Standard D&B

The standard Dun & Bradstreet platform is a solid foundation for entity identity resolution and corporate hierarchies, but it does not satisfy advanced business expectations without improvements.

The Enhanced D&B solution is a complete suite of D&B data enhancements, maximizing the D&B data potential, providing capabilities far beyond the standard D&B offering, enabling your business.

Known D&B data challenges

• The identity resolution algorithm relies more on older linguistic models compared to modern probabilistic and ML models, resulting in a lower yield, and higher false positives.
• Quality of D&B data and identity resolution is country dependent and inconsistent, making fine tuning of entity resolution challenging.
• The D&B services can become expensive, if the data is not properly selected and prioritized.
• The platform doesn’t adjust to client’s specific needs, e.g. specific industry or country.
• DUNS records change control can be poor at times, leading to serious undesirable hierarchy shifts.
• D&B firmographic data is legalistic, entity names and hierarchies can be misleading at times.
• D&B parenting has gaps, corporations are frequently fragmented, hampering analytics, data science and AI.
• Alternative hierarchies for business meaningful corporation/ parent hierarchies are not available, hampering focused analytics, data science and AI
• Legal data restrictions in China might hamper Chinese market insights and analytics (a China government restriction)

Enhanced D&B suite

…a solution for maximizing the value of the D&B data, resulting in better insights, analytics,  data science and AI results.

D&B entity matching

Identity resolution (D&B matching) requires striking a balance between the identity resolution yield (match rate) and the share of false positives.

The decentralized management of entity data sets by D&B and the presence of multiple (generations) of identity resolution engines make it very difficult to strike a balance. We discourage from overly customizing the match acceptance algorithm, as it could have an opposite result, and also decreases predictability of false matches.

Entity matching can be improved by implementing a Data Stewardship platform (EHP.)  A false match rate of around 5% is acceptable when finetuning the standard D&B platform.  A Data Stewardship platform, which is a part of the Enhanced D&B suite, provides tools to efficiently address the remaining false matches.  We also recommend considering third party data mastering tools, like tamr.

D&B data change control

While hierarchy updates due to the M&A (mergers & acquisitions) are critical, D&B occasionally releases major erroneous hierarchy updates.  We recommend to close monitor hierarchy changes and if an error is identified, mitigating them internally.  D&B typically addresses these changes within a month or 2 months.  Hierarchy errors have a major impact on your reporting and analytics and often impact leadership dashboards.

Parent Hierarchies

One enterprise must be represented by exactly one hierarchy (tree.)

A single hierarchy (tree) for business partner data belonging to the same company/ entity is essential.  Although D&B is one of the few sources providing business hierarchies (entity trees), D&B hierarchies have remained fragmented, legalistic, frequently changing, hampering advanced analytics, data science and AI.

The Enhanced D&B solution resolves this problem by introducing a Parent DUNS superstructure. This superstructure typically resides at and above the D&B standard GU (Global Ultimate) DUNS.  Some D&B (legal) hierarchies require a solution that works in the opposite direction, splitting standard GU (Global Ultimate) DUNS into meaningful business entities, complemented by  entity grouping functionality described above.

Alternative Hierarchies

Most business partner hierarchies can be supported by parent hierarchies described above. A small number of cases require alternative hierarchies to meaningfully represent the business views.  E.g. It might be equally important to know the total amount of business we do with the US Federal government and its key agencies, like the National Institutes of Health (NIH), Department of Defense, resulting in multiple hierarchies for related entities.

Expert Review 

A continuous review of business partner data is essential for accurate business partner identity resolution and its use in business insights.  Businesses and market are constantly changing; It is critical that your business partner data are adapting to these changes.  To stay cost efficient and to maximize the benefits, business partner records must be prioritized based on their business value and must always cover all records, new and existing.  The comprehensive review is a critical success factor, contrasting with a commonly used control of the data at the creation only (“create and forget”.)

A modern, easy-to-use Data Stewardship platform is essential for supporting expert reviews – (enterprise harmonization platform – EHP.) 

Data in EHP can be located with ease, utilizing probabilistic search tools, allows for business value prioritization, incorporates Machine Learning (ML) and Artificial Intelligence (AI.)  This unlocks additional use cases like Global Trade Compliance and other ad hoc inquiries.  Human oversight and overrides are critical for the data mastering success.  The process is known as “data refinement”,

EHP Machine Learning and AI components play a critical role in suggesting records which might require a review.

Entity data in China

Chinese laws restrict certain entity information in China.  D&B must adhere to these rules, creating limited gaps in the China entity data.  We recommend a custom solution if your company is impacted by these restrictions.

Solution Benefits

Customer 360

Mastering all meaningful customer records across ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), Marketing systems, as well as records from 3rd party data enrichment platforms facilitates total customer insights across all your business domains.   Mastered data overcomes the challenges of system integrations, duplicated records and the real-world variability.

Commercial

• How much revenue does each customer generate? What are the customer revenue trends?
• What opportunities are we currently pursuing with a customer?
• What is our total business with a customer, who is also our supplier?
• Which market industries do we sell to?  How much revenue does each market generate?  What are the trends for each market?
• Who are similar customers and what can we learn from our most successful customers?
• How much do we sell to government entities?
• Industry specific customer information enrichment from 3rd party data

Marketing

• Who are our contacts for each customer?
• Execute campaigns based on commercial insights
• What is the market industry of contacts?

Mergers & Acquisitions (M&A)

• Analyze your relationship with potential M&A targets
• Respond to Mergers & Acquisitions (M&A) inquiries from antitrust authorities
• Gain insights into your total business relationship with customers during week 1
• Use insights into your new total business relationship with customers for accelerated synergy realization

Legal/ Corporate functions

• Provide insights into the value of your relationship with business partners to make legal decisions
• Support ad hoc inquiries about your business partners from government agencies
• Support Global Trade compliance
• Corporate communications

Finance – Credit and Cash Management

• How much receivables is outstanding with a customer
• How consistent are payment terms across all instances of a customer
• What is the total credit limit for a customer
• Integrated 3rd party credit rating information

Purchasing

• How much do we buy from each supplier?
• What are the risk profiles of our suppliers, using 3rd party data

Supply Chain

• Proper identification of suppliers, customers and internal parties is one of the critical components for Advanced Supply Chain Planning, Management and Execution

Data Science

• Data harmonized at levels above raw data is essential for most of the data science plays, avoiding working with meaninglessly fragmented (partial) data

Operations

• Business Partner data facilitates prioritization of various functions
  • Quality – focusing on key customers
  • Purchasing – focusing on key suppliers, e.g, in risk management
  • Environmental – focusing on key business partners
  • Customer Excellence – collecting feedback about our product and services