MPD

Master Data Management

Optimize Data Quality for MDM Success with Advanced Pre-Processing Solutions

Efficient Data Preparation for Seamless MDM Integration

Unlock the full potential of your Master Data Management with a streamlined pre-processing approach. Ensure data consistency, accuracy, and completeness before MDM implementation to improve decision-making and drive business efficiency. DataEconomy’s pre-process solutions are designed to optimize your data quality, setting the foundation for a successful MDM strategy.

Ingestion

Our robust data ingestion solution is designed specifically for master data management. It accommodates real-time, near real-time, and batch processing. This flexibility allows you to manage data across multiple domains effectively and ensures you have the insights you need when you need them.
It enables immediate capture and synchronization of data from various sources as it is created or updated. This is crucial for maintaining up-to-the-minute accuracy in environments where master data changes frequently, such as customer records, product information, or supplier details.
This feature supports near real-time updates to master data, allowing for slight delays tolerable in most business operations. This ensures master data remains current with minimal lag and is suitable for scenarios like frequent updates to customer profiles or inventory levels.
Itallows the processing of large volumes of master data in scheduled batches, making it ideal for consolidating data from multiple systems, updating reference data, or performing bulk data imports.

Validation

Our flexible, customizable data validation feature is designed specifically for master data management. It allows users to define and configure validation rules for each unique data source. This ensures that only high-quality data, meeting accuracy, consistency, and reliability standards, is integrated into your MDM system, keeping your data clean and trustworthy.
Users can configure custom validation rules based on each data source’s unique requirements. This ensures that data integrity is maintained according to the source’s specific characteristics and standards.
Users can configure multiple validation rules for the same data attribute. This means users can apply a series of checks to ensure the data meets all necessary criteria, such as format, range, uniqueness, and consistency, before it is accepted into the MDM system.
It allows validation rules to be adjusted dynamically as data sources evolve or business needs change. This ensures the MDM system can adapt to new data quality requirements without disruption.

Profiling

Our comprehensive data profiling feature thoroughly assesses and analyzes your data to ensure it’s fully prepared for integration into the MDM system. Identifying and addressing inconsistencies ensures that only clean, high-quality data is incorporated, resulting in a more reliable and efficient MDM system.
Automatically scans and evaluates data from various sources, identifying key characteristics such as data types, formats, distributions, and patterns. This helps users understand the current state of their data and identify potential issues before integrating it into the MDM system.
Generates detailed metrics on data quality, including completeness, accuracy, consistency, uniqueness, and validity. These metrics provide insights into areas where data may need to be cleaned, standardized, or enriched.
Identifies anomalies and outliers in the data, such as unexpected values, duplicates, or inconsistent formats. This allows users to address data issues proactively, ensuring that only high-quality data is incorporated into the MDM system.
Users can define and apply custom profiling rules tailored to specific business needs or data domains. This flexibility ensures the data profiling process aligns with organizational requirements and MDM standards.
The tool generates comprehensive reports that summarize the findings of the data profiling process, highlighting areas that need attention and providing actionable recommendations for data preparation.

Standardization

Standardize your data to ensure consistency and accuracy across all master data records. This process helps maintain high-quality data, reduces duplicates, and strengthens the reliability of business processes that depend on the accuracy and consistency of name and address information.
Standardizes names and addresses according to predefined rules, ensuring uniformity across all records. This includes correcting common errors, formatting data consistently, and aligning with industry standards.
Seamlessly integrate with leading external data providers like SmartyStreets, Melissa Data, Dun & Bradstreet (D&B), and more. This enables real-time verification and enrichment of names and addresses, ensuring the data is standardized, accurate, and up-to-date.

Users can configure and adjust standardization rules to meet specific business needs, ensuring that the standardized data aligns perfectly with internal requirements and industry regulations.

Ingestion

Robust data ingestion tailored for Master Data Management, supporting real-time, near real-time, and batch processing to master multiple domains.
Real-time Ingestion: Enable immediate capture and synchronization of data from various sources as it is created or updated. This is crucial for maintaining up-to-the-minute accuracy in environments where master data changes frequently, such as customer records, product information, or supplier details.
Near Real-time Ingestion: Supports near real-time updates to master data, allowing for slight delays that are tolerable in most business operations. This ensures master data remains current with minimal lag, suitable for scenarios like frequent updates to customer profiles or inventory levels.
Batch Ingestion: Process large volumes of master data in scheduled batches, making it ideal for consolidating data from multiple systems, updating reference data, or performing bulk data imports.

Validation

A highly flexible and configurable data validation feature tailored for Master Data Management, allowing users to define and configure validation processes specific to each data source, maintaining the accuracy, consistency, and reliability of ensuring that only high-quality data is integrated into the MDM system.

Source-specific Validation: Users can configure custom validation rules based on each data source’s unique requirements. This ensures that data integrity is maintained according to the source’s specific characteristics and standards.

Multi-layered Validation: Configure multiple validation rules for the same data attribute. This means users can apply a series of checks to ensure the data meets all necessary criteria, such as format, range, uniqueness, and consistency before it is accepted into the MDM system.

Dynamic Adjustment: Validation rules can be adjusted dynamically as data sources evolve or business needs change. This ensures the MDM system can adapt to new data quality requirements without disruption.

Profiling

Comprehensive data profiling feature that assesses and analyzes data to ensure it is ready for integration into the MDM system, ensuring that only clean, consistent, and high-quality data is integrated, ultimately leading to a more reliable and effective MDM system.

Automated Data Assessment: Automatically scans and evaluates data from various sources, identifying key characteristics such as data types, formats, distributions, and patterns. This helps users understand the current state of their data and identify potential issues before integrating it into the MDM system.

Data Quality Metrics: Generates detailed metrics on data quality, including completeness, accuracy, consistency, uniqueness, and validity. These metrics provide insights into areas where data may need to be cleaned, standardized, or enriched.

Anomaly Detection: Identifies anomalies and outliers in the data, such as unexpected values, duplicates, or inconsistent formats. This allows users to address data issues proactively, ensuring that only high-quality data is incorporated into the MDM system.

Customizable Profiling Rules: Users can define and apply custom profiling rules tailored to specific business needs or data domains. This flexibility ensures that the data profiling process aligns with organizational requirements and MDM standards.

Data Readiness Reports: The tool generates comprehensive reports that summarize the findings of the data profiling process, highlighting areas that need attention and providing actionable recommendations for data preparation.

Standardization

Standardize the data to ensure consistency and accuracy across master data records. This will maintain high-quality master data, reduce duplicates, and enhance the reliability of business processes that rely on accurate and consistent name and address information.

Name and Address Standardization: Standardizes names and addresses according to predefined rules, ensuring uniformity across all records. This includes correcting common errors, formatting data consistently, and aligning with industry standards.

Integration with External Data Providers: Seamlessly integrate with leading external data providers like SmartyStreets, Melissa Data, Dun & Bradstreet (D&B), and more. This enables real-time verification and enrichment of names and addresses, ensuring that the data is standardized, accurate, and up-to-date.

Customizable Standardization Rules: Users can configure and adjust standardization rules to meet specific business needs, ensuring that the standardized data aligns perfectly with internal requirements and industry regulations.

Contact us

We Measure the Success by the value delivered to Your End Users

We’re happy to answer any questions and help you determine which of our capabilities best fits your needs. 

Contact : 614-356-8153 
Email : info@dataeconomy.io 
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2

We do a discovery and consulting meeting 

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We prepare a proposal 

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