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Cloud & Automation: Changing CSPs’ OpEx outlook
In today’s world, being data-driven is crucial for organizations to stay competitive. However, many still struggle to embrace and embed data into their cultural DNA, preventing its use at scale. To achieve this, organizations must follow a new management approach that empowers everyone and not just data experts to work with data – a process called data democratization.
Data democratization improves agility and facilitates data-driven decision-making across all levels of the organization, but its progress is blocked by lack of data literacy, governance, and infrastructure. To address these issues amid ever-growing data complexity, organizations need a real-time distributed approach, such as data fabric.
Data fabric simplifies the data integration infrastructure and reduces technical debt. It introduces diverse data integration methods by using active metadata, semantics, knowledge graphs, and machine learning to develop flexible, reusable, and augmented data integration pipelines. Data fabric also supports various operational and analytics use cases across multiple deployment and orchestration platforms.
Gartner’s research on Data and Analytics Trends indicates that data fabric can reduce time for integration design by 30%, deployment by 30%, and maintenance by 70%.
Modern data flows – made possible by data fabrics – can meet the demands of a data-intensive and fast-paced business environment. However, making the transition to modern data flows needs strategic planning and execution.
Metadata is the core of the data fabric connecting several tools, systems, and processes. It provides the context, lineage, and structure to data, which helps seamlessly integrate disparate sources and allows the data fabric to function as a dynamic and unified system.
In a modern data architecture, metadata becomes a key ingredient for a well-organized data catalog. It could comprise a metadata repository, a technical glossary of data tables and their components, a business glossary (for context), and search functionalities (for data discoverability) and capabilities to ensure good governance.
A data catalog acts as a bridge between the technical and business aspects of data. Data analysts and scientists use catalogs to find datasets for analysis, while business users rely on them for business glossaries with data dictionaries to find datasets within a specific business context.
Serving as an organized inventory of an organization’s data assets, a data catalog provides users with information on available datasets, their origins, access permissions, and associated risks, all in a centralized location.
A key concern in deploying a data fabric is the development of an enterprise-wide data model. Many enterprises consider this as an expensive and time-consuming initiative. However, the concepts have to be defined for the first use case, and then the flexibility of the graph allows the models to adapt to evolving business needs.
A good place to start is to adopt an MVP mindset for data modeling, which focuses on a critical business problem to drive the broader data fabric initiative. Only the essential work can be prioritized to achieve the first significant business value. The mapping and modeling of data sources can be reused across various internal applications. This maximizes the data fabric’s ROI.
Moreover, customer data can determine the best business use case, as improving it directly leads to new revenue opportunities and addressing historically fragmented data.
Additionally, data models must follow best schema practices, including normalization and denormalization contextualized to reflect which data is being brought onto the fabric. The architecture can also leverage virtualization tools for unification and federation, extending storage cost benefits and reducing latency at the data consumption layer.
A critical phase in the project is linking metadata, the data model, and the data itself to downstream systems. Data virtualization plays a key role here and accelerates time to value. So, data stewards do not have to worry about extracting the data, ensuring the usable format, reformatting it, loading it, and waiting for the completion of other tasks. They can instead check the data in existing data repositories.
Some systems may not be well-suited for virtualization, particularly when performance or security is a concern, as is the case with external systems. Therefore, it is necessary to embed virtualization with materialization capabilities for optimal performance and security.
Many organizations operate with a mix of multi-cloud or hybrid cloud data infrastructures, making it impractical to follow a centralized data approach. Data fabric creates an abstraction layer that unifies diverse technologies under one umbrella without the need to centralize data storage. It enables self-service data access through no-code/low-code principles, powered by a blend of active and passive metadata and knowledge graphs.
The approach toward data democratization with data fabric at the core of the data strategy will fall short without proper data governance. It is not just another good-to-have process; rather it is the foundation upon which the data fabric is built. The governance layer ensures consistency in data privacy, security, and access from the core data layer up to the application, cloud, and network levels.
Mobile and IoT devices at the edge are rapidly driving data creation and insights, with the global datasphere expected to triple in size by 2025. Half of that data will come from mobile and IoT devices, collectively labeled “the edge.”
Due to this surge in data, enterprises are shifting from rigid on-premises setups to more flexible cloud and hybrid environments. However, handling this expanding edge data requires efficient processes (data processing/storage/optimization) across platforms from edge to cloud and back.
As a unified layer, data fabric provides data management and integration capabilities, making any data type from any source ready for human users and machines. As it is agnostic of deployment platforms, data process, data use, geographical locations, and architectural approach, data fabric ensures that data is efficiently accessed and governed.
While still emerging, data fabric acts as both the infrastructure and translator for data across various platforms, from data centers to different gateways and devices operating at the edge.
As you rollout the data fabric implementation, it is critical to leverage monitoring solutions to optimize storage solutions and manage change:
Monitoring: Implementing a data monitoring and observability solution helps understand data health and status across the ecosystem. These solutions enable automated monitoring, triage alerting, root cause analysis, and SLA tracking, all of which work together to ensure the quality and integrity of data as it flows through the data fabric.
Optimizing storage solutions: Enterprises can use technologies like compression, deduplication, and tiered storage for storage efficiency. For businesses that face unpredictable spikes in data streams and storage needs, elastic storage on the cloud provides on-demand storage capacity, adaptability to changing workloads, and support for different data types and protocols.
Managing change: As a part of the change management process within a data fabric implementation, organizations need to review and update data policies, compliance standards, and security measures to adapt to emerging challenges.
Gartner predicts data fabric deployments will quadruple the efficiency of data utilization while halving human-driven data management tasks. The complexity of diverse data types, sources, incompatible formats, and co-existence of on-premises data centers and cloud platforms add to the challenges of inefficient tools for data extraction and transformation.
Enterprises require an effective data management strategy to integrate and orchestrate data across multi-cloud and hybrid environments. While data virtualization can help eliminate silos and consolidate data, it lacks the automation ability to meet data quality needs. In this backdrop, Data fabric with a metadata-driven orchestration engine offers a smarter solution, to deliver seamless integration and substantial business value.
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