Cloud has become a progressively ubiquitous delivery model for organizations of all sizes, leaders in IT and other stakeholders. But these players should not forget that the cloud is not an all-in-one solution. An enterprise should not expect to have its various requirements fulfilled by a single cloud services provider. In most of the cases, businesses moving to the cloud will require the services through a range of providers, combined with traditional on-premise application-to-application (A2A) and business-to-business (B2B) systems. That leads to an increasing demand to adopt integration strategies that support multiple complex integrations: A2A, B2B, cloud-to-cloud (C2C) and on-premise enterprise applications to SaaS/cloud applications.
The fact is that most cloud computing solutions are not massive and that’s why they require multi-cloud solutions. This means that several cloud computing systems, both private and public, are picked together to get to the solution the enterprise requires. For example, you might have database-as-a-service from one provider, IaaS from the other, and a PaaS from a third.
The idea is to incorporate these cloud solutions based on the requirements to form an integrated system that shares both processes and data in real time. Multi-cloud systems are widely distributed which need to be planned well. This includes operations, governance, performance, security, and most significantly, data integration, that allows information to flow between the cloud-based components and other external enterprise systems.
But the fact is that most enterprises do not have an integration strategy. These enterprises that practice data Integration without a strategy and much forethought, fall prey to a series of data integration problems that need to be solved immediately. And there comes the challenge to move data integration from the tactical, or project-level thinking, to strategy, or enterprise-level thinking.
If you don’t have a data integration strategy, you need not to panic as many other companies, both small and big do not have it either. Actually, most of them don’t even realize how data integration strategy can help them. In that case, you need to update yourself with the evolving data trends. You need to study the evolution to scrutinize how your data integration strategy will help you to manage data adaptation and scaling as your data volume through different resources rises with time.
The advancements in data integration can be best realized when you consider the differences between solving data integration problems using custom programming code versus leveraging data integration technology. Earlier, data integration meant merely code generators and vestigial ETL, and in some cases, custom coding was an option as well. These days, data integration brings us real-time data exchange, sophisticated management capabilities, data governance and many other features that make data integration much more turn-key that makes it possible to deliver quick business value. The players in IT industry should realize that the current generation of data integration offers a full-fledged package that includes ETL, enterprise application integration, and real-time integration functionality, as well as data cleansing and data profiling tools. Moreover, it’s easy for them to integrate with data stores in public and private clouds, with many of them running as cloud-delivered technology.
There are three emerging reasons that explain the importance of prioritizing the development of a data integration strategy. No matter the variations in the enterprises running all over the globe, most enterprises find that big data, use of cloud computing, and the need to eliminate data silos drive interest (or re-interest) in data integration these days.
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Big Data: With the rising demand for Big Data, many structured, as well as unstructured data stores, are being logically gathered together through the use of various new technologies. These technologies include Hadoop and other Big Data software that allow management and alignment of data into the petabyte range, distributed on file systems. There’s a clear need for data integration technology to maintain and manage the movement of data from a store to another.
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Cloud: The advancement of the cloud has redirected the process of data integration. As the placement of data on public clouds has increased, the need to sync these data back into the enterprise has heightened as well. This has to be done carefully as a shift to the cloud should not just create another silo of data, but there should be a consistency between these systems and databases that fed into the local data center or public clouds.
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Elimination of silos of data: Finally, talking about the prevailing problem of many enterprises which stays unresolved for them is, the elimination of silos of data. Though the enterprises can access data integration technology for some time, most enterprises have built or purchased core systems without vision or technology to share critical business data. If that’s what bothers you as well, it’s time to give another thought to data integration methodology that you are using right now and adopt strategic data integration technology.
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As part of the data integration strategy, you need to ascertain the value of the technology through different levels of business cases. Make sure to analyze the costs incurred and benefits reaped from your strategy. For example, avoiding double entry in customer information. Also, you need to consider the cost benefits, which cannot be defined well but is as important as other factors. For example, increased customer satisfaction due to the organizations’ ability to provide better data around order status.
