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Master Data Management and Operational Workflows: Two Modern Use Cases

ABSTRACT: This blog explores the opportunity for automated workflows to help cross-functional teams collaborate and standardize organizational master data.

Sponsored by Semarchy

As parents know, it’s one thing to teach our children good grammar. It’s quite another to have them use it. Such is the challenge of master data management: enterprises might define just the right master data, only to have business users veer away from it. This blog explores the opportunity for automated workflows to help cross-functional teams collaborate and standardize organizational master data. Designed and implemented well, automated workflows can make the modern business just a little less chaotic and complex.

Master data management

Master data management (MDM) is a set of practices and tools that aim to maintain a single source of truth with consistent, trusted records for key business entities. MDM tools match and merge data from various source systems to create standard attributes and terms that describe entities such as products, customers, and partners. These “golden records” strengthen data governance programs by helping reduce duplicates and resolve discrepancies. They enable business users to make smart decisions and take the right actions.

Obstacles

But that only happens if data stewards and business users overcome certain obstacles. 

  • Frequent terminology changes for organizational units, entities, and processes 

  • Siloed tools, teams, and datasets

  • A lack of technical skills among business users

Such obstacles hurt team productivity, drive up costs, and reduce agility. And they explain why enterprises rank “data quality/MDM” as the most important trend in data, analytics, and BI for six years running, according to the latest annual survey by Eckerson Group’s research partner BARC.

To overcome these obstacles, enterprises must standardize how they create and operationalize master data. Data experts within IT need to design workflow templates that automate the sequence of tasks related to a given event—perhaps the addition of a new product, change to a customer record, or removal of a supplier within the MDM system. These workflows can help data engineers and business users collaborate as they create and update master data records.

Use cases

All easier said than done! The good news is that vendors such as Semarchy help simplify cross-functional MDM processes with automated, data-driven workflows. Let’s consider two use cases that illustrate what’s possible: the creation of a new product and the revision of a customer entity.

1. Product team releases a new product

Suppose the head of product for a financial-services company creates a new insurance product for first-time home buyers. They use a guided workflow to ensure stakeholders collaborate efficiently, align with master data, and maintain visibility to spot bottlenecks.

  • Design. The data steward designs a workflow template called “Create Product.” They use a graphical interface to configure the workflow parameters, assign task owners, and define actions that trigger handoffs between owners. This template's workflow will span the product, sales, legal, and marketing teams.

  • Execute. The product team kicks off a workflow by creating records that contain product names, numbers, and prices. They click to complete the task, automatically routing the records to sales leaders for pricing approval and the legal team for compliance approval. Once sales and legal teams complete their tasks, these records become “golden records.” The marketing and PR teams receive notifications containing this new master data, which they incorporate into external content.

  • Manage. The data steward, product leader, and various business stakeholders receive notifications and access their role-specific dashboard to understand task status and a detailed workflow history. These stakeholders might start or stop a workflow based on their assigned role, or add tags to a workflow to assist handoffs. 

2. Revision of a customer entity

Now suppose the Northeast sales leader for a furniture wholesaler combines records for two global customers after they merge with one another. Combining these records creates a ripple effect for sales, marketing, finance, and customer service teams. 

  • Design. The data steward designs a workflow template called “Change Customer Master Data.” Following a similar process as described earlier, they graphically configure the workflow parameters, owners, and handoffs. 

  • Execute. The sales leader revises the customer names and merges the records. This triggers an automated request to finance teams in North America and Europe that they update their sales databases, regulatory filings, and tax documents. Once finance managers complete those tasks, they route the workflow back to the data steward for final review and approval.

  • Manage. Various stakeholders, particularly the data steward and other governance officers, check task status and oversee the change process through a central dashboard. This gives them the confidence they need to ensure compliance with internal policies and regulating bodies.

Conclusion

The tasks described here are not new; in fact, companies had to follow similar processes when they used pen and paper 100 years ago. But even in the modern computing age, they create a level of complexity that can undermine productivity, agility, and visibility. Enterprises that automate their MDM workflows can improve the agility and efficiency of responding to business events. They can reduce just a little bit of the complexity and redundancy of our modern business environment.

Kevin Petrie

Kevin is the VP of Research at BARC US, where he writes and speaks about the intersection of AI, analytics, and data management. For nearly three decades Kevin has deciphered...

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