Learn how you can improve the data quality and throughput achieved by ERP data mining processes.
The overall value of ERP is often driven by the density of a platform’s data stream. This means that the more valuable data you pass across a given channel, the better your results will be over time. However, volume alone is not necessarily an advantage unless the multiplicity of those elements are analysed and optimized from the outset. This is where ‘real’ ERP data mining efficiency comes to the fore.
1. Increased automated device utilization
Modern ERP systems are a far cry from the legacy configurations that primarily depended on flat UI’s at the front end, kludged to RDBMS platforms at the back-end. Thankfully, time heals nearly every wound, and numerous innovations have been applied to ERP platforms including the advent of ‘smarter’ base code languages, thinner and more sophisticated data management platforms, and today’s big dog, an intrinsic ability to integrate ERP data mining with a host of data capture devices.
Consequently, ERP device utilization has spiked, and now applies across a host of ways to push/pull data, ranging from the customer point-of-sale level all the way down to the back-office financial ledger. Some of these innovations include; near-field communications systems (NFC), vision augmentation devices directly involved within the active manufacturing floor, mobility-adept sales and purchase devices, and applied active/passive RFID tags that drive elemental cost and sales data to both inventory/shipping and financial management levels in real-time.
2. Optimize real-time data management
At the heart of the matter, the value of ERP data mining depends on the nuts and bolts quality assurance of individual records. A single bad record configuration, multiplied by any exponential record count will begin to snowball. If enough of these snowballs begin to roll within an overall ERP data mass a platform can lose valuable data when the system needs it most.
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Given the nature of today’s synchronized multi-point data mining systems, data optimization must also apply end-to-end. This means that all records driven across the entirety of an enterprise’s ERP supply-chain must be validated, managed and audited on a regular basis in order to ensure complete record compliance in real-time. Nevertheless, once this awareness becomes intrinsic to an enterprise’s ERP culture, this characteristic quickly translates from being nothing more than an overly large, but generally passive business value, to an active universal information element that can be leveraged to produce commercial advantage time after time.
3. Optimized synchronization of reporting systems
As suggested in the previous section, synchronized, multi-site, ERP data mining offers numerous ways to derive value from enterprise operations. However, while the aforementioned values are generally obvious and usually immediately appreciated, the reporting level is where revenue ends up on the bottom line. Consequently, optimized end-to-end reporting systems must exist throughout the entire business, otherwise, at some point, a critical senior manager or line operator will ‘miss the memo’, causing lost time at best, and lost revenue at worst.