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Legacy Data Management

Legacy data management is an indispensable part of today’s technological landscape. As businesses and organizations continue to adopt new software, tools, and systems on a regular basis, the need to maintain historical information grows ever more pressing.

Importance of Data Inventory

In order to ensure seamless integration between existing and new technologies, companies should consider developing more comprehensive understandings of their data inventories. Data inventory is the process of identifying and cataloging all data assets within an organization. This includes not only current data but also legacy system data — information stored in previous generations of IT systems or systems that are no longer supported by their original vendors. A thorough data inventory is essential for developing effective strategies for managing legacy data and addressing any challenges. 

One of the most critical aspects of legacy data management is determining how best to store the information. Legacy data storage systems can range from traditional on-site servers to cloud-based solutions. It is important for organizations to carefully assess their specific needs before committing to a particular method for legacy data storage. When evaluating options, various factors should be considered, such as:

  • The volume of data
  • The required frequency of access
  • Security concerns
  • Cost constraints 
  • Regulatory and compliance requirements

Another key consideration in managing legacy data systems is how to convert old data formats into formats that are compatible with current technologies. For instance, legacy data conversion might involve migrating databases into newer models or transforming file types so they can be accessed by modern applications or platforms. This process can be complex — sometimes requiring specialized knowledge or expertise — but it is crucial for ensuring that valuable historical information remains accessible and usable. 

Challenges of Legacy Data Migration

Managing legacy system data can present several unique challenges that need careful attention.

  • Data quality: Legacy systems may contain inconsistent or inaccurate records due to factors such as changes in regulations since the initial recording. 
  • Compliance issues: Organizations should ensure that they handle sensitive information according to relevant regulations (such as the GDPR) when dealing with legacy data. 
  • Integration difficulties: Connecting older systems with newer platforms can require significant work, especially when the legacy technology is based on proprietary formats or protocols. 
  • Security concerns: Older systems may be vulnerable to cyber threats, so organizations should take appropriate measures to protect their legacy data. 

What Is Legacy Data?

Legacy data is digital information that is stored in outdated or obsolete IT systems but still holds value for the organization. It can also be historical data that is current and stored in legacy systems that are still in use. Effective legacy data management involves a few steps, including: 

  • Understanding one’s data inventory
  • Determining suitable storage and archiving solutions
  • Converting old formats into compatible ones

By managing their legacy data effectively, companies can access valuable insights from the past and enable more informed decision-making processes in the present.

Legacy Data Modernization

Legacy data modernization is the process of updating and improving an organization’s existing information systems that were previously developed. This process is essential in today’s rapidly progressing digital world, as organizations have more need than ever to ensure that their systems are up-to-date and capable of handling contemporary data formats. 

Data Management Systems

One example of legacy modernization is migrating from a traditional data management system to a more agile, cloud-based solution. A data management system is an essential component of practically any organization, as it helps businesses store, organize, and retrieve their vital digital information.

There are many examples of data management systems available in the market today. Each different kind offers unique features and benefits that cater to the specific needs of different kinds of organizations. However, as technology advances at breakneck speed and cloud computing becomes increasingly popular, businesses need to adapt their legacy systems to keep pace. 

Cloud Infrastructure

Cloud infrastructure plays a vital role in the modernization process. But what is cloud infrastructure? Cloud infrastructure refers to the various hardware and software components — such as servers, storage devices, and networks — required to support cloud computing services. By transitioning from on-premises solutions to cloud-based platforms such as Microsoft Azure, organizations can achieve increased flexibility and scalability while reducing operational costs. There are even some solutions such as Skytap that allow you to migrate and run legacy systems in the cloud. 

Legacy Data Management Challenges

Integrating legacy data into these new environments, such as the cloud, can pose a set of challenges. One such challenge is ensuring that the existing information remains accurate and usable after migration of legacy systems. This may involve reformatting or restructuring the data so that it can be utilized effectively within modern databases without compromising quality.

Migration from legacy systems to modern databases involves moving vast amounts of critical information between different platforms and architectures. Successful migration requires thorough planning and execution if the inherent risk of data loss or corruption is to be minimized. The process may also involve updating or replacing any business applications that rely on the legacy system’s data structures. 

Data Assessment

Before beginning a legacy data modernization project, it’s crucial to conduct a comprehensive data assessment (also called a data audit). A data assessment involves examining existing systems and databases to determine which data elements are valuable and relevant and identifying any potential issues that could impact migration efforts. The findings from this assessment can then inform decisions about which platforms, tools, and methodologies should be employed during the modernization process. 

Migrating legacy data effectively requires a thorough understanding of the proper handling of outdated technologies and methods. By integrating digital information into modern databases such as cloud infrastructures, businesses can benefit from improved agility, cost savings, and enhanced functionality. However, success ultimately hinges on effective planning and execution so that no vital organizational assets are inadvertently compromised during migration.

Legacy Data Migration

Legacy data migration is an essential process for modern businesses that want to maintain the value of their historical information. As companies adopt new technologies like cloud-based platforms, it becomes critical for them to ensure that their existing data remains usable within these new environments. Legacy data migration is the practice of transferring historical information from older systems to modern systems so it can continue to be accessed and leveraged by the organization that owns it.

Data Center Migration Tools

Data center migration tools make it much easier to transfer data from one location or environment to another. These tools offer features like:

  • Automated discovery
  • Mapping
  • Tracking of dependencies between applications and infrastructure components
  • Robust scheduling capabilities
  • Real time monitoring during migration processes

By using these advanced data migration tools, organizations can minimize downtime and avoid potential data loss during the transition. 

Legacy System Migration

Migrating legacy systems requires careful planning and execution to minimize disruptions to business operations. It’s also very important to ensure that all relevant stakeholders are engaged throughout the process. A key aspect of effective planning is identifying which legacy applications should be migrated directly into the new environment and which should be re-platformed or re-architected for better compatibility with modern technology solutions. 

Data center migration is a complex undertaking that involves many facets — including hardware relocation, application portability testing, and network connectivity adjustments. Successful migrations require close collaboration among the various IT teams responsible for different components within an organization’s IT infrastructure — from application development groups working on legacy app migration to those managing the deployment of new cloud-based solutions. 

By following best practices in legacy system migration, organizations can reduce maintenance costs associated with outdated technologies while also enhancing scalability and agility. Effective communication and coordination are also vital for minimizing service disruptions during data center exit procedures and ensuring seamless transitions. 

Legacy application migration involves moving software applications from older, outdated systems to newer, more efficient platforms — such as migrating from on-premises servers to cloud-based infrastructure. This process often involves re-architecting or re-platforming applications to ensure compatibility with the target environment. Alternatively, there are solutions such as Skytap which allow you to migrate and run IBM Power workloads natively in the cloud without refactoring or rewriting applications. By successfully executing legacy application migration, organizations can reduce total cost of ownership (TCO) associated with maintaining aging systems while increasing operational efficiency and business agility at the same time.

With Skytap on Azure, you can migrate entire IBM Power estates, including applications, data, and networking configurations to the cloud without rewriting or refactoring. Learn more about what Skytap can do for your legacy data management and cloud transformation.

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