Why Legacy Systems Modernization is Imperative in Modern Big Data Strategies

In the digital age of Big Data and Analytics, legacy systems are becoming a growing problem for organizations looking to implement digital transformation. An analysis found that maintaining some of the U.S. government’s legacy systems “costs about $337 million” annually.

Along the same lines, the U.S. Air Force anticipated the cost of maintaining a 14-year legacy mainframe to increase to $35 million in 2020. The number would be even higher now.As Big Data exceeds the limits of “traditional” database systems, there is a need to modernize the legacy infrastructure to gain a competitive edge. In the coming years, technologies like Big Data and Artificial Intelligence (A.I.) will drive more business models.

However, legacy system modernization has its share of challenges and requires professional expertise. Organizations must implement this transition to modernization without disrupting their internal processes.

Here is why legacy system modernization is imperative for organizations and how Emergys can help in this initiative.

Every modern enterprise requires some level of legacy system modernization to build efficiency and respond to evolving market changes.

As it stands, legacy systems are more prone to cyberattacks and security threats. In its research report, Cato Networks pointed out that legacy vulnerabilities are the biggest enterprise cyber risk. Similarly, a CERT alert warned about the surge in hackers’ interest in legacy-based ERP solutions.

Cyberattacks are no longer restricted to large corporate networks. 60% of small enterprises running legacy systems can shut down within six months of a successful cyberattack. They can only address legacy-driven vulnerabilities by updating legacy tools and security protocols through modernization.

In the age of Big Data, legacy systems are not designed for modern best practices, thus hampering both productivity and scalability. For instance, legacy applications may not be compatible with unstructured data sources.

Data latency can also be a challenge due to the lack of system integration. Most modern enterprise systems can integrate seamlessly with third-party systems through APIs. But legacy systems consume both time and resources to integrate with third-party tools.

Besides, to support proactive decision-making, modern enterprises require data analytics for valuable insights. And legacy systems certainly face challenges in providing real-time visibility into data flows.

Favorably, legacy system modernization enables business enterprises to transition to digital products with real-time data analytics. This can improve the customer experience, enhance the business decision-making process, and lower maintenance costs. Is legacy modernization necessary for every company? To answer that, business enterprises must address the following questions before undergoing any modernization program:

• Is the legacy system capable of processing different data formats from external applications?
• Is the legacy infrastructure capable of handling increased data volumes?
• Is the company spending more each year on the maintenance of legacy systems and applications?
• Does the company need to customize every integration to connect to new applications?

If the answer to these questions is “yes,” then it is time to evaluate the best modernization strategy. Next, let us discuss why Emergys is best positioned to modernize legacy infrastructure.

As data velocity and complexity keep growing within organizations, we believe this is the right time to modernize legacy systems. Here’s why:

• Legacy and mainframe technologies are expensive to maintain each year.
• Legacy system files and data are not compliant with industry regulations.
• Legacy-based batch processes are not able to cope with growing Big Data volumes.
• The existing data infrastructure does not support new data formats like audio, video, and weblogs for analytics.
• Data professionals looking for advanced analytics are constrained by the current limited computing power and resources.

• Study the existing legacy system and develop a Big Data implementation roadmap.
• Examine the mainframe applications (if present) along with their datasets and batch processes. Implement mainframe decommissioning by migrating to Big Data platforms.
• Leverage the capabilities of Big Data platforms like Hadoop and Elastic search to archive legacy data, including database tables, files, and documents.
• Use the Apache Spark technology to “re-platform” legacy processes on Hadoop.
• Implement enterprise data warehousing on Hadoop.

With our Big Data managed services, we can also streamline Big Data operations with minimal downtime. Here is a case study of how our cloud-based data archival solution saved a manufacturing company $0.5 million in process costs each year. Similarly, a U.S-based university reduced its data management costs by 50% by re platforming to Hadoop.