Master Data Management in Shared Services

For multinational organizations operating across diverse geographical locations and business units, moving to shared services has provided tangible benefits. These advantages go well beyond cost savings – they also improve efficiency through streamlined processes and enhanced service quality through standardization.

As shared service organizations continue to evolve and increase the scope of their services, they are including different processes that help their parent organizations increase efficiency and decrease overall operational cost. One of such steps includes Master Data Management (MDM).

Every enterprise relies on various types of master data, such as employee records, supplier details, customer information, material specifications, and financial data, to operate effectively. Leading organizations leverage shared service models to ensure that this data is processed meticulously and expertly administered. As a result, they are able to prevent data silos, reduce errors, and empower better decision-making across different verticals of an enterprise.

Why MDM Matters

Master Data Management is an enterprise-wide, cross-functional effort that facilitates the business goal of accessing a single view of a customer and the organization. Here are the benefits of an effective MDM strategy:

• Provides Consistent Data Flow
  An effective management of shared services data provides a single version of truth for back-office processes and functions. A centralized location for all data points ensures that a parent organization gets consistent data on customers, vendors, finance, and supply chain.

• Ensures Accuracy of Data
  MDM acts like a sophisticated data refiner. It takes raw data, enriches and standardizes it before uploading it to the data lake. This meticulous preparation ensures that the organization only relies on accurate and complete data.

• Fuels Deeper Customer Connections
  With access to master data concerning customers, companies can know them better and come up with strategies to attract a wider customer base.

• Elevates Data Products for Precision and Compliance
  By standardizing data across different systems and departments, MDM revolutionizes data products. This facilitates accurate reporting and analysis, while allowing shared service organizations to comply with local regulations and industry standards.

• Acts as Definitive Source for Connected Systems
  Master Data Management also acts as a powerful system of reference, which shares high-quality data with various applications and other data domains through REST APIs.

• Provides Smarter Data for AI
  MDM and Artificial Intelligence have a mutually beneficial relationship. The former can utilize AI algorithms to detect and merge duplicate records automatically. This intelligent cleansing improves the quality and reliability of the data. This enhances the performance and accuracy of generative AI systems.  

Implementation Steps

Effectively implementing master data management for shared services requires a clear strategy. Begin by defining the value MDM will create, aligning with priority business goals like operational efficiency or customer insights, which directly impact cost savings and revenue growth.

Measure success using ROI, total cost of ownership, and performance baselines. To achieve this, focus on the following MDM best practices for implementation:

• Build a Golden Record
  Shared service organizations should create a single and accurate source of truth. This requires integration and continuous updating of data from all business units, which AI and ML can facilitate.

• Establish Robust Data Governance
  Develop a robust data management framework with clearly defined roles and responsibilities. These need to be backed by a government council that maintains data integrity and reliability.

• Choose the Right MDM Tool
  Another aspect of effective MDM implementation is selecting intuitive and AI or ML-powered tools. These should align with business priorities so that they can improve data quality while ensuring seamless integration.

• Plan for Capability Building and Change Management
  To ensure widespread adoption of the MDM principles, the shared service organization must focus on securing executive sponsorship. Engaging stakeholders and providing comprehensive information is equally crucial.

• Start with a Pilot
  Begin with a controlled pilot in one domain to validate the design, governance, and workflows, allowing for adjustments before scaling.

Challenges and Solutions

Implementing Master Data Management in shared services is highly beneficial. However, it comes with its own set of challenges. 

Redundant Data

The presence of redundant or duplicate data is one most pervasive issues for businesses. This issue occurs when various processes, systems, or applications maintain similar information with differing attributes.

By implementing a well-structured MDM process, shared service organizations can tackle this issue. It centralizes and standardizes data management practices, facilitating a source for a single and authoritative version of the truth. 

Data Discrepancies

Consolidation of data scattered across disparate systems can sometimes result in errors and inconsistencies. They can lead to substantial revenue losses and damage the brand reputation. There may even arise potential legal repercussions.

To ensure a consistent master data accuracy across all systems, an efficient MDM process is key, which minimizes significant business risks. 

Operational Bottlenecks from Bad Data

In case of errors in data entries and inconsistencies in critical information, such as product or customer details, business operations can have a serious impact. They also affect the cost and performance of varying operations.

By establishing a ‘golden record’ and standardizing data, MDM tackles these inefficiencies. This translates into smoother operations and more reliable business outcomes.

Measuring Data Quality

Good data quality refers to the data that is suitable for its intended use case. This entails that the quality of data always remains context-dependent, which means there is no universal benchmark for measuring its quality.

In the context of Master Data Management, several key dimensions help evaluate the data quality, including:

• Completeness: Are there any missing values crucial for the data’s purpose? 

• Validity: Does the data conform to predefined rules and formats? 

• Uniqueness: Is the data free from duplicates or redundant entries? 

• Consistency: Is the data uniform and in agreement across various data stores and systems? 

• Timeliness: Does the data accurately reflect reality as of the required point in time, ensuring it is up-to-date? 

• Accuracy: To what degree does the data precisely represent the real-world entity or event it describes? 

Ready to transform your shared services with high-quality, reliable data? Contact us today to learn how robust Master Data Management can drive efficiency, empower better decisions, and accelerate your organization’s growth.