Prescription Analytics for MDM

Prescriptive analytics to be considered for Master Data Management (MDM)

Prescription Analytics for MDM is an advanced data-driven approach that helps us to improve the quality, consistency, and governance of their master data while also providing recommendations and actions to address data-related challenges. Master data includes critical information about entities such as customers, products, suppliers, and employees, and it is foundational for various business processes.

Here's how prescriptive analytics is applied in the context of MDM:

​1. Data Quality Improvement:

• Prescriptive analytics can assess the quality of master data by analyzing data integrity, consistency, completeness, and accuracy. It can provide recommendations on how to clean and standardize data, such as data cleansing procedures or data enrichment sources.

• It can also prescribe specific data validation rules and data quality metrics to monitor and maintain high data quality standards.

2. Data Matching and Deduplication:

• In MDM, data duplication and matching are common challenges. Prescriptive analytics can recommend strategies to identify and merge duplicate records based on advanced matching algorithms.

• It can suggest which records to merge, what attributes to prioritize, and how to handle potential conflicts or discrepancies.

3. Data Governance and Stewardship:

• Prescriptive analytics can help define and recommend data governance policies, roles, and responsibilities. It can guide organizations in establishing data stewardship practices, suggesting who should be responsible for specific data domains.

• It can also recommend workflows and approval processes for data changes and updates.

4. Data Standardization:

• To ensure consistency across master data, prescriptive analytics can recommend data standardization rules and techniques. It can suggest how to transform and harmonize data to follow defined standards.

• This includes recommendations for handling variations in data formats, units, and naming conventions.

5. Data Enrichment and Validation:

• When master data is incomplete or outdated, prescriptive analytics can recommend external data sources or APIs for data enrichment. It can guide the process of validating and appending data, ensuring it is up to date and accurate.

• It can also suggest validation procedures for newly entered data, preventing the introduction of incorrect information.

6. Data Synchronization:

• In organizations with distributed systems and databases, prescriptive analytics can recommend strategies for data synchronization, including how to propagate changes to master data across the enterprise.

• It may suggest batch processes, real-time data integration, or APIs for synchronizing data across applications.

7. Data Retention and Archiving:

• Prescriptive analytics can recommend data retention and archiving policies based on legal and business requirements. It can suggest criteria for identifying data to be archived and for setting retention periods.

• This helps organizations manage their data storage costs and compliance with regulations.

8. Data Access Control:

• To maintain data security and privacