The vast majority of companies who are moving to cloud applications also have a significant current investment in on-premise operational applications and on-premise capabilities around data warehousing, business intelligence and analytics. This means that most of them will be working with a hybrid cloud/on-premise data management environment for the foreseeable future.
Moving at ‘cloud speed’, and setting up a new application in a matter of hours, is a big advantage in terms of business agility, but one cost is that managing data becomes more complicated.
It seems that more often than not, a hybrid architecture isn’t planned - it just happens. The ‘typical’ use-case pattern starts with organisations integrating a cloud application, perhaps for CRM or HR, then, they add cloud database before finally adding a cloud data warehouse and/or analytics capability.
A lot of the time it’s the business that drives this pattern in an effort to solve a particular problem. Most frequently, IT is hugely involved in the effort, but the cloud analytics decision is made by the business side. As a result, IT inherits significant new data management complexity.
It can be difficult to retrofit governance into existing systems. Often, the focus is on the initial data migration to the new operational application or analytics, where a simple bulk data loader is employed in the interest of speed and agility. This has several downsides:
Once the new applications have gone live, focus shifts to ensuring data consistency. Moving between cloud and on-premise systems and cloud-to-cloud brings new challenges, and leave fewer resources dedicated to overall data management.
If you don’t want to slow down the business initiatives that are driving the new applications, but still want to prevent that data complexity or chaos, it will pay to have a data management architecture and best practices in place before-hand.
Data management in a large organisation is challenging. But this gets even more complex when it’s hybrid. The key is to plan ahead so that you’re not a roadblock to the business. Key considerations for a successful programme should question:
At the end of the day, the business challenge is to deliver value faster than the competition. The IT challenge comes with meeting the speed and quality requirements of the business while enabling them to accelerate their business agility. It can be done, but it takes careful planning and a good, forward-looking data management architecture.
The vast majority of companies who are moving to cloud applications also have a significant current investment in on-premise operational applications and on-premise capabilities around data warehousing, business intelligence and analytics. This means that most of them will be working with a hybrid cloud/on-premise data management environment for the foreseeable future.
Moving at ‘cloud speed’, and setting up a new application in a matter of hours, is a big advantage in terms of business agility, but one cost is that managing data becomes more complicated.
It seems that more often than not, a hybrid architecture isn’t planned - it just happens. The ‘typical’ use-case pattern starts with organisations integrating a cloud application, perhaps for CRM or HR, then, they add cloud database before finally adding a cloud data warehouse and/or analytics capability.
A lot of the time it’s the business that drives this pattern in an effort to solve a particular problem. Most frequently, IT is hugely involved in the effort, but the cloud analytics decision is made by the business side. As a result, IT inherits significant new data management complexity.
It can be difficult to retrofit governance into existing systems. Often, the focus is on the initial data migration to the new operational application or analytics, where a simple bulk data loader is employed in the interest of speed and agility. This has several downsides:
Once the new applications have gone live, focus shifts to ensuring data consistency. Moving between cloud and on-premise systems and cloud-to-cloud brings new challenges, and leave fewer resources dedicated to overall data management.
If you don’t want to slow down the business initiatives that are driving the new applications, but still want to prevent that data complexity or chaos, it will pay to have a data management architecture and best practices in place before-hand.
Data management in a large organisation is challenging. But this gets even more complex when it’s hybrid. The key is to plan ahead so that you’re not a roadblock to the business. Key considerations for a successful programme should question:
At the end of the day, the business challenge is to deliver value faster than the competition. The IT challenge comes with meeting the speed and quality requirements of the business while enabling them to accelerate their business agility. It can be done, but it takes careful planning and a good, forward-looking data management architecture.
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