Amazon launched its Amazon Web Services (AWS) platform 15 years ago. Two years later, the company has built more than 100 applications on top of it. People now know how this story ends -- almost all enterprises deploy applications in the cloud in some form or fashion. However, the adoption of cloud services by enterprises remains challenging, especially for enterprises with large on-premise operations. How to better migrate critical business data and metadata to the cloud to support ongoing operations and analytics remains a challenge for many enterprises.
Despite the increasing awareness of the strategic value of cloud migration in recent years, many enterprises continue to take a counterintuitive and costly approach. Enterprises tend to break down their approach into pieces -- "just move new information to the cloud without worrying about the current data that exists in the on-premise facility," or "think about governance and security later." While this approach may help limit budget concerns and clarify scope, it can be cumbersome in the long run and delay the potential return on investment of cloud migration. In addition, if an enterprise leaves some data in an on-premise deployment facility, it will ultimately limit its use for analysis in future modern data stacks. The reality is that taking full advantage of modern data stacks requires a clear understanding of the use cases for moving to the cloud and the data needed for success.
Adopt Agile methods
Ask business leaders why they haven't moved their business from an on-premise facility to a cloud platform, and they often cite data governance concerns. While governance is indeed critical to securing data and ensuring proper use, the need to truly adopt agile data governance goes far beyond that. Data has the power to keep businesses running and thriving during times of disruption, and today's organizations simply cannot afford to have their data disrupted due to governance issues.
Investing upfront in agile data governance or reinvesting in existing processes prevents data and analytics clogging and enables organizations to use more modern tools to accelerate return on investment. In addition, it facilitates collaboration between data teams and allows organizations to acquire knowledge as they work. Especially in cloud migration, this makes it easier for data producers to understand why enterprises are moving to cloud platforms and what data-driven initiatives they want to run in modern data stacks. With this knowledge, data engineers can create a backlog of prioritized data assets in an on-campus deployment facility and wait for migration.
Get the analysis in order
Whether you're starting over in the cloud migration process or trying to upgrade some immature technology, organization and consistency are key. Ask big questions to establish metrics that will guide current processes and what success will look like in the future. Its data is then structured into a consistent architecture and style to ensure smoother running.
An enterprise may want to organize data according to existing architecture types. Over time, consider a hierarchical data model. For example, an enterprise's data might be organized by business units, but in the future it wants to consolidate around entities such as customers, products, and orders. Perhaps the enterprise uses the star schema today but wants to layer on the table to make it easier to analyze in the future. Regardless of the choice, consistently applying the architectural style will ensure that the platform works not only for data producers, but also for data consumers.
Use the right tools for the process
The best approach without investing in the right tools will still not be completely successful. Of course, this area has been and will continue to be more challenging for many businesses as the recession and inflation fears put pressure on budgets. However, this new reality does not need to limit cloud migration. Understanding the end-to-end process will help organizations prioritize the right tools, increase efficiency, and create real business value.
Many of these choices will depend on the use case of the enterprise. As an enterprise's budget increases and migration scales up, data governance platforms, data quality, analytics, lineage, and more can come online when their priorities become strategic. For example, if an enterprise is trying to identify complex dependencies and the most commonly used assets, then lineage will be key. Alternatively, if an enterprise is trying to keep track of the data it owns and ensure that it also shows up in the new environment, then metadata inventory and comparative analysis are obvious priorities. Regardless of an enterprise's short - and long-term goals, a data catalog is the glue that holds metadata together, ensuring discoverable and searchable, analyzable, and enabling self-service.
As data leaders know, one of the most challenging parts of any migration process is engaging the right stakeholders at the right time. To be truly successful, all stakeholders should be involved in cloud migration and do practical analysis, not just hypothesis. Choosing analysis use cases that align with what consumers actually need to accomplish, and setting clear deadlines, can help businesses measure value and prevent getting stuck in the first place.
In addition, one of the benefits of building a cloud migration foundation on top of the data catalog is that it enables coordinated, consistent, and centralized work among parties. The data consumer can process the data in real time to assess how successful the model is at answering questions. Administrators can document the business glossary and metric definitions along with the data. Because all of this and more revolve around a platform, it makes coordination easier and prevents future knowledge debt.
Ultimately, it is never too late to migrate to the cloud with the right agile data governance methods, analytics, tools, and people processes. As the demand for data grows exponentially, participating in the process, even in a piecemeal approach, can pay dividends.