the risk of cloud failure.
Business continuity is a primary driver for a distributed cloud architecture and has significant implications in relation to database selection. All data platforms can run in the cloud, but organizations are increasingly seeking data platforms that can automatically replicate data across multi-cloud and hybrid architecture and rebalance in the event of failure. Active-active replication is important in cloud bursting to support additional capacity requirements, as is the ability to automatically scale down resources when they are no longer required. Workload isolation is also important in terms of supporting ephemeral workloads that can be quickly provisioned and deprovisioned, alongside those with specific performance requirements that are better suited to long-lived resources.
Developers have increasing influence over the databases selected to support application development and modernization, and the variety of data platform choices has never been greater. Providing developers with complete autonomy in terms of data model, architecture and deployment location can create fragmentation, however. The most advanced organizations are balancing the benefits of choice with the need for control through the introduction of managed services that provide developers with a pre-selected choice of cloud-native databases that have been chosen to support multiple use-cases and architectural patterns.
Data-driven organizations stand to gain competitive advantage as they can respond faster to worker and customer demands for more innovative, data-rich applications. Selecting the right database is essential to delivering personalized and conversational experiences driven by predictive and generative AI that expands access to information and accelerates decision-making. All organizations must evaluate whether their data platforms have the flexibility and functionality required to support agile application development and modernization to deliver strategic business transformation.