On August 8, 2023, Intecracy Group consortium held an online event in the format of an Intecracy Expert Webinar dedicated to the pressing challenges of digitalization in large enterprises. The main topic of the discussion was «Master Data Management and Reference Data Management». The event's speaker was Serhii Balashuk, a leading solution architect. The expert and the participants focused on practical methods of eliminating duplicates in corporate databases and establishing a Single Source of Truth (SSOT) using modern Data Management technologies.
A modern enterprise operates with colossal volumes of information generated daily across various functional departments. Finance, logistics, sales, and procurement specialists often utilize isolated information systems. As a result, the issue of inconsistent reference data arises: the same counterparty, product, or service might be registered across different databases under slightly different names or with varying identifiers. This leads to reporting errors, decision-making delays, and additional costs associated with manual data correction.
The Challenges of Decentralized Information Storage
During the webinar, the prerequisites for the emergence of chaos in corporate data were analyzed in detail. As an organization scales, the number of integration links between systems grows exponentially. Without centralized control, every new data exchange interface merely multiplies the number of errors. Serhii Balashuk emphasized that classical relational databases of individual application systems are incapable of independently solving the problem of global format incompatibility and record duplication at the entire company level.
To overcome these challenges, specialized Master Data Management (MDM) solutions are applied. The primary goal of implementing such tools is not just a technical merging of tables, but the creation of a comprehensive methodology and infrastructure for cleaning, enriching, and synchronizing critical business data among all consumers within the corporate perimeter.
“Creating a single source of truth is not a one-time technical project, but an ongoing management process. The main compromise that architects face when implementing MDM is choosing between rigid centralization, which slows down the entry of new records, and analytical consolidation, which allows systems to operate autonomously but requires complex algorithms for matching and merging data post factum. We must find a balance that corresponds to the dynamics of a specific business”, — noted Serhii Balashuk.
Mechanisms of Deduplication and Data Consolidation
The speaker paid special attention to the technical mechanisms that ensure high quality of reference data. The deduplication process consists of several sequential stages. First, data from various sources undergoes standardization — bringing address formats, phone numbers, company names, and tax codes to a unified standard. The next step involves applying heuristic algorithms and fuzzy matching methods to detect potential duplicates.
After identifying similar records, the system makes a decision on merging them based on configured survivorship rules. For instance, the system determines which source has a higher priority for a specific attribute: a counterparty's address may be retrieved from the CRM system, while their payment details are sourced from the ERP. In this manner, a so-called 'Golden Record' is formed, containing the most up-to-date and verified information.
Architectural Styles of MDM System Construction
As part of his presentation, Serhii Balashuk analyzed three main architectural models for building master data management systems in detail:
1. Registry — source systems store their data locally, while the MDM system contains only pointers and cross-reference identifiers. This approach is the least invasive but requires complex distributed queries to form a complete picture.
2. Consolidation — data is regularly collected from local systems into a centralized hub for analytics and reporting, while operational data entry remains decentralized.
3. Transactional model (Centralized/Transactional) — the creation and modification of any master data occur exclusively within the MDM system, which subsequently distributes updates to all other applications. This ensures the highest level of data cleanliness but requires restructuring many organizational business processes.
The choice of architecture depends on the enterprise's readiness for organizational changes and the technical maturity of its IT landscape. Concluding the webinar, the speaker emphasized that the effective implementation of Data Management tools drastically reduces the cost of integrating new systems in the future, as any new application connects to an already existing, cleaned, and structured source of information.
The Role of Data Governance in Ensuring Reference Data Quality
In addition to purely technical data cleaning tools, a critical component of success is the implementation of Data Governance processes. This is a set of regulations, roles, and responsibilities that define who exactly in the organization is responsible for creating, verifying, and approving new records in reference books. Without clearly defined business roles, such as Data Stewards, even the most advanced MDM platform will quickly fill with outdated information.
Data stewards act as a bridge between the IT department and business users. They resolve disputes when automated deduplication algorithms cannot unambiguously determine whether two records are duplicates, or when conflicts arise in synchronization rules. The webinar demonstrated that the combination of automated Data Management tools and a refined organizational structure is the only reliable path to building a sustainable enterprise information ecosystem.