Data Archiving Accelerating AI Success with JD Edwards EnterpriseOne

Data Archiving is a long-recognized component of good Data Governance. New technologies such as AI/ML and analytics can benefit from a focus on data archiving, as effective data management enhances performance, ensures compliance, and broadens data sources for AI and analytics.

AI/ML and analytics generate enormous amounts of data that need careful management, especially as it ages. Many organizations have invested substantial amounts of money in ingesting and analyzing data. Archiving needs to be an integral part of the long-term governance of this data. The capabilities and responsiveness of AI/ML and analytics solutions are often based on the CPU and memory available, particularly in the Cloud. However, Cloud solutions can be expensive due to the pricing of compute, memory, and storage tiers.

Organizations should aim to maximize the utility of the compute-intensive environment where the most relevant data is analyzed, modeled, and information generated. Moving less-useful, aging data that may no longer be relevant or rarely accessed to less compute-intensive environments maximizes its value.

Moreover, transferring sensitive confidential data to other datasets reduces risk. This type of data is often subject to legal or regulatory requirements and must be protected from unauthorized access, use, or disclosure. Examples include Personal Identifiable Information (PII), Financial Information, Medical Information, Trade Secrets, and Intellectual Property (IP). In the context of analytics and AI, legal and compliance are still evolving regarding the data generated by these systems.

An added benefit of moving data to archived sets is that the data can still be ingested by AI/ML and analytics systems, thereby expanding the data sources available to these systems.

With Release 24, JD Edwards EnterpriseOne (E1) has increasingly become a product that can be leveraged when implementing AI/ML and analytics. ARCTOOLS is an archiving solution designed for JDE E1. It efficiently moves large amounts of data from production to archive without impacting the servers. ARCTOOLS can run 24x7 without interfering with users, achieving a typical throughput of millions of rows per hour.

ARCTOOLS and JDE

• Offers a time-tested approach for archiving JDE data.
• Comes with a pre-defined analysis for selecting archival data.
• Fast, flexible and easy to manage

Data Quality and Availability

• High-quality data is essential for successful AI/ML models. Ensure that your E1 data is clean, accurate, and accessible.
• Consider data integration, data cleansing, and data enrichment processes.

Enhanced Performance

• AI and analytics platforms benefit from moving latent and aged data to dedicated archiving and long-term governance platforms.
• By offloading historical data, active workloads can focus on real-time processing, improving performance.

Data Accessibility and Trust

• Archiving ensures that historical data remains accessible for analysis.
• Automated document categorization, predictive algorithms, and natural language processing (NLP) make data retrieval easier and more efficient.

Data Integrity and Compliance

• Archiving preserves data integrity by securely storing historical records.
• Compliance requirements often mandate data retention. Archiving helps meet legal and regulatory obligations.

Cost Efficiency

• Moving older data to an archive reduces the load on expensive compute resources.
• Reduced average cost per document processed contributes to cost savings.

Scalability and Long-Term Planning

• As AI generates enormous volumes of new data, archiving becomes essential for managing capacity and license limits.
• Long-term AI plans require strategies to handle aging data outside compute-intensive environments.

Integrating AI/ML with effective data archiving allows organizations to unlock new insights, maintain compliance, and optimize resource utilization.

Click here to explore more on how AI and analytics can enhance JD Edwards and supercharge your data with archiving.