The Business benefits of Information Technology to enterprises were mostly derived from the transactional capabilities of relational databases. Business Intelligence and Data-warehousing technologies which provided additional business value were also dependent on RDBMS as their data sources. The Last 5 years, Machine Learning has emerged as an additional technological component which can provide significant value addition to Relational Database. The First wave Machine Learning tools were able to extracted business insights like better trend predictions and data-analytics from the dormant RDBMS datasets. The Second Wave Machine Learning tools were able to extend the ability to capture, storage and process images, sounds and other alternate datasets in addition to the textual data in RDBMS.
ML-MDM is a new generation of application of Machine Learning technology over RDBMS datasets. ML-MDM tools are helping business leverage the meta-data such as material-codes, customer information etc. ML-MDM has emerged primarily to address the data-quality issues which not only affect the ML applications but also the daily business operations as well. By extracting insights and patterns from the meta-data elements, ML-MDM tools today are providing value to many other enterprise use-cases such as Inventory and price optimization Market Intelligence, operational risk management through repair analytics, Connected Worker Solutions For Health And Safety etc.
This document provides an overview of the evolution of ML-MDM technologies and the related emerging trends, along with the market potential for these technologies.
Evolution of ML-MDM
The Mismatch in the material-codes in ERP systems and their physical counterparts has been a recurring problem faced by every large and medium enterprise for the past decade. The initial solution has been manual reconciliation through annual audits to synchronize the IT and real-inventories. This process for subsequently outsourced to MDM specialist organizations. The MDM external service organizations have been applying machine learning technologies to improve their productivity.
Current MDM external service organization are providing even workflow integration which removes the duplication of material-codes and ensure that the material-code mismatches are resolved in near-real-time.
ML-MDM solutions are now extending the application of their ML technologies to create value addition in several related use-cases. Inventory Optimization, price optimization, market intelligence, equipment repair analytics for even Plants as a whole, equipment safety, worker health and safety etc. through integration of multiple data-sets. The integration of CAD/CAM 3d models’ metadata into the transaction datasets of captured in RDBMS is emerging as a significant new trend called as Digital Twins. Digital Twins are expected to be a whole new wave of technology trend which would positively impact the enterprises in the coming decade. Enterprise Architecture tools which were the earlier generation of visualization and meta-data management of enterprises are also expected to merged into the digital-twin platforms.
Business Value of ML-MDM technologies
- Inventory and price optimization
The primary value of ML-MDMs is to ensure that there are no redundant procurement of materials and equipment spares due to inventory-E RP system mismatches. The secondary value of ML-MDMs is to provide pricing information of all the equivalent and near-equivalents of a material or equipment through similar-search, and the help the procurement teams buy the right products at right price, leading to price optimization.
- Market Intelligence
MDM tools can leverage web-crawling technologies to auto-fetch the pricing information of all the materials and Equipments of an enterprise, and also keep the pricing databases constantly updated. Analytics can then leverage these datasets to gain the complete market-intelligence on the procurement and supply-chain side for the enterprise.
- Operational risk management through repair analytics
ML-MDM tools are leveraging the operational log information of various Equipments and their parts directly through their digital interfaces, and then integrated them with additional meta-data including equipment bill-of-materials, CAD/CAM models, Production Floor Plans etc. Repair Analytics leveraging predictive analytics are proving to be more accurate when integrated with the metadata-sets rather than the stand-alone equipment based predictions. This new generation Repair Analytics tools are becoming integral value-adds to the operational risk managements put in place.
- Connected Worker Solutions for Health And Safety
In addition to the equipment health and safety, worker safety factors are also being integrated into the new generation ML-MDM based digital-twin platforms. By integrating the mobile devices of the workers with the systems, the operational and safety risks are sent as real-time alerts to the connected workers to improve their safety.
ML-MDM and related Market Opportunities
As ML-MDM is going to be core information technology for both Large and Medium Enterprises, the user-base is expected to very large and diverse. As the technology is also just emerging, innovative vendors will also have a scope to be niche players in specialized tasks. The MDM market will be have two essential player categories, which are MDM tool vendors and MDM external service providers.
For the MDM tool market per se, According to a new market research report “Master Data Management Market by Component (Solutions & Services), Data Type, Deployment Type (Cloud & On-Premises), Organization Size (SMEs & Large Enterprises), Vertical (BFSI, Retail, Manufacturing, Healthcare), and Region – Global Forecast to 2023”, published by MarketsandMarkets™, the Master Data Management Market size is projected to grow from USD 9.5 billion in 2018 to USD 22.0 billion by 2023, at a CAGR of 18.3% from 2018 to 2023.
MDM external service providers (ESPs) provide strategic, tactical and operational support to organizations engaging in MDM programs. These services include the formulation of information strategies, business cases, roadmaps, business and technical metrics, governance organizations and processes, organizational recommendations, information life cycle discovery, and project planning. These services may also be used to assist in the selection, installation and configuration of MDM software.
Master data management initiatives remain a complex undertaking that require business and IT to collaborate in order to ensure uniformity, accuracy, consistency, and accountability of an enterprise’s master data assets. Enterprises often need external strategic, tactical and operational support to make the most of master data initiatives. They also require partners with deep business knowledge and coverage of essential data strategy and data management skills.