The business impact of too much data can be far reaching:
Deterioration in system performance creeps up gradually on an organization with potentially serious consequences
Legal liabilities associated with storing data are magnified
The larger your data footprint the more costly the storage, and cloud computing power needed to process it
Slow batch job run times and back up times can begin to impact the working day
Longer upgrade times
Need for increased investment in hardware
Your JD Edwards Data Story
As a J D Edwards user, your company will have been collecting data in the normal operation of its business processes for the past five, ten, twenty or more years.
Your data consists of items or ‘sets’ that come from your everyday business processes, without which any company would not exist.
These sets of data can start out as self-contained pieces representing an individual process eg.
- A Sales Order (SO)
- A Purchase Order (PO)
- An Inventory item (INV)
- A Manufacturing Works Order (WO)
Over time, as these sets of data are processed, other sets will be created to support the processing of the item eg.
- A SO may require the purchase of a part, therefore creating a PO and linking it to the SO
- A SO may require the manufacture of an item, thus creating a WO and linking it to the SO
- An Invoice will then be raised to send to the customer which will be linked to the SO
- Financial postings will be made to the GL for the Invoice and to other parts of JD Edwards
The list grows and applies to many different business processes.
Gradually, your business moves away from having a database that consists of many separate sets of data.
Instead, your database has a structure more akin to multiple ‘strings’ of interconnected data sets.
The quantity and complexity of these interconnected and in some cases overlapping data strings build and grow. Eventually, this sizeable amount of data can start to resemble a huge deeply tangled web.
After years or decades of data collection, this may leave you wondering:
- How much disorder your data is in?
- How it is arranged?
- What action to take?
It may seem a daunting task. However, with the right tools and knowledge it is far easier and simpler than you might first imagine.
The first step is to begin to understand the nature of the data sets, and the defined relationships in JD Edwards for your data ‘web’. By starting to comprehend it you are immediately better positioned to kick off the process of unpicking your unique data tangle.
Undoubtedly, some of your data will have important value to your business in your ERP, but despite some of your data holding great value there is also likely to be a large quantity of data that will have little or no significant benefit. This will be mainly due to the type, nature and age of that data.
How to make your data leaner, cleaner and fit for purpose.
Identify the business data that is old and complete. This can be archived (and potentially purged at a later date).
Understand the relationships that data has with other data in the JD Edwards system.
Cross validate the related data.
Remove the strings of data from your JD Edwards system and move them into an archive.
Identify incomplete data and / or data with bad integrity so it can be flagged for checking and fixed if appropriate.
Find out what makes this the safest, easiest to use solution for JD Edwards archiving.
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