How to manage data integration during an acquisition
Growth is a critical part of every business. To stay on top of the current landscape, innovation is key. Especially when an acquisition happens, there are decisions that need to be made regarding data integration. “The ability to store, manage, control and replicate data between the mainframe, on-premise software, and public cloud platforms in real time will allow businesses to tap into innovation—even during an acquisition,” says Milan Shetti, CEO, Rocket Software. Additionally, in an article on CIO.com Shetti shares how to manage data integration effectively during and acquisition to better manager and leverage your organization’s data.
Data synergy
“Organizations need to integrate on-premises systems, like mainframes, with cloud platforms to best manage influxes of data and stay ahead of the curve amongst competitors. With a wide range of varying data, the ability to leverage the reliability, data integrity, and scalability of the mainframe with the accessibility and collaborative benefits of the cloud is the best solution for overall data visibility and control. For this to be possible, data integration tools must also allow access to and replicate data from intricate mainframes, preserving the value of historical data. Implementing hybrid-cloud solutions also dismantles data silos that have plagued businesses for years.”
Data synchronization
“Implementing real-time synchronization capabilities into business’s storage systems is crucial to ensure that data reflects their operational realities within a rapidly changing economic landscape. Without real-time updates, IT teams could run the risk of making misinformed decisions based on outdated data, which can cost the business money. With real-time data syncing, data integration tools can run historical, on-premises data for cloud-based AI and ML analytics, which are especially important datasets that are often not utilized, especially when a company is dealing with competing priorities in an acquisition. The use of AI will only continue to rise, making this capability crucial for decision-making.”
Software development
“Software development is another crucial reason why well-managed and up-to-date data is crucial. This is especially true for quality assurance (QA) testing of companies’ applications. When organizations leverage DevOps practices in their software development processes, testing applications in isolated environments is no longer sufficient. Teams must integrate up-to-date data into the testing process to replicate real-world scenarios, which ensures that software functions as expected under various conditions. Using realistic data sets during testing allows teams to validate application performance, security, and user experience more effectively. It also enables proactive issue identification, leading to faster bug detection, reduced development time, and an overall enhancement of application performance.”
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