As more companies deploy master data management solutions, there has been an increasing demand for even more value-added capabilities from master data. One of the most significant is the demand for MDM solutions to manage relationships between parties including individuals, individuals and households, individuals and corporate entities, informal groups and organizations. Understanding relationships between parties and products as well as product hierarchies is critical for enterprises. It is quite common that MDM begins with one or a few master entities and then evolves a) to include a broader set of entities and b) to increasingly focus on relationships.

Businesses are looking for ways to understand family and partnership relationships to correctly determine who their best customers are, how to estimate the risk-adjusted values of customer relationships, and what the organization should offer to attract new customers and retain their best existing customers. Government agencies want to gain a deeper understanding of relationships between suspects and criminal organizations to prevent terrorist threats, money laundering and other criminal activities or unwanted events.

Many organizations already devote resources to understanding relationships but are oftentimes doing so through manual means with limited efficiency, scale or consistency. MDM technologies that are capable of recognizing relationships and hierarchies in data can be leveraged by these organizations to provide significantly better ways of understanding relationships.

Different types of organizations can benefit from deploying MDM technologies to understand relationships. The methods and approaches companies can take as they move toward a more customer- and relationship-centric business model are described in the following sections.

Understanding Relationships Benefits Organizations

Market evolution and regulatory requirements mandate that organizations need a better understanding of party relationships and hierarchies. The order spans horizontally across all industry verticals, government agencies and organizations.

In the banking industry, the Basel II committee, recommends standards and best practices in banking, and advises that institutions look behind corporations, partnerships, foundations and other organizations at the principals - and their family members and partners - to identify who has control over businesses and their assets. For example, when a company is a subsidiary of another company, the principals at the parent company may be the individuals that need to be identified.

Businesses want to get a greater share of each customer’s wallet, enable more and better cross-sell and up-sell opportunities and increase customer retention rates. However, they want to do so while gaining better control and limiting, or even eliminating, relationships that are risky or not cost-effective. Organizations may use multiple names to do business with a company. Understanding relationships would help a company identify all the names a single organization is using, which would reduce risk exposure by better controlling how much credit is being extended to a single entity (or to closely related individuals or individuals and organizations). For example, a small business owner could lease or purchase a computer under his or her own name and then lease or purchase another one under his/her company’s name. It may be important for the computer company to understand that this is the same person, especially if that same person applies for credit multiple times under different umbrellas, and the company issues credit multiple times to that same individual, creating a high level of risk exposure.

Energy companies and other companies that provide services paid for at the end of the month face similar risk from customers they call “spinners.” Spinners are notoriously bad customers who, order service under different names and never pay for it. For example, a customer of an electric company first puts service in a husband’s name, then a wife’s and then other household members’ names, and they don’t pay again and again. The same situation could occur with a company that uses different names. With spinners, it is extremely important to know how people are related, and how people and organizations are related.

For hospitality and travel companies that operate loyalty programs, it is important understand the behavior of individual customers and also their spouses, partners and other family members. This knowledge will help companies develop better and more attractive offers to loyalty club members. It is not uncommon for people to buy hospitality and travel services based on family preferences rather than individual preferences, so the more information companies have about how people are related will ultimately improve the bottom line.

In health care, it is obvious that understanding how patients are related would be helpful for becoming more familiar genetic diseases. However, knowledge of relationships between patients and providers is also critical in order to support online portals and self-service applications. These solutions require data visibility, security and eligibility that can be effective only if the system “understands” individuals on the provider side and which offices are authorized to have access to a patient’s health record, type of this access, etc.  By providing physicians with an information portal that has complete patient detail, including how physicians are related to patients, how physicians are related to hospitals, how physicians are related to their offices, and what kind of health plans they offer in each office, hospitals make it more convenient for physicians to interact with them, which ultimately results in more physician referrals and more revenue.

Relationship data is also critical for government agencies that are looking for criminals. For example, when the Homeland Security agency is tracking terrorists or the police are looking for organized crime suspects, they need to see how people are related and who is connected to whom. Often, there are members of organized crime rings and terrorist groups that operate behind some kind of fence, so understanding relationships - not only between people, but also between people and different kinds of associations, funds and organizations - can be critical.

It is clear that the need to understand relationships spans across all types of organizations and can bring benefits in numerous ways. But, how can organizations successfully implement a program that enables them to better understand relationships?

Extending MDM to Relationships

Companies that want to understand relationships need to migrate from an account-centric or product-centric view of the enterprise to a customer-centric or relationship-centric view.

At a high level, there are three ways to build entity relationships and hierarchies.

  1. Use an external trusted source of relationships (or multiple trusted sources) and build internal relationships and hierarchies by comparing in-house data with the trusted sources.
  2. Create and set rules that will be used by the systems to automatically infer entity relationships. These rules can be based on common attribute values, (e.g., people sharing the same home address are defined as a household). The attributes can be matched exactly or probabilistically by applying the same probabilistic methods that were used earlier for entity resolution.
  3. Develop relationship matches and links manually using graphical interfaces that enable relationship building.

These methods can be used individually or combined to achieve varying levels of success in terms of identifying and understanding symmetric and asymmetric, one-to-many and many-to-many relationships. Each method has its unique benefits, areas of application and limitations. In full-scale MDM implementations, enterprises can benefit from using all three methods to create a comprehensive view of relationships.
If an organization is using an external trusted source or sources, it relies on that source’s data and matches its internal records against the external data to help establish which of its records are related. This approach is frequently used to establish and maintain the hierarchies of customer or product data, which are critical for managing credit risk, marketing and territory alignment. Structurally, a single root with an unlimited number of layers below represents each hierarchy. Applications typically display and manage these hierarchies in a tree view. For global MDM implementations, it is usually important to build a solution with an open architecture that is capable of supporting data from providers in many countries and multiple types of decision tree logic. Decision tree logic “decides” which trusted external source should be used first to augment a specific internal record.

When rules are created, they can determine which records are related and how, and then they can be used by matching algorithms to infer relationships and validate relationship rules. These rules can be simple, or they can be quite complex and include fuzzy and probabilistic matching. For example, a data hub can define rules for identifying people who work for the same employer, belong to the same group or are part of the same household. Or, rules can be created that specify that a company is allowed to have only one parent, or a patient must be associated with at least one provider. When a validation rule is violated, an alert is generated to notify the system that clerical review of a data exception is required. For example, if a company was found to have two parent companies, this would violate an established rule, which would result in the rules engine generating a data exception alert and sending the alert to a data stewardship queue for clerical review and processing.

Human intervention is often necessary to resolve rules violations and other data anomalies. However, in other instances, organizations cannot use external sources or create specific rules to establish relationships and need to rely on human reviewers and manual methods to determine how data is related and the links between data. It is important that data stewardship technologies and manual processes are seamlessly integrated with MDM technologies in organizations that want to establish a relationship-centric view of the enterprise.

A sound relationship management strategy that combines all three approaches enables enterprises to build comprehensive MDM solutions that appropriately balance the use of advanced algorithms with business user and data stewardship input.

The benefits that understanding relationships provide are significant and span across industry verticals, government agencies and other organizations. A sound approach to relationship management is a key part of any mature MDM solution and the transition to a customer-centric enterprise.

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