Mdm-En — TechCom

Data Governance for Customer 360: managing the customer master record

Mon, 15 Jun 2026 06:11:38 +0300

Building a holistic view of the customer, known as Customer 360, is a fundamental objective for enterprise businesses, particularly in the banking and financial sectors. This goes beyond mere data aggregation; it involves creating a single, authoritative, and up-to-date master record that serves as the source of truth for all systems and departments. However, in practice, implementing this concept faces organizational and technical risks, the primary one being the determination of who precisely has the authority to modify this master record, especially with the increasing role of artificial intelligence (AI) and heightened cybersecurity requirements.

Improving system integration quality through AI-based data governance

Thu, 14 May 2026 06:07:16 +0300

By 2026, over 70% of successful digital transformation initiatives will rely on an effective Data Governance strategy integrated with artificial intelligence capabilities. This will enable addressing data quality issues critical for system integration. The increasing complexity of enterprise IT landscapes, encompassing on-premises systems, cloud solutions, and hybrid infrastructures, necessitates a new approach to data management where AI becomes not just a tool, but a central element of the strategy.

Securing APIs against emerging threats with AI technology

Wed, 13 May 2026 06:08:01 +0300

By 2026, Gartner predicts that 40% of web application attacks will utilize APIs as their primary vector, underscoring the critical importance of strengthening application programming interface security within system integration contexts. The increasing complexity of enterprise landscapes, encompassing hybrid and multi-cloud environments, expands the attack surface for potential adversaries. Traditional defense methods are no longer sufficient to effectively counter dynamic and adaptive threats, demanding the integration of artificial intelligence-based solutions.

How API-centric integration improves enterprise time-to-market

Thu, 30 Apr 2026 06:05:48 +0300

According to Gartner, by 2025, over 65% of global companies will use APIs as the primary integration mechanism for their critical business applications. This trend is driven by the need to accelerate time-to-market, enhance operational efficiency, and ensure flexibility amidst rapid market changes. API-driven system integration allows businesses to quickly adapt their IT landscapes by integrating new services and solutions without significant rework of existing systems.

Role of APIs in modern IT landscapes

APIs (Application Programming Interfaces) act as bridges between different software components, enabling them to interact and exchange data. In the context of system integration, APIs provide a standardized and secure way to connect enterprise applications such as ERP, CRM, ECM, and specialized industry systems. This facilitates the creation of modular, flexible, and scalable architectures that easily adapt to new business requirements.

Enterprise data management and governance for system integration

Fri, 24 Apr 2026 06:10:33 +0300

According to Gartner, by 2026, over 80% of organizations that fail to implement comprehensive Data Governance strategies will face significant fines for non-compliance or lose competitive advantages due to poor data quality. Enterprise Data Management and Data Governance have become critical components for any company striving for effective digitalization and system integration. This is not merely a set of rules but an integrated strategy encompassing processes, policies, standards, and technologies to ensure data quality, security, and accessibility throughout its entire lifecycle.

Methodologies for FinOps and cloud expenditure management

Mon, 20 Apr 2026 06:05:26 +0300

The head of the development department at a major financial institution faced a challenge: despite all efforts to optimize the architecture, the monthly cloud services bill continued to grow, regularly exceeding the planned budget by 15-20%. Analysis revealed that the causes were not only unaccounted-for peak loads but also inefficient resource utilization, redundant capacity, and a lack of transparency in cost allocation across various projects. This scenario, unfortunately, is typical for many companies migrating to the cloud without a clear cost management strategy.

MDM and data integration strategies for large enterprises

Fri, 17 Apr 2026 06:08:19 +0300

In a large industrial enterprise with a long history, there are multiple ERP systems, separate CRM solutions for different departments, legacy databases with information on equipment and suppliers, and new cloud services for logistics management. Each department works with its own data, which is often duplicated, contradictory, or simply missing in other systems. The marketing department launches a campaign based on one set of customer data, the sales department uses another, and the finance department cannot even compile a unified profit report because information about the same products has different identifiers in different systems. This leads to operational delays, incorrect decisions, lost profits, and reduced competitiveness. This is where system integration and Master Data Management (MDM) become not just desirable, but critically important tools.

