How Hierarchies Enable Proper Customer Data Management

Editor’s Note: This post was originally published in August 2021. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Enterprise data is complex, spanning multiple data domains and representing numerous, interconnected relationships such as people and companies, individuals and households, businesses and locations, and healthcare providers and hospitals. Data hierarchies connect these disparate entities in a structured and understandable way so that organizations can use them in analytics, operations, and AI applications. 

For B2B businesses, understanding the hierarchical nature of customer relationships is essential. A typical B2C company has more customers than a B2B company, and the customer-to-account relationship is one-to-one, which makes managing customer data fairly simple. Conversely, a “single” customer in a B2B scenario can be incredibly complex; customers are composed of many locations and divisions, and can roll up to parent companies and subsidiaries. For example, the legal entity you contract with may differ from the regional office where you deliver packages or the parent company that represents the overall corporate relationship. 

The connections between healthcare providers and organizations such as hospitals or clinics—including affiliations and pathways—are critical to placing the most qualified provider in an open role. And linking individuals to a household provides insights that can reveal new cross-sell or upsell opportunities.

However, when critical business entities are trapped in data silos, relationships are obscured, making it difficult to gain a 360-degree view of information that reveals interconnected relationships and untapped opportunities. By utilizing hierarchies and enterprise knowledge graphs, organizations can gain access to trusted, real-time views across people, organizations, households, and associated items like products, orders, invoices, locations, and more—revealing deeper insights within the data and enabling teams to answer complex questions about key business entities. Let’s take a closer look at the different types of hierarchical relationships, the challenges in managing them, and the value they deliver to your business.

What is a Data Hierarchy?

A data hierarchy provides a connected view of an organization’s data that highlights the relationships among and between key business entities in order to reveal meaningful connections that surface valuable, actionable insights. Examples of common relationships include:

• B2B contacts and companies: The relationships that exist between people and organizations that show how they relate to accounts, industries, and geographies. 
• Healthcare organizations and providers: The referral pathways and affiliations that connect providers and healthcare organizations. 
• Households: The links between individuals who live under the same roof.

Common Challenges with Hierarchy Management

While hierarchies may seem like a simple way to organize critical information, it’s difficult to maintain and oversee them for several reasons.

1. Duplicate, siloed, or misentered data: Duplicate, siloed, and misentered data limits the effectiveness of hierarchies. As a result, it becomes difficult to structure the data and spot meaningful connections within it. 
2. Missing, incomplete, or outdated data: When critical information is missing, incomplete, or outdated, it negatively impacts the effectiveness of hierarchies. For example, if fields such as legal entity or address are obsolete or missing, this type of bad data might lead to two entities or branches being treated as separate when they are, in fact, the same. 
3. Changing corporate relationships: It is common for company names and corporate relationships to change, especially when the company is part of a merger or acquisition. However, it can be difficult to keep up with this constantly shifting corporate information, causing hierarchies to quickly become inaccurate or obsolete as the name of a customer and its subsidiaries, parent, or locations change. 

Mastering Enterprise Data for Hierarchies

Tamr’s AI-native master data management (MDM) offers a robust approach to defining, implementing, and governing hierarchies across multiple domains. Key features include:

• Unique IDs: Data clustering (deduplication, ability to cluster at various levels, and creation of a single identifier): Knowing that different types of hierarchies may address different business needs, clustering at different levels or based on different criteria is essential for generating effective hierarchies. Additionally, unifying duplicate records and giving each record a unique identifier allows for a single and complete source of data. Together, these form complete and flexible hierarchies.
• Enterprise knowledge graphs (real-time, connected views across key business entities): By using AI models to unify data and connect the dots within and across an organization’s most important business entities, Tamr creates enterprise knowledge graphs that uncover meaningful connections and reveal valuable, actionable insights. By connecting these graphs to AI reasoning systems, AI agents become smarter and more “human” in their reasoning abilities, making it easier for them to navigate and connect the data in siloed systems and suggest hierarchical relationships.  
• Enrichment with external data sources (ability to include data sources for accurate, up-to-date info and external guidance on hierarchy structure): Using verified, third-party data sources to enrich records provides complete and accurate information in real-time. For example, using firmographic data such as addresses and contact information alongside validated, third-party data from sources such as Dun & Bradstreet, Tamr can verify a match, and once confirmed, can update and enrich hierarchy structures and the entities within them.

What Value Do Data Hierarchies Deliver?

Data hierarchies can help your business increase revenue and growth while minimizing risk. For example, by visualizing your B2B customer data in an enterprise knowledge graph and comparing certain fields, you can identify which accounts to prioritize and contact to develop leads. Specifically, with the help of these hierarchies, you can discover more points of contact and better understand which type of relationship you have with each entity. As a result, your business can reveal cross-sell and upsell opportunities that were previously undetected. 

Additionally, data hierarchies make business processes more efficient. By putting your critical data into hierarchies, your organization can understand key relationships such as customers to accounts, individuals to households, or healthcare providers to clinics and hospitals. With these critical relationships visible via data hierarchies, organizations can more readily spot new opportunities, enhance lead routing and assignment, and limit confusion as it relates to mapping and follow-up. 

Finally, data hierarchies can also help your company make business decisions with reduced risk. With clearly defined corporate relationships, as well as complete, trustworthy account information, you can mitigate any risky business decisions.

To maximize the value of a data hierarchy, it needs to contain clean, curated, mastered data; connect relationships in an enterprise knowledge graph; and include updated and enriched information. Assuming that all of these conditions exist, hierarchies can be extremely useful and advantageous for businesses, helping them to build strong relationships, uncover untapped opportunities, and deliver the kinds of experiences customers expect.