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Tapping into the vast power of emerging technologies, like AI, requires flexibility and scalability to support rapidly evolving data needs. With that, most organizations have come to recognize the importance of adopting a hybrid approach, opting to leverage multiple environments spanning everything from cloud to on-premises. A hybrid cloud approach to data management is essential for improving data quality and availability, both of which pose immense challenges to enterprises today.  

But an impactful hybrid approach must go beyond simply moving data between environments. It demands reliable and trusted data. Without trusted data, organizations risk inaccurate insights, flawed decision-making, and biased AI/ML outputs.  

Enter: the importance of “true” hybrid cloud. 

What elevates a hybrid cloud strategy to the level of “true” hybrid? Significantly, “true” hybrid is the ability to operate as a single platform across both data center and cloud, as well as at the edge, accounting for the entirety of the data lifecycle, whether that’s the point of ingestion, warehousing, or machine learning. This approach helps unlock data insight from across the enterprise, maximizing the potential value of data analytics, AI, and LLMs. 

To achieve true hybrid, enterprises can follow a clear roadmap to take their IT infrastructure to the next level. Here’s where to start. 

Enable seamless integration across environments 

is a mixed infrastructure environment that can act as a single cloud environment. It seamlessly integrates on-premises, cloud, and edge environments into a unified infrastructure, allowing data, workloads, and applications to move freely. From a user perspective, there is no noticeable distinction between where data resides. This flexibility enhances adaptability, supports all data types, and ensures consistent functionality across platforms. This is a critical factor for organizations aiming to scale AI and analytics efficiently. 

So, how can enterprises determine if their approach meets the criteria of true hybrid? It’s key to remember that while supporting multiple clouds offers major benefits, organizations with a considerable data center investment or who keep datasets on-premises require a more complete solution.  

A true hybrid model enables organizations to keep data and workloads moving multi-directionally and friction-free while having identical and portable functionality available across infrastructures. Importantly, to be “true” hybrid, it also must be able to move this data, whether structured, unstructured, or semi-structured, with consistent governance and security. This allows organizations to orchestrate data and workloads across the entire IT landscape.  

Establish a foundation of trusted data 

Organizations that accelerate their AI initiatives with trusted data are better positioned to deliver reliable insights, maintain regulatory compliance, and scale innovation across the enterprise. But establishing trusted data at a foundational level means overcoming multiple obstacles. Issues around transparency, control, access, and a number of other factors all require robust privacy, security, and governance frameworks to adequately address them. 

For an enterprise looking to establish that baseline of trustworthiness, there are a few critical points to examine: what data is available, who may (or may not) access it, and what environment it will be used in. Gaining a granular understanding of an organization’s data flow — and managing access to sensitive information — can protect against regulatory vulnerabilities, government penalties, and reputational damage.  

Solving for those three points of availability, accessibility, and environment requires the right solutions. For example, working with a trusted partner like Cloudera and leveraging the enables organizations to break through data silos, increase visibility, and ensure data quality by embedding consistent security, governance, lineage, and metadata management across environments.  

Implementing the ideal data infrastructure 

As enterprises navigate addressing the data requirements that enable true hybrid, three key approaches deliver key functionality in terms of flexibility and scalability of data management, and ensure that the right, trusted data can be used to feed AI and analytics effectively. These include: 

• Unified data fabric: Data fabric helps make disparate data sources available in a safe, compliant, and self-service manner across the organization. This helps enterprises bring together disparate data into one single source — a critical part of what differentiates hybrid cloud and true hybrid cloud.  

• Scalable data mesh: A data mesh is designed to be decentralized and self-service, giving the ability to implement data as a product, providing simple authorized access, and giving each domain autonomy, agility, and flexibility to craft their data as a product of their organization. 

• An open data lakehouse: A data lakehouse offers a single location for storing and accessing data across all analytics engines. It offers significant benefits, including reducing the need for data duplication and synchronization, which lowers costs.  

With these in place, organizations have the foundation to begin capitalizing on emerging technologies, knowing their data will deliver the best possible outcomes. 

Tapping intro true hybrid 

By now, most organizations understand the value that comes with adopting a hybrid cloud approach. But the enterprises that are able to stand out and gain a competitive edge are the ones that go a step further. Those organizations embrace true hybrid. Enterprises that lay a foundation of trusted data, stronger data management standards for security and compliance, and a next-gen tech stack will not only win the moment — they will future-proof their organizations at scale.  

Learn more about how can help you achieve true hybrid.