Master data management represents a framework of electronic technologies that helps business enterprises to execute secure data management practices. Master data encompasses information regarding customers, clients, business prospects, citizens, suppliers, products, and securities, etc. Usually situated in different locations within the electronic networks of a business, these lines of information pertain to users, internal and external data sources, offline channels, and online platforms.
The Role of Big Data in Master Data Management (MDM)
In addition, the advent of big data and cloud computing technologies is spurring deep changes in the concept of master data management systems. Consequently, such systems are undergoing re-engineering with a view to manage disparate silos and streams of information through data management initiatives.
Let us examine some of the trends in the evolving domain of such technologies –
· Multi-domain MDM
Enterprises may seek to implement multi-domain master data management strategies in a bid to defeat legacy disconnections between different work groups. However, a single product may not suffice in meeting the requirements of modern enterprises. Gartner has noted that, “for customer data, you most often need a data quality capability such as entity resolution. For product data, the more common data quality capability you need is semantic and/or text string parsing…” To meet such demands, software vendors are offering different product ranges with diverse capabilities. They are also devising integration capabilities that mesh different master data management products into an integrated system.
· Relationship Resolution
Master domain management technologies are undergoing refinements to identify and understand relationships between various entities. This is in response to demand from various regulators and regulatory frameworks that seek to unveil relationships between individuals and organizations, between informal groups and corporate networks. Consequently, software vendors are designing advanced algorithms that are integrated into master data management technologies. The impulse that animates such queries lies in governmental policies and intent to prevent and block money laundering activities, terrorist financing, and other undesirable activities.
· Data Stewardship
This implies formal processes that can identify and correct any instances of data loss or data corruption inside a corporate network. The evolution of master data management solutions will enable business entities to implement deeper levels of data stewardship across the enterprise. These actions can extend to human reviews and analysis of various data processes and their workings. In addition, dedicated data steward resources can be marshalled to focus appropriate levels of intensity in a bid to guarantee high accuracy outcomes. Consequently, software designers and vendors are creating new user interfaces that ensure top-notch data quality and data governance standards.
· Integration with Business Processes and Workflows
Software architects are working to integrate master data management systems with business rules engines, compliance requirements, company policies, and interoperability standards. Further, software makers and designers are working to integrate sentiment analysis and social media interactions with master data management systems. The aim is to improve data integrity and boost business processes while gaining operational excellence. In line with this, “vendors are increasingly addressing the centralized capacity of MDM to accommodate a variety of sources in a way that can even automate critical business functions,” according to certain industry experts.
· MDM and big data
The inherent momentum in modern business environments has necessitated the integration of master data management and big data. Recent industry estimates note that an overwhelming 94% of poll respondents felt data governance was either “important” or “essential” to big data. In line with this, an integrative approach should empower businesses to create and institute linkages that connect external data flows with existing data sources inside an enterprise. The primary challenge lies in dealing with the volumes and complexity presented by external data silos. A different problem emerges when MDM-based identity resolution tools navigate the complexities of linking external data to a company’s internal data flows. Lately, it has emerged that industry players view “MDM as a resource to drive big data analysis.”
· MDM in the Cloud
Cloud computing technologies and systems represent a game changer in modern computing paradigms. Increasingly, business enterprises are embracing the reality of data located safely and securely in the cloud. Certain aspects of master data management systems can be transferred to the cloud; however, certain outliers such as the financial services industry are effecting a pushback to such trends. This action stems from their reluctance to place sensitive customer and financial information away from their internal computer servers and networks. In response, software vendors and network designers may undertake to integrate on-premises MDM systems with software-as-a-service applications. This tactic has gained ground as a ‘hybrid data management’ technique.
In light of these trends, master data management technologies are undergoing the rigors of evolution. End-to-end data management, machine learning, data governance protocols, and digital transformation represent some of the new-age technologies that will guide the future evolution of MDM. It is high time that business enterprises create the policy frameworks that will enable such transformation.