Data analysis is perhaps the most important step in all of IT migration. The stakes are high, as failed migrations result in costly outages, lost data and inefficient business operations. In an earlier posts, we reviewed the steps of Project Management and Data Discovery/Collection. But a seamless transition from raw data to analysis is essential to build an actionable environment for informed data-based decisions. At this stage, data is scrubbed, normalized and linked to one other. This phase quickly identifies sources of the most current information, while pinpointing any missing data. Bad or duplicate sets are removed, while dependency mapping begins to define baseline inventories.
During interdependency mapping, clear connections between data and the infrastructure is assembled. This pinpoints which applications communicate with one another, and whether it’s internal or external. Identifying core services usage is also achieved during this phase. Maps across applications and infrastructures are drawn, and a master inventory is engineered to identify gaps and conflicting information. Data is then normalized and an inventory baseline is created. Finally, the target site is sized – with all performance and infrastructure data calculated to maximize compute power and storage requirements.
Given the level of detail, data analysis certainly appears to be one of the most difficult stages in the IT migration process – and failure can be very costly. A report by Bloor Research estimates failure rates for most IT migration projects at close to 40 percent. This is significant considering Forbes 2000 companies currently spend about $5 billion annually on migrations. And the story gets even worse when failed migrations lead to outages. A study on US data centers calculates the cost of unplanned IT infrastructure downtime at $7,900 per minute. With a typical incident lasting almost 90 minutes, each company faces a shocking $700,000 financial hit for each outage!
Effective analysis minimizes downtime by quickly pinpointing the right data sources to transport to the new system. Based on the quality and type of data sources, IT can more effectively build in migration timelines and understand if the cost of migration is prohibitive compared to the quantity of data. The best analysis also ensures new systems are brought online faster without losing critical historical content.
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