How Does Pimcore MDM Improves Enterprise Data Governance, Quality, and Security Standards?
Master data is now 100% important to accurately define entities like employees, customers, prospects, vendors, suppliers, sites, and hierarchies in the enterprise. Along with that, stewardship and management of master data is even more critical to establish and enforce guidelines for data collection, integration, and processes. Without it, organizations cannot navigate forward in the digital-driven world— which means no digital transformation, no analytics to amplify growth, no personalized customer experience, and low probability to perform even basic operations. To get maximum value from master data, it must be up-to-date, accurate, of high quality, and relevant. That’s why data governance, data quality, and security standards must not be neglected to ensure your master data always remains readily available and trusted.
Pimcore’s Master Data Management Software
Pimcore Master Data Management Software is a leading open source MDM available in the market that enables you to manage all aspects of any master record. It helps you manage any data such as product data, customer data, employee data, asset data, partner or vendor data, location data, and reference data. It is multi-domain and multi-vector and can be easily deployed on both on-premise and cloud infrastructure. Pimcore MDM offers specific features and capabilities that support different aspects of data stewardship.
Data Quality and Data Governance
Data quality and data governance are essential parts of enterprise data management. It has a more significant role in supporting compliance and defining data policies in the information architecture.
Pimcore MDM provides a conduit for implementing data quality dimensions associated with your critical master data elements, defining the data rules that affect compliance, defining quantitative measurements for conformance to information policies, and ways to integrate these all into a data governance framework. It empowers concerned teams across the organization to enforce well-defined data governance policies and establish the underlying organizational structure for better management, oversight, and execution. Here is how Pimcore MDM enables data governance, data quality, and security standards:
Central Governance for Consistency and Integrity
- Facilitate in the centralized management, governance, compliance, and transparency of master data across the data value chain
- Define, authorize, and reuse key master data entities for consistency and better operational efficiency
- Eliminate error-prone and tiring manual processes for master data in multiple systems and applications
High Quality, Clean, and Trusted Master Data
- Analyze process quality and monitor data quality and data completeness
- Automatically supply high quality and the right type of master data to your output channels
- Provide a central view for data validation and data quality check
- Offer rich auditing and versioning features to track all data modifications in one place
- Get pre-configured data types for colors, URLs, geodata, countries and languages, numbers, or SKUs
Integration and Reuse
- Take charge of data integration through easy import and export of data between Pimcore and external systems
- Reuse of Pimcore data model, existing business logic, and configuration for data validation
- Offer a full-featured REST Webservice API and a Data Hub GraphQL API for two-way real-time integration with other apps/systems
- Master data is used and managed across multiple distributed systems. This demands a high level of security for data credibility and blocking unauthorized access to critical data. Pimcore MDM implements a multi-layer security concept to keep Pimcore-based solutions safe.
- Provide advanced user and rights management and authentication mechanisms
- Ensure multifactor authentication (2FA) and industry-standard SSO protocols
- Restrict the access of unauthorized users to the system
- Implement data storage security with stringent file accessibility
Organizations evaluating master data management software should approach the overall data governance, data quality, and security standards as a discipline by leveraging an adaptive framework that enables the application of different governance styles to suit the context of different business scenarios.
They should compare the features of MDM solutions by identifying first their relevance and connection to business outcomes and secondly their ability specifically to support information stewards’ work on policy enforcement. Last, they must match internal requirements of data governance, quality, and security capabilities for MDM-related projects to vendors’ application solutions specific to this market. Moreover, focus on all dimensions — people, process, technology, and data when addressing the master data management use case. These dimensions are important considerations if the MDM solution is to maximize ROI through reuse while minimizing administrative costs and errors due to inconsistencies between technologies.
Keeping these factors in mind during the MDM platform selection will ensure that the master data repository does not adversely affect by a failure, or potential failure, of data and analytics governance. Instead, it will benefit in business-oriented growth as well as strengthen the ability to manage all enterprise data activities with confidence.
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