This Long Awaited Technology Could Revolutionize Structured Data Lifecycle Management

History of Structured Data Management

Structured data management has undergone significant evolution over the past 30 years. It has transitioned from basic databases and manual processes to advanced, technology-driven systems, highlighting the increasing importance of data in decision-making.

The late 1990s and early 2000s saw the advent of big data, real-time processing, and the development of NoSQL databases, along with the establishment of data governance frameworks. In the last decade, automation, AI, and cloud computing have further transformed data management, making it more intelligent, scalable, and integral to business strategy. This has ensured ongoing innovation and competitiveness.

Today, data management has evolved to include not just structured and unstructured data, but also complex data integration systems like data lakes, data pipelines, and advanced analytics platforms. This progression has transformed data management from simple storage and retrieval to sophisticated processing and analysis, enabling businesses to harness the power of data for strategic decision-making.

Legacy Applications

Legacy applications are outdated software systems that were developed with older technology and are still in use despite being surpassed by modern solutions. These systems often run on obsolete hardware, rely on discontinued software frameworks, and lack support for contemporary security, integration, and data management standards.

While they may still function for specific business operations, legacy applications can hinder organizational agility and innovation due to limited scalability, poor compatibility with new technologies, and high maintenance costs. Additionally, they are prone to accumulating dormant data and pose compliance challenges, as they are not designed to meet current regulatory requirements. Transitioning away from legacy applications involves risks and complexities, but it is crucial for organizations to modernize in order to improve efficiency, enhance security, and stay competitive in today’s rapidly evolving technological landscape.

Dormant Data - The Risks

Dormant data refers to information that is no longer actively used or accessed but is still stored within an organization's systems. This data might include old files, outdated records, or legacy system data that is retained for compliance, historical, or backup purposes but no longer serves any immediate business function.

Retiring dormant data is crucial for organizations to maintain data integrity, optimize storage costs, and ensure compliance. It enables organizations to focus on managing active, valuable data, leading to more effective decision-making and enhanced operational agility.

Failing to manage dormant data correctly can pose significant risks to an organization, including increased security vulnerabilities, higher costs, and compliance issues. Dormant data, often stored without regular access or monitoring, can become a prime target for cyberattacks, as it may contain sensitive or outdated information that lacks adequate protection.

Dormant data, often referred to as "forgotten data", poses significant risks when left unmanaged. As data sits unused and out of sight, it can easily fall through the cracks of security protocols and data governance policies, making it an attractive target for cyberattacks. Without regular monitoring or updates, dormant data is more likely to contain outdated and vulnerable information that could be exploited by malicious actors.Additionally, storing large volumes of unused data increases costs related to storage and maintenance while also complicating data management efforts.

Poor handling of dormant data can also lead to compliance breaches, especially if it includes personal or regulated information that isn’t properly monitored or archived. This can result in hefty fines, legal liabilities, and reputational damage. Properly managing dormant data through archiving or deletion is essential for mitigating these risks and maintaining an efficient, secure data environment.

Business Challenge

Many growing companies have made several acquisitions and tried to integrate the acquired businesses into their existing systems. As part of their digital transformation efforts, companies have also been transitioning to new versions and applications to meet the demands of the modern world.

As a result, many old legacy applications and databases are still in use or stored in data centers. As the amount of data collected and managed in-house increases, so do operational costs, liabilities, and energy consumption.

Holding onto legacy applications presents several significant challenges for organizations. One of the primary issues is technical debt, as outdated software becomes increasingly difficult to maintain and modify. This debt not only leads to high maintenance costs but also hinders the ability to implement new features or integrate with modern systems.

Additionally, legacy applications often run on outdated hardware or software platforms that lack vendor support, leading to increased vulnerability to security threats and compliance issues.

Another key challenge is the limited scalability and performance of legacy applications. As the volume of data and user demands increase, these older systems may struggle to keep up, resulting in reduced performance and a poor user experience. The lack of scalability also means that businesses cannot easily expand or adapt these applications to meet new operational needs.

Moreover, skills gaps exacerbate these issues. As the workforce familiar with legacy technologies retires or shifts to newer platforms, it becomes difficult to find personnel with the necessary expertise to manage and troubleshoot these older systems.

This scarcity of skills can lead to operational disruptions and increased reliance on costly external consultants.

Holding onto legacy applications can lead to several compliance issues, as these outdated systems may lack the necessary controls and security measures to meet current regulatory standards. Legacy applications often do not support modern encryption techniques or data protection frameworks, making them vulnerable to data breaches and unauthorized access.

Additionally, the lack of regular updates and vendor support can result in non-compliance with data privacy regulations like GDPR, HIPAA, or CCPA. This can expose organizations to hefty fines, legal liabilities, and reputational damage. Moreover, legacy systems often have limited logging and auditing capabilities, making it difficult to track data usage or detect suspicious activity, further complicating compliance efforts. Addressing these issues is crucial to ensure that legacy systems do not become compliance liabilities for the business.

Finally, holding onto legacy applications impedes digital transformation efforts. Modernization initiatives that leverage cloud, artificial intelligence, or advanced analytics are often incompatible with legacy systems, creating data silos and limiting innovation. This inability to fully embrace new technologies can ultimately leave companies at a competitive disadvantage in an evolving market.

Addressing these challenges requires a strategic approach to application modernization, such as migrating to newer platforms or adopting cloud-based solutions.

The Solution

ROAD is an advanced data lifecycle management platform designed to centralize legacy and archived data within a single environment, whether deployed on-premises or in the cloud.

It provides a streamlined solution for extracting data from outdated applications, transferring it to the storage of your choice, and enabling easy data reporting and access. Built from the ground up with a cloud-native backend, the platform efficiently moves structured data at high speeds, making it an ideal choice for initiatives focused on legacy application retirement and dormant data management.

The platform represents decades of thought leadership and development, bringing together industry knowledge to create a comprehensive DLM solution. It will deliver a unified vision focused on user experience, practical features, seamless integrations, and real-world use cases. Ultimately, it will serve as a one-stop solution for managing structured data, including the governance of inactive yet critical information. Our mission is to support clients and partners throughout their data journey—whether it involves meeting compliance and reporting requirements, simplifying data analysis and transformation, or enabling digital transformation initiatives.

For more information on the capabilities of ROAD today, please visit https://www.infocorvus.com/solutions/ROAD