System Integration Strategies Replacing Monolithic ERP

Fri, 10 Apr 2026 09:00:00 +0300

Implementing a large monolithic ERP has traditionally been considered the answer to enterprise needs for a unified management system. However, reality shows that monolithic ERPs are difficult to adapt to specific business needs, are expensive, and often evolve into a rigid, 'concrete' system that stakeholders are hesitant to modify.

The Problem with the Monolithic Approach

Any configuration often requires extensive customization, making upgrades painful. Unused modules incur significant licensing costs. Implementations can stretch for years. Furthermore, vendor lock-in becomes virtually impossible to overcome without a complete re-engineering of processes.

2026 Electronic Document Management: AI-driven archiving and support

Fri, 06 Mar 2026 11:21:34 +0200

Electronic Document Management 2026: AI Assistants and Archiving Automation

At the end of reporting periods, legal and administrative departments process large volumes of documents: contracts, addenda, invoices, and acts. Each document must be classified, validated, enriched with metadata, and archived. These operations are still often manual or semi-automated, making them time-consuming and prone to errors, delays, and data inconsistencies.

Modern document management approaches rely on AI/ML technologies to automate these processes. These are no longer experimental tools but applied solutions already used in enterprise ECM and DMS systems (Enterprise Content Management / Document Management Systems).

Data Stewards and Owners in Master Data Management

Thu, 05 Mar 2026 15:53:23 +0200

As organizations shift to decentralized architectures and integrate AI, IT directors and architectural leads face a harsh reality. The lack of clear data ownership and management has become a critical bottleneck for operational reliability. Enterprise integration landscapes, often comprising dozens of systems (ERP, CRM, ECM), frequently suffer from fragmented data quality. The core issue lies not in the choice of technology stack, but in the absence of an organizational structure. Without defined roles for Data Owners and Data Stewards, it is impossible to create reliable data contracts and ensure stable Master Data Management (MDM).

Scalable architectural patterns for MDM and system integration

Fri, 27 Feb 2026 14:36:00 +0200

Modern enterprises operate under conditions of constantly increasing data volume and exchange complexity. When the number of corporate systems exceeds a critical threshold (typically 8 or more autonomous applications), direct integration between them becomes a serious architectural challenge. Without standardized approaches, the IT landscape quickly degrades into a chaotic web of connections, where a change in one system triggers a cascade of errors in others. Architects are forced to spend colossal resources maintaining fragile connections, losing control over data integrity and complicating end-to-end audits.

Leveraging AI for master data management and integration of systems

Thu, 19 Feb 2026 15:58:29 +0200

In 2026, companies face increasing data volumes from diverse sources. The need for data consolidation and quality assurance has become a significant business challenge. Industry estimates indicate that a substantial portion of large businesses' operational costs is linked to poor data quality. This makes Master Data Management (MDM) a strategic imperative, now amplified by the integration of Artificial Intelligence (AI).

The role of AI in enhancing data quality and consistency

Artificial intelligence is already actively employed to address key MDM challenges. Machine learning algorithms can automatically identify duplicates, detect anomalies, and uncover inconsistencies in data, tasks that previously required significant manual effort. This not only accelerates processing speed but also improves master data quality, ensuring its consistency across the entire enterprise infrastructure.

Addressing offboarding as a critical cybersecurity vulnerability

Fri, 16 Jan 2026 15:46:29 +0200

Imagine this scenario: a valuable developer with access to critical repositories and production systems leaves the company. The offboarding procedure is limited to returning a laptop and signing a clearance form. Months later, during an internal audit, it's discovered that their Gitlab account is still active, and access keys to cloud services were never revoked. This isn't a hypothetical situation but a real vulnerability that threatens thousands of companies daily.

Implementing Zero Trust for Hybrid Infrastructure Security

Thu, 08 Jan 2026 09:39:00 +0200

Imagine this scenario: a remote employee connects to the corporate network via VPN. The traditional security model often grants them broad access to internal resources after a single authentication. But what if their device is compromised, or their credentials are stolen? This is precisely where the fundamental weakness of outdated approaches lies, opening the door for attackers to move laterally within the network. In hybrid infrastructures, where data and applications are distributed across on-premises servers, private, and public clouds, this risk is amplified.