In many modern organizations data migration usually viewed as an administrative IT task. This is understandable because at a fundamental level we are talking about moving data from one place to another.
Whether the underlying objective involves upgrading infrastructure, improving system performance, promoting cloud adoption, ensuring compliance, or a number of other options, migration is a means to an end rather than something of a more strategic nature.
Vice President of Product Marketing at Datadobi.
Compounding the problem, more than 80% of enterprise data is unstructured and scattered across multiple formats, applications, and storage technologies. This makes it difficult to know what data exists, where it is located, and how valuable or risky it may be.
As a result, applying traditional data migration to these use cases becomes problematic, not least because most legacy tools lack the intelligence to interpret unstructured data sets.
They treat all files the same and, without the ability to evaluate content or context, can mean businesses move redundant or sensitive information to the wrong storage environment, creating new weaknesses and exposing the organization to unnecessary risk.
Changing your thinking
In this context, data migration needs to be redefined as a process that begins before organizations even consider moving anything.
The starting point should be to create a clear understanding of the existing data set: what is stored, how it is used, who owns it, and whether it serves any other purpose.
This insight forms the basis for making better decisions about what data to retain, move, reclassify, or delete to meet the demands of enterprises running complex hybrid technologies. IT infrastructure.
The main question, of course, is how? Simply put, today's generation of intelligent data management tools makes this process scalable by using metadata analytics to quickly classify unstructured data and in context.
This allows organizations to act strategically rather than perform blind migrations in which all information is treated the same, regardless of whether it was created five minutes ago or fifteen years ago and whether it has real business value.
Take automationfor example, which uses analytics to minimize manual efforts on the part of IT teams and ensure consistency across different data storage environments.
As a result, migration becomes a proactive mechanism for modernizing infrastructure and improving governance, rather than a reactive routine.
A strategic approach to migration also allows organizations to take full advantage of the flexibility offered by modern storage systems.
This allows you to better align data value and storage investment, ensuring that hot data supporting active workflows is prioritized for high-performance environments, while cold or dormant data sets are securely archived or deleted according to policy.
This shift helps address the cost-effectiveness issues traditionally associated with migration and turns it into a catalyst for change, creating a cleaner, more manageable data environment.
Modern migration in action
So what does an organization rethink its approach to data migration look like?
The translation of this modern approach into daily activities depends on its effective execution. This starts with clearly defined roles where THISCompliance and business teams collaborate towards common goals.
Migration decisions should reflect both technical feasibility and organizational priorities, including regulatory obligations and business outcomes.
These organizations also have a clear understanding of their data warehouse and actively monitor its development. They have moved beyond basic file inventory and now use intelligent systems to determine which data is most important, which poses a risk, and which no longer serves any operational purpose.
From a practical perspective, data discovery and classification must also be done at a scale that meets broader business needs. This is best achieved through platforms that can enforce consistent policies, automate tasks, and track progress across systems.
Rather than treating migration as a separate project, it should be included in broader programs such as infrastructure renewal, cloud service implementation or improvement of management.
While these activities are typically associated with ongoing data management, they are also necessary during migration.
Automating processes such as assigning data ownership and applying retention schedules helps ensure that migration decisions align with the broader management strategy rather than being handled manually or revisited later.
In doing so, organizations not only improve the success of current migrations, but also lay the groundwork to make future migrations more efficient and less disruptive.
Measurement also matters. While few organizations today consistently monitor migration performance, those that are successful track the effectiveness of migration efforts using clear key performance indicators, ranging from storage cost reduction and policy compliance to availability, accuracy, and user experience.
Implementing these metrics not only helps demonstrate value, but also shapes ongoing improvements and aligns migration with changing business needs.
Ultimately, those who use this approach report less memory waste, lower operational costs, and faster access to reliable information.
More importantly, they can adapt their data infrastructure to meet changing business requirements without encountering the all-too-familiar pitfalls of an outdated approach to data migration.
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