How to measure the success of Data Archiving (Part 3)
In the final part of this 3 part series of blog articles 'measuring the success of data archiving', we look at two more customer examples
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How to measure the success of Data Archiving (Part 2)
In part 2 of 'measuring the success of data archiving', we look at real customer examples
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How to measure the success of Data Archiving (Part 1)
Can you measure the impact of data archiving? How do you demonstrate ROI? We address these questions and others in this JD Edwards article
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What is Oracle Validated Integration (OVI)?
Find out how Klik IT achieving Oracle Validated Integration with JD Edwards EnterpriseOne Expertise for its integration of Purge-it can help you and your organization
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Purge-it Version 5.2 FAQs
Explore some of the most frequently asked questions relating to the latest release of the JD Edwards data archiving solution, Purge-it
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Is Purge-it easy to use for E1 data archiving?
Purge-it customers tell us one of the key drivers for choosing the data archiving solution is its usability. In this blog post we look at the main factors that make Purge-it easy to use.
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What is Purge-it?
Find out all about Purge-it. How it works, who it's for and what it offers JD Edwards users
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Get a JD Edwards data healthcheck
In this blog post we look at how the JD Edwards data archiving solution Purge-it! gives your JD Edwards system a health check.
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30 features of Data Archiving with Purge-it
Discover the top 30 features of data archiving with Purge-it that make it a truly easy product to implement.
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What is JD Edwards Archiving as a Service?
You're no doubt familiar with the as a Service model. Discover the business benefits of opting for Archiving as a Service for your JD Edwards E1 data archiving needs in this blog post.
Read more >How to measure the success of Data Archiving (Part 2)
Marek Case Study
Project background:
- 20 years of data impacting system performance
- Slowing JD Edwards noticeably
- Reports were taking longer to complete
- Negatively affecting the User Experience (UX)
Next steps:
- Find a solution to speed up JD Edwards
- Archive the data that was no longer needed
Results of the archiving project:
- Marek's Payroll run times improved by an average 49%
- JD Edwars system got faster. Marek’s CFO runs a large report annually. Following archiving the report completes in a matter of seconds.
La-Z-Boy Case Study
Project background:
- Large data volumes
- Continuous background re-indexing
- Many index jobs never finishing
- F0911 - General Ledger contained 1.5 billion rows
- 1.25 million rows being added daily
- Index maintenance jobs forced to terminate before completion during monthly database maintenance
Next steps:
- Rethinking why we keep data (not how we keep data)
- Production data to be retained when it is relevant and essential to the business process
- When data is no longer relevant, archive or purge
- Automate the process
- Approach the data systematically
Results of the archiving project:
- Fully automated archiving - "set in and forget it"
- 10 days of data removed per 1 day of data added
- Processing spread across the whole data set
- Re-indexing sped up (previously F0911 had never finished. Always self terminated after 30 days)
- 97% reduction in report run-time
Prior to archiving
- run the query once every quarter, because of length of time it takes
- heavy resistance amongst users to archiving
Post archiving
- run the query whenever needed, because it only takes 25 minutes!
- users requesting archiving after experiencing increase in performance
- enabling more frequent and efficient management in the business
- time previously absorbed by troubleshooting issues (in the business and in IT) is now used to add value to business prcesses.
