Many businesses are moving towards a cloud-based approach in terms of managing their data, but that doesn’t mean that incorporating the cloud into businesses is an easy process. Also, after moving to a cloud-based environment, many organizations realize that their data is now spread across multiple disparate systems and siloed into various different cloud environments. Such silos prevent businesses from effectively and efficiently leveraging their critical data at scale. With more and more companies inching towards cloud adoption, and unfathomable amounts of data residing in siloed environments, how can businesses ensure that data is not only accessible, but usable as well? It all comes down to cloud data integration.
Cloud Data Integration
Cloud data integration means the combination of data from different cloud systems, integrating them to gain a unified view of the data.
It is unavoidable for organizations to have their data siloed across various sources, mostly because of the scale of their business, or because their business operations are spread out across a variety of locations and geographies. Also, organizations have their own preferred methods of storing and managing data. How can companies make the right decision in terms of optimizing their data needs? In such situations, it becomes crucial for organizations to make sure they have access to their integrated data across different business units and for this data to be available to decision makers of the organization at the right time for the right decision-making processes.
Cloud data integration bridges the gap between existing data, helping stakeholders to derive meaning and insights from the available data across the data sources and ultimately helping organizations to make informed business decisions. Cloud data integration is the process of consolidating data from disparate systems such as public cloud, private cloud, and on-premises systems to gain a unified view of the data to match the needs of today’s businesses, where the ultimate goal is to make data easily accessible to key users and systems in real-time.
Challenges with Data Integration
However, data integration & more specifically cloud data integration can itself be challenging.
A company could be using multiple public clouds like AWS, Microsoft Azure, or Google Cloud Platform, or it could also be operating in a hybrid cloud environment with on-premises systems, cloud systems, and legacy systems as well. It is not uncommon for some organizations to be operating data management IT systems that were first established in the late 70’s or 80’s. Many companies still rely on legacy systems or have data stored in those systems for specific use cases. Transferring data between these systems can be extremely time consuming. And without the right processes or the right automation tools in place, it is challenging to integrate data of different schemas and formats.
Additionally, cloud data is often a mix of structured and unstructured data, and there is no standard approach or protocol available to integrate these data systems. In such cases, the right solutions are needed to connect to these different sources to get the necessary data.
Lastly, the technology itself that organizations use in the cloud for integrating data, such as the traditional extract, transform and load (ETL) processes, are very rigid because of the physical connections that need to be managed between the systems, lacking the overall flexibility to adapt to a rapidly changing business environment. It becomes difficult to build flexible data models that can be easily scaled up for a cloud environment.
Alleviating Data Connectivity Woes
To overcome these challenges and facilitate effective cloud data integration, companies need logically oriented data integration solutions, enabled by data virtualization, rather than traditional, physically oriented data integration solutions, like ETL processes.
Cloud data integration is the missing piece of the puzzle for organizations to gain data visibility. As more and more data is accumulated on the cloud, it is important that all the data is integrated and adhering to the right practices and protocols to not only make informed decisions but to also have clear visibility into the cloud data.
To avoid hassles at a later stage, it is best to consider cloud data integration right from the start. Start thinking about the architecture with regard to data integration and data analytics. Think in terms of how the architecture will exist in the cloud, and how it will communicate with the on-premises architecture. Start thinking about cloud data integration early, to avoid situations in which data growth becomes exponential. Organizations need to have the right architecture and practices in place to govern, control, and manage the whole cloud environment.
For more insights on cloud data integration alleviating data connectivity woes for organizations, check out our discussion with Sutender Mehta, Product Marketing Manager for the EMEA and LATAM regions at Denodo, for more information on All Things Data!
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