An Introduction to Next Generation Master Data Management (MDM)
Master data management is the framework of processes and technologies aimed at creating and maintaining an authoritative, reliable, sustainable, accurate, timely and secure environment. This environment represents a single version of truth as an accepted system of records across a diverse set of systems, business units, and user communities.
Although MDM is not new, there has been a recent spike in interest in developing MDM solutions. This is because of strategic and tactical needs of organizations across a wide spectrum of industries. Some of the key drivers of this trend are compliance regulations like GPDR, Sarbanes-Oxley Act, and HIPAA.
Master Data Management is also enabling organizations to better focus on customer centricity initiatives and gain better insight into customer goals, demands, ability and propensity to request additional products and services. When executed correctly, this can increase cross-sell and up-sell of revenue opportunities and improve overall customer experience.
Some of the key technical challenges that occur during an MDM implementation can include:
- Data governance and ability to measure and resolve data quality issues
- Creating and maintaining organization-wide consistent data definitions
- Scalability challenges that require MDM solutions to deal with volumes and complexity, specifically with increased use of “big data” including unstructured data like mobile, social media
- The need to implement process controls to support audit and compliance reporting
MDM solutions and vendor products are continuously extending capabilities across various feature sets with introduction and integration of new technology and advance data quality and matching solutions. Below are a few specific core functionalities that have continuously evolved over time to meet today’s business requirements and reduce implementation risks.
MDM business value evolving from integration model to analytical model
Many customers tightly link their MDM initiatives to real-time customer engagement (360 view) and business processes optimization. These use cases depend on a wide array of attributes and metadata that provide context for personalization, logistics, and preventative maintenance. In the past, a hub accounted for only a few hundred data elements at most.
Today, customers need solutions that can support thousands of data elements for one domain and tens of thousands for a multi-domain hub. Some data driven use cases are:
- Contextual information based importance like Google Now showing upcoming trips, reporting destination weather, predictive analytics and recommendations
- Built-in capability of MDM that correlates entities into 360 views for open tickets, orders, and friends’ reference
- Behavioral, preference, permission, security, identity, location, and time are all maintained and connected in the graph and represented to data consumers within a business context. For example, behavior patterns can define the customer domain instead of focusing only on identity. This creates relevance, agility, and flexibility to shape master data to any business service.
Architecture considerations for the next generation of data sources: (Big Data, Social), Cloud and other key technology trends like graph database
MDM tools like Informatica, Reltio, and Pitney Bowes use a graph database to collect and link master data with additional attributes and metadata. Integrating across channel internal, external, mobile, and unstructured sources with relative ease is a key consideration where graph database adoption is critical.
Additionally, data models are more dimensional and the data levels are deeper. To accommodate this added complexity and sophistication of the organization’s needs, customer references tend to prefer contextual and analytical MDM solutions over traditional MDM tools that have relational database as a part of its architecture.
Solutions that combine MDM capabilities with analytics are powered by a graph database, machine learning, big data, and analytical visualization. These solutions translate master data directly into insight. Visualizations of data patterns show customer connections and preferences. Machine learning provides insights simply by understanding data linkage. Some example scenarios include product recommendations, identity and fraud analysis, mergers and acquisitions reconciliation. Machine learning adopts to dynamic and constant evolving data, whereas relational databases are slower in terms of interconnected data.
Specific tools are needed to provide the ability to build advanced match and merge processes algorithms. This is especially true for social analytics, which is among the most widely applied use case in big data. The biggest challenge with social analytics is successfully identifying the social profiles of an organization’s existing customers, mainly because multiple results can be returned when searching for common names. Also, many people use aliases rather than real names in their social profiles, making it even more difficult to identify these profiles.
A proactive data governance process
For proper data governance, you need an MDM tool that can streamline and automate stewardship processes, removing the manual processes that have overwhelmed stewards and bottlenecked data use in the past. It’s also important to implement advanced data governance policies. These involve rules around not only data usage, ownership, etc. but also around big data-specific data governance and data stewardship.
Two other factors necessary in data governance include ensuring external data does not affect the integrity of the internal data and ensuring the capability to manage escalation paths for data conflicts. For security reasons, you should also make it easy to define and manage the enforcement of privacy policies.
Reltio, Informatica, SAP, IBM, And Pitney Bowes Lead the Pack (Forrester)
These leaders have demonstrated extensive MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.
External reference data providers and their services will play a prominent role in MDM implementations for years to come. Companies such as Dun & Bradstreet, Acxiom, Lexus-Nexus are a few examples of these data providers. They are key in providing key functionality such as data cleansing, rationalization, enrichment, and matching.
Business demands and vendor consolidation will eventually result in the availability of holistic MDM solutions that will include sophisticated data quality components, flexible rule engines, metadata repositories and compliance & audit monitoring capabilities in componentized service based product suites.
At Intersys, our large group of highly skilled consultants are exceptionally qualified in many different types of MDM tools and use cases. With our expertise, we can implement or support all of your Maser Data Management requirements and help your organization on its journey towards digital transformation.