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Introduction
Today's Customer MDM reflects an evolution over time. Tomorrow's Customer MDM represents a seed change. Agile decisioning will be at the core of every modern enterprise. Customer MDM is the key to unlocking agile decisioning.
Building Customer MDM as a process for the edge
Customer MDM will be the crucial lynchpin to agile, rapid decisions across the enterprise, responding to real-time signals and events through AI-driven identity resolution. The solutions to drive MDM – as a process, not a product – to the edge are buildable today. With AI-driven identity resolution, edge computing/decision rules engine, and continuous access to a virtualized data-as-a-service marketplace consisting of first and third-party data on demand, we can start architecting tomorrow's solutions today. However, we need to be careful to make sure the culture and processes of the enterprise are brought along as well.
Signal-driven responses and the rise of data marketplaces
In tomorrow's world, the most critical attribute of the Customer MDM utility will rest within Identity Resolution and how it unlocks agile decisioning. Agile decisioning requires a sophisticated signal and response approach to enterprise solutions, with participants creating, consuming, and responding to a set of known or new enterprise signals and events. Modern MDM solutions actively participate in an enterprise's signal/event infrastructure. They must be considered an enterprise process, not a specific software product or architectural component. A robust, flexible MDM solution consumes, responds to, and generates event signals with different signal response modes (e.g., a signal to pull existing customer profile information in response to a credit card application may require a real-time response to satisfy business requirements, while creation and processing of the addition of new credit card account information to a customer profile may be able to be processed with less stringent requirements). MDM responds to enterprise events by providing unified services related to existing and derived customer information.
But tomorrow's solution will continue after identity resolution. Modern MDM solutions will be bridged with a data marketplace – owned or leveraged – consisting of first and third-party data aligned to flexible, multi-dimensional customer information in the form of profile entities that provide varying levels of party information that is progressively collected, enriched, and validated over time. Information can range from typical customer information collected as part of a company's formal business interactions (e.g., a customer opens a checking account) to more random temporary data (e.g., a customer used his credit card in Austin, TX on Tues). The emerging modern Customer MDM solutions will focus on collecting as much information about an individual as possible – based on the individual, the moment the signal is being generated, and all entity relationships known at the time. The data store must be flexible in structure with data in a consumable fashion and be able to provide that information in different formats, either in response to specific event signals or in creating secondary event signals.
Transforming Customer MDM with real-time identity resolution
To accurately respond to event signals, modern Customer MDM solutions must also employ highly accurate and flexible Identity resolution services tied to the data stores that when come together, are fed into a "Rules Engine" that manages the response to the signals/events based on the identity and profile of both the individual and the moment in which they exist. Customer MDM solutions must be able to take varying sets of information from signals/events and associate that information with a specific individual and their associated profile information. This is much more dynamic than historical MDM software processes focused on background processing that limited Identity Resolution to existing legacy customer record matching and collapsing. Advanced identity resolution is a strategic asset that uses the customer profile for identity resolution rather than just a flat golden record. It generates a 360-degree profile in real time that provides an entity-centric view of each customer and their relationships.
The identity resolution process must be automated (for low latency/real-time response) and involves several steps, such as standardization, normalization, validation, enhancements, and data enrichment. Input information from different sources is processed to form feature sets that define broader entities and generate additional or inferred features about the entity. These features are used to identify direct and indirect relationships of the entity and establish a contextual understanding. A framework of blocking-matching-clustering is then used to identify candidate profiles for matching from a pool of resolved entities and match and link the entity to the closest cluster. The matching process uses pre-built advanced algorithms and open-source reference libraries for high-precision feature matching. Clustering generates groups of similar and linked entities, thus identifying duplicates/matches across sparse and heterogeneous user profiles.
Building dynamic customer identities with Machine Learning
Machine learning algorithms leverage deterministic and probabilistic methods to create the entity profile and resolve identities progressively. The process successfully handles typographic, informational, and temporal variations, improving its accuracy and completeness with time by self-correcting and reducing false positives and negatives.
This resolution framework is flexible and continuously procures the latest updates of the entity's profile from its data sources and applications, which are added to existing resolved profiles. The process assigns an enterprise-wide persistent ID to each customer profile and links all identifiers across different LOBs as a keyring. This incremental framework also allows additional third-party data sources to enrich the profiles further with information about beneficial owners, hierarchies, and risk profiles. The enriched contextual awareness and complex relationship information added to primary demographic data can improve the accuracy and completeness of identity matching.
Conclusion
The primary mission of modern Customer MDM solutions is to bring contextualized customer information as close to decision-making as possible. To accomplish this, it must be able to respond to events, resolve associated identities, provide contextual customer profile information, and generate its own associated downstream events. The more master data solutions can provide core, extended, and contextual data about a party in real time, the more effective enterprise responses and actions will be.
