As the global data storage market is set to more than triple by 2032, businesses face increasing challenges in managing their growing data. This shift to hybrid cloud solutions is transforming data management, enhancing flexibility and boosting performance across organizations.
By focusing on five key aspects of cloud adoption for optimizing data management—from evolving data strategies to ensuring compliance—businesses can create adaptable, high performing data ecosystems that are primed for AI innovation and future growth.
1. The evolution of data management strategies
Data management is undergoing a significant transformation, especially with the arrival of generative AI. Organizations are increasingly adopting hybrid cloud solutions that blend the strengths of private and public clouds, particularly beneficial in data-intensive sectors and companies embarking on AI strategy to fuel growth.
A McKinsey & Company study reveals that companies aim to have 60% of their systems in the cloud by 2025, underscoring the importance of flexible cloud strategies. Hybrid cloud solutions address this trend by offering open architectures, combining high performance with scalability. For technical professionals, this shift means to work with systems that can adapt to changing needs without compromising on performance or security.
2. Seamless deployment and workload portability
One of the key advantages of hybrid cloud solutions is the ability to deploy across any cloud or on-premises environment in minutes. This flexibility is further enhanced by workload portability through advanced technologies like Red Hat® OpenShift®.
This capability allows organizations to align their infrastructure with both multicloud and hybrid cloud data strategies, ensuring that workloads can be moved or scaled as needed without being locked into a single environment. This adaptability is crucial for enterprises dealing with varying compliance requirements and evolving business needs.
3. Enhancing AI and analytics with unified data access
Hybrid cloud architectures are proving instrumental in advancing AI and analytics capabilities. A 2023 Gartner survey reveals that “two out of three enterprises use hybrid cloud to power their AI initiatives”, underscoring its critical role in modern data strategies. By using open formats, these solutions provide unified data access, allowing seamless sharing of data across an organization without the need for extensive migration or restructuring.
Furthermore, advanced solutions like IBM watsonx.data™ integrate vector database like Milvus, an open-source solution that enables efficient storage and retrieval of high-dimensional vectors. This integration is crucial for AI and machine learning tasks, particularly in fields like natural learning processing and computer vision. By providing access to a wider pool of trusted data, it enhances the relevance and precision of AI models, accelerating innovation in these areas.
For data scientists and engineers, these features translate to more efficient data preparation for AI models and applications, leading to improved accuracy and relevance in AI-driven insights and predictions.
4. Optimizing performance with fit-for-purpose query engines
In the realm of data management, the diverse nature of data workloads demands a flexible approach to query processing. With watsonx.data, multiple fit-for-purpose open query engines are offered such as Presto, Presto C++ and Spark, along with integration capabilities for data warehouse engines like Db2® and Netezza®. This flexibility allows data teams to choose the optimal tool for each task, enhancing both performance and cost-effectiveness.
For instance, Presto C++ can be used for high-performance, low-latency queries on large datasets, while Spark excels at complex, distributed data processing tasks. The integration with established data warehouse engines ensures compatibility with existing systems and workflows.
This flexibility is especially valuable when dealing with diverse data types and volumes in modern businesses. By allowing organizations to optimize their data workloads, watsonx.data addresses the challenges of rapidly propagating data across various environments.
5. Compliance and data governance in a hybrid world
With increasingly strict data regulations, hybrid cloud architectures offer significant advantages in maintaining compliance and robust data governance. A report by FINRA (Financial Industry Regulatory Authority) demonstrates that hybrid cloud solutions can help firms manage cybersecurity, data governance and business continuity more effectively than by using multiple separate cloud services.
Unlike pure multicloud setups, which can complicate compliance efforts across different providers, hybrid cloud allows organizations to keep sensitive data on premises or in private clouds while using public cloud resources for less sensitive workloads. IBM watsonx.data enhances this approach with built-in data governance features, such as having a single point of entry and robust access control. This approach supports varied deployment needs and restrictions, making it easier to implement consistent governance policies and meet industry-specific regulatory requirements compromise on security.
Embracing hybrid cloud for future-ready data management
The adoption of hybrid cloud solutions marks a significant shift in enterprise data management. By offering a balance of flexibility, performance and control, solutions like IBM watsonx.data are enabling businesses to build more resilient, efficient and innovative data ecosystems.
As data management continues to evolve, using hybrid cloud strategies will be crucial in shaping the future of enterprise data and analytics. With watsonx.data, organizations can confidently navigate this change, using advanced features to unlock the full potential of their data across hybrid environments and be future ready to embrace AI.
Start your data optimization journey with IBM watsonx.data today
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