Prosol: Embeddable Workflow Process For Continuous Data Quality Management
Every year, even Large Enterprise IT teams face the Master Data Quality Decay Problem. The tried and proven solution in the industry has been hiring Consulting firms to curate and improve the Master Data Quality. The underlying assumption of the IT Management teams is that the new, more-standardized descriptions will help the employees better manage the inventory data which will in turn contribute to cost cutting. However after repeated iterations year after year, Most Enterprises realize that end-of-year data quality improvement exercises is only a temporary fix and not a long term solution.
Quality data is the foundation to implementing master data governance. However, over the progress of time, the quality of data deteriorates with more and more master data gets created. While the Data in the Enterprise IT can be quickly enforced with a Cost-Effective Annual Data Cleaning, the cost of getting rid of the excess inventory is very high. Forward looking Enterprises have identified this and have been exploring viable alternatives for the annual Data Cleansing approach.
Data Cleansing Tools have been using a probablistic product description matching algorithms to identify duplicate product codes and eradicate them. However, with the advent of Deep Learning based Machine Learning and Natural Language Processing algorithms pioneered by Google and DeepMind, New generation Machine Learning algorithms like Word2Vec, Long-Short Term Memory (LSTM), Sequence-to-Sequence Models etc. are being leveraged for developing software tools that match product specifications based “Similarity Measures” instead of probabilities and rankings.
CodaSol have developed Machine Learning Catalogs from the vast datasets it has gathered while working in data-cleansing projects over the period of years. Prosol adds an additional enterprise specific layer over the ProPedia Machine Learning Product Catalogs based on Industry Standard Classifications like UNSPSC
Data Cleansing Tools have been using a probablistic product description matching algorithms to identify duplicate product codes and eradicate them. However, with the advent of Deep Learning based Machine Learning and Natural Language Processing algorithms pioneered by Google and DeepMind, New generation Machine Learning algorithms like Word2Vec, Long-Short Term Memory (LSTM), Sequence-to-Sequence Models etc. are being leveraged for developing software tools that match product specifications based “Similarity Measures” instead of probabilities and rankings.
CodaSol have developed Machine Learning Catalogs from the vast datasets it has gathered while working in data-cleansing projects over the period of years. Prosol adds an additional enterprise specific layer over the ProPedia Machine Learning Product Catalogs based on Industry Standard Classifications like UNSPSC
Prosol allows Enterprises to adopt their own classifications and attribute ranking mechanisms. Prosol provides a custom template engine which can help enterprise generate Material codes that adhere to their policies while still leveraging Industry standards like UNSPSC as the underlying foundation. Prosol allows multiple templates to be used for different material categories as well.
By Leveraging Prosol, our customers ensure that new item descriptions, modifications, or deletions are always in synch with globally standards.
When integrated with IT platforms including ERP, Inventory Management, EAM (SAP PM), and CMMS (Maximo) solutions, our governance solution ensures that data quality does not decay and remain in-tact in perpetuity. This real-time Master Data Management provides our customers with the ability to intelligently govern and synchronize materials data across multiple applications and systems.
Prosol’s Efficient Product Coding Process can improve understanding of OEMs and OPMs for Products
By leveraging Propedia , ML based Master Data Management integrated into it, Prosol can help in systemizing Products-Tools Relationships through Bill-Of-Materials data.
Procurement Optimization by avoiding intentional and unintentional excessive purchase
Leveraging OEM and OPM Vendors product similarities comparision to decide on supplier can result in significiant input-costs reduction as OPM vendors can be very cost-effective than OEM suppliers in most cases.
B6-2nd Floor, Gateway Office Parks–SEZ Campus, 16, GST Road, Perungalathur, Chennai - 600063, Tamil Nadu, India
Copyright © 2024 CODA. Designed by digital wordings
