Why IBM for master data management
IBM is a leader in master data management solutions – helping organizations gain a single view of data and meet growth, revenue-generation & cost-reduction goals.
Improve operational business processes, big data and analytics via trusted views from a complete, flexible, and proven MDM solution.
Help public sector organizations and commercial enterprises recognize and mitigate the incidence of fraud and threat
Integration with IBM InfoSphere Master Data Management (MDM) enables development of applications that incorporate trusted data into an enhanced 360-degree view of the customer
Works with IBM InfoSphere Master Data Management (MDM) to provide real-time matching of master records at big data volumes in hadoop
Master Data Management for Industries
Create a single view of citizens to improve service, verify benefits and prevent and detect threats and fraud
Increase wallet share, provide better customer service and reduce costs through operational efficiencies
Understand the full customer relationship and introduce new products quicker
Shift your view from policy-centric to customer-centric
Connect disparate pieces of patient, provider and member information across multiple places, into a single, actionable view
Connect disparate pieces of customer information across lines of business, service locations, billing systems and more
Master Data Management white papers
Continuing the MDM journey
The Challenge of Effective Master Data Management
Threat and Fraud Prevention with InfoSphere Identity Insight
Preventing fraud with identity & social network analysis - A guide for Bank Executives
Gartner Magic Quadrants
The MDM advantage: Creating insight from big data
MDM plays a crucial role in translating data into meaningful and useful intelligence. Together, big data and MDM can help CIOs and CMOs understand customers at an individual level and become more responsive to their needs.
Master Data Management for Big Data
Together big data and MDM can help extract insight from the increasing volume, velocity, variety, and decreasing veracity of data, in context, beyond what was previously possible.