Modernizing MDM for a Data-Driven Business

Over the past few decades, collecting, storing, analyzing, distributing, and using data has changed immensely. The Internet of Things (IoT) continues to grow, technology is evolving, and more companies are using Big Data and cloud applications than ever before. As a result, a company’s ability to use data, new applications, and data sources determines its relevance. Moreover, a critical aspect of a modernized data strategy will be practicing new master data management (MDM) capabilities.

What is the Significance of Master Data Management (MDM) modernization?

Previously, MDM was a back-end hub in the “hub and spoke” unit, where the utility was highly interconnected to integration abilities. However, MDM is progressively used to help deliver organizational value across the customer journey. A few aspects that explain the need for data modernization are stated below:

• Offer customer knowledge for white space evaluation. Thus, enabling personalized engagement and up-sell or cross-sells opportunities.
  
  
• Allow quote-to-cash processes through governed product master data to meet the new product or feature introduction needs.
  
  
• Inform discounting and pricing decisions throughout quoting and recommencement. This depends upon user lifetime value, current adoption/usage, and previous buying capabilities across mediums.
  
  
• A holistic view of customer engagement and NPS scores beyond products or services provided through sentimental social media evaluation. It is offered to user success teams to interact with users posing the risk of attrition during renewals.

MDM Platform for Easy Back-end Mechanism

Reaping the significance of modernized MDM platform to allow back-end mechanisms:

• Allowing trusted financial and management reporting by offering the company’s functions/units the capability to develop, enrich, and enhance financial master data and hierarchies such as cost centers, charts of accounts, etc.
  
  
• Offering a “clean room” feature through multi-tenant cloud MDM with Big Data storage for user overlap analysis and other analytics preceding Mergers and Acquisitions (M&A).
  
  
• Assisting complex regulatory guidelines, which continue to grow. This includes denied party screening, global trade compliance, GDPR, etc.
  
  
• Offering the back-office with governed product MDM to help product profitability reporting.
  
  
• Equip a standard template of reference and master data mappings to assist with main shifting projects.

Elements of a Modern MDM Platform

Despite crucial investments in MDM resources and technology, several organizations need help understanding its benefits. This is due to factors like data explosion, which causes productivity problems. or business needs that evolve in a digital world. There are specific functions that offer this required functionality and quality of an MDM platform as modern:

1. Visualization and smart search: Front-office has become data-centric and is immensely engaged in data stewardship functions like complex product taxonomies and managing user hierarchies. Businesses can adopt these tasks through highly configurable and intuitive UI.
   
   
2. Microservices architecture: With the increasing variety and number of business capabilities and data domains supported by MDM, the MDM case built on a microservices architecture becomes stronger.
   
   
3. Hybrid/cloud environment enablement: Hybrid MDM solutions offer interoperability for cloud platforms in the value chain and on-prem applications. It allows cloud solutions users to effectively leverage their data and data management functionalities. Furthermore, the transition to the cloud for on-prem becomes easier for businesses.
   
   
4. Business and governance process orchestration: Businesspeople need to collaborate efficiently across the company to create and manage reference and master data. This data empowers key business processes like vendor/user compliance, pricing, and marketing campaigns. Modern MDM functions such as chat features, mass maintenance, highly customizable workflows, and ML-allows guided workflows that offer users potential actions to enhance productivity.
   
   
5. Machine learning and AI: Automating traditional data management tasks helps IT and business units to gain operational efficiency and reduce operational expenses. ML-based automated stewardship decreases data management costs and efforts by constantly emulating human data steward actions.
   
   
6. Richer or deeper relationships: Marketing analysts and user-facing personnel often explore links and get hold of a user’s product portfolio for mass personalization. MDM should manage high volumes on a Big Data platform to meet organizational demands and influence NoSQL to offer richer or deeper network relationship views for intuitive exploration.
   
   

Master Data Management (MDM) and the Cloud

While commencing the journey to the cloud, companies need to look for data distributed across business units, data feeds, applications, regions, and data lakes. However, it’s not always needful to overhaul all data storage and move it to the cloud. Hence, companies prefer a hybrid approach with some data and applications hosted on-prem and others existing in the cloud.

Integrating MDM with a data lake and cloud techniques drives several valuable outcomes for business users and data professionals. This includes the ability to:

• Understanding user’s patterns and needs instantly. Through a graph-derived processing engine to relate and match data which provides a more detailed view of users and other business-critical entities.
  
  
• Enhance self-service empowerment, providing access to reliable data without relying on IT.
  
  
• Increase extensibility to new data sources, designing custom metadata scanners for easy and fast access to customer data sources, like bespoke applications and legacy databases.
  
  
• Increase extensibility to new applications, assisting a metadata repository for easy access to custom web portals, reports, and workflow-derived systems.
  
  
• Allowing smarter data discovery. By automation identification and classification of business assets.
  
  
• Rapidly achieving customer 360 views. Decreasing TCO by leveraging cloud functionalities
  .
• Offer automatic concurrency scaling; by adhering to fluctuating data volume and computing needs by inbound and outbound interfaces.
  
  

Critical Considerations for MDM Modernization

To ensure that the MDM solution offers advanced capabilities, some points to consider throughout modernizing efforts include:

• Interpret a phased and pragmatic MDM roadmap that provides incremental business value by shifting the solution up the maturity curve. For instance, business processes enabled, DQ/integration capabilities, domains mastered, and others.
  
  
• Leveraging operating dashboards and metrics to assess data quality trends, policy compliance levels, adherence to SLAs, and exception remediation rates. This is done to fix master data specific to transaction processing fallouts.
  
  
• Verify that the MDM management has certifications that include cybersecurity, data-centric, and application controls to safeguard your data at rest and in motion.
  
  
• Aim at addressing master data governance roles and business process reforms needed to confirm the MDM investments to provide expected value once the solution is operational.
  
  
• Evaluate if the MDM solution collaborates seamlessly with cloud and enterprise applications, along with metadata management, external data providers, data governance, and reference data management.
  
  

Conclusion

“Modernizing MDM for a Data-Driven Business” is a valuable resource for organizations looking to modernize their MDM practices and improve their data-driven decision-making capabilities.

WRITTEN BY

Anjali Goyal

Anjali Goyal is a content writer at TechEela. She helps businesses increase their online presence with optimized and engaging content. Her service includes blog writing, technical writing, and digital marketing.

Follow

0