What is master data management?

Master Data Management is a process that involves the management of critical data assets to ensure uniformity, accuracy, stewardship, semantic consistency, and accountability across an organization. MDM aims to create a “single source of truth” for important data, such as customer information, product data, and financial data. This definition shifts away from the outdated notion that MDM is a technology, to reflect the reality that master data management is a business capability.

Master data are the objects in a business that provide context for transactions. For example, for a procurement transaction a supplier (person) may provide a service (thing) under the terms of a contract (concept). For a purchase, a customer (person) may buy a product (thing) from a store (place).

Why is data integrity the foundation of master data management?

Data integrity is crucial to the success of MDM because it ensures that the data being managed is of high quality and trustworthy across all systems and departments. If data is inaccurate, inconsistent, or unreliable, it can lead to errors in decision-making, wasted time and resources, and ultimately damage the reputation of the organization.

How does Master Data Management work?

MDM involves a series of processes, enabled by technology, that work together to create a single source of truth for critical data. The process typically involves identifying key data assets, cleansing and standardizing data, and creating a master record that can be used across the organization.

How can we align business and data management?

The alignment of business and data management is critical for an organisation's success. To achieve this alignment, four pillars need to be in place: data strategy, data culture, data monetisation, and data literacy. Data Strategy: A clear data strategy is essential for aligning business and data management. It should outline how data will be collected, stored, managed, and analysed to support business goals. A well-defined data strategy can help identify data gaps, define data standards, and establish a roadmap for data management initiatives. Data Culture: Data culture refers to an organisation's values, beliefs, and behaviours around data. It is essential to foster a data-driven culture that values data as a strategic asset and encourages data-driven decision-making. A data culture can help to promote collaboration, transparency, and accountability around data management. Data Monetisation: Data monetisation is the process of leveraging data to create business value. It involves identifying opportunities to monetise data assets, such as through new revenue streams or cost savings. Data monetisation requires a clear understanding of data value and the ability to extract insights from data to inform business decisions. Data Literacy: Data literacy refers to the ability of individuals to read, understand, and effectively use data. It is essential to build data literacy across an organisation to ensure that employees have the skills and knowledge to leverage data for business purposes. This includes data analysis, visualisation, and interpretation.

Telecommunications Company Accelerates Data Migration

The telecommunications enterprise boasts an all-encompassing range of services, encompassing broadband Internet, fixed telephone and mobile device services, as well as television and on-demand entertainment.

Catering to an extensive clientele base, including residential, business, government and wholesale markets, it has become imperative for the firm to revamp its core legacy systems for billing and operations in response to rapidly-evolving technological advancements.

A swift and efficient approach was crucial for the migration of data, one that would guarantee the continuity of business operations while ensuring that the data integrity remained intact throughout the process.

As the firm undertook the daunting task of upgrading its legacy billing support system (BSS) and operations support system (OSS) to modern data center applications, it confronted one of the most arduous and potentially problematic tasks - that of transferring data from the old system to the new one. Since each application formats data differently, maintaining data integrity while moving and transforming data was a vital aspect that could not be overlooked. It was imperative to preserve business continuity and ensure compliance with regulatory standards.

Traditionally, an SQL process is used to translate formats from the legacy system into the requisite new system formats. However, for this customer, the challenge was to consolidate hundreds of terabytes of transaction data from multiple legacy applications into a single new system. The development of an SQL-based solution would take far too long, and it would be nearly impossible to verify the accuracy of the data migration.

Given the size and complexity of this project, the firm required a scalable solution equipped with analytical capabilities to assess data quality, identify errors, uncover root causes and prioritize fixes based on their potential impact on revenue or customer experience.

Read more about how this telecommunications company rapidly integrated diverse data, easily discovered elusive insights and continuously monitor business operations to expose anomalies.

Master Data Management

Align Data, Process, and Governance

For Decision Making and Operational Excellence

Telecommunications Company Accelerates Data Migration

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