The Case for Master Data Management

Jose Almeida
4 min readFeb 7, 2022
The Case for Master Data Management

For years organizations have been questioning if Master Data Management (MDM) was the right approach, or if they had the condition to move forward to an MDM implementation, this is no longer the case.

MDM is no longer a solution for large organizations, or a vanity project. Given just how critical data is, MDM is vital for any organization irrespective of its size and reach.

An MDM solution consolidates the complete data offering into a single source, providing integrated data to every business function across the organization.

It will have a direct impact on increasing efficiencies by improving data quality, reducing time and cost, avoiding data duplication and redundancy, increasing data accuracy, increasing data compliance, enabling data driven decision making — creating a single source of truth for master data — the most critical and foundational data in every organisation.

This is particularly critical when addressing the financial services industry (banking, financial services, insurance), but not limited to it.

Every organization with very complex data ecosystems, that have grown over the years, sometimes with dozens of different source systems — data silos, islands of data, one of the greatest barriers to an efficient usage of data and to unlock the potential of analytics for business decision processes.

These data silos were born out of an organization’s growth, the result of years of initiatives driven by business problems, years of different business strategies, multiple technological options, and even resulting from mergers or acquisitions.

From the data perspective, data silos are an abnormality, they impact data sharing, data quality, the costs of data acquisition and preparation, they have a serious impact on the insights that can be driven from data, and more critically they impact on an organization’s competitiveness.

This is where MDM can play an important role, overcoming the challenges of manual data management, redundant data, and data discrepancies, collecting, processing, storing, analysing, and integrating data from the different organization’ sources.

When to consider Master Data Management?

Most organizations that are planning initiatives like updating its data warehouse to enable near real-time data, creating a big data environment to support analytics, increasing digital capabilities, migrating data and analytics to the cloud, business intelligence, big data analytics, machine learning or artificial intelligence.

It’s important to understand that Master Data is a key component for the success of any of these initiatives.

Most of us can relate with these problems:

• Increasing data volumes from more and more sources.

• The gap between business and IT, leading to often contradictory strategies.

• Difficulty in managing a siloed data ecosystem.

• Difficulty to identify and define data across sources.

• Lack of standard business and data management rules and data protection policies.

• Rising data-security concerns around providing employees with remote access to data.

• Difficulty to identify, cleanse, standardize, and curate data for sharing.

• Existence of duplicate, erroneous, inaccurate, and incomplete data.

• Negative impact of regulatory requirements, either data protection regulations, or industry regulations.

In each of these issues there’s master data involved in some degree. That’s why it’s critical that any data initiative is built on a strong Master Data foundation.

Below are a few cases where implementing a Master Data Management solution is the best option to consider:

1. Improving Efficiency

In organizations with very complex data ecosystems — data silos — inevitably the different business lines are working with specialised data pertaining to their needs, and their own versions of the organization’s master data, leading to inefficiencies in time, effort, and energy.

2. Regulatory, financial and risk reporting

There’s a considerable benefit from MDM in regulatory reporting requirements compliance, and when generating reports for auditors or regulators. New business models make it increasingly important to have a single source of compliance data.

3. 360-degree View of Customer Data

Organizations with siloed ecosystems find extremely difficult to maintain a comprehensive, 360-degree view of a customer who simultaneously has multiple products across different business lines. MDM provides a single, common data reference across systems, enabling a central point for customer-level reports making the integration of data across multiple sources easier.

4. Increasing Data Quality

Creating a single source of truth is one of the key objectives of an MDM implementation, this means also to remove duplications and inconsistencies, impacting directly on the quality and accuracy of data, with a cascading positive effect on the entire data dependent processes in the organization.

5. Data Privacy

MDM with improve the capability to manage all the personal data exiting across the multiple silos, clearing identifying all the data under the scope of data protection regulations, allowing more efficient management of this data, and reducing the risk of data breaches.

6. Cross-Channel Integration

With the emergence of new business models and the proliferation of different channels, we’ve moved from transaction-oriented relations with customers, to an event-oriented relation. Without a central reference for each of an organization’s customers it’s impossible to keep track of all its iterations and be able to derive business valuable insights from this data.

7. Enhancing Data Driven Decision-Making

The outcome of a decision process, made without trust on data or without trustable data, can’t be much better that common guess work, however educated it might be.

Creating a reliable and accurate source for an organization’s core data — The data that is critical in most of its business processes is a crucial step towards data driven decision processes.

8. Developing Cross-Department Coordination

In organizations where each business line is working withing its own silo, with its own versions of customers, products, etc., creating a disconnect with the business strategy, and impeding the integration of data from these silos.

Conclusion

These are but a few examples where MDM can drive quantifiable bottom-line benefits for organizations. MDM enables organizations to provide the business with trusted and complete data to improve business, with positive impacts on business performance, opportunities, operational costs, or customer loyalty.

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Jose Almeida

Data Consulting and Advisory MEA - Driving better insights through better data (www.josealmeidadc.com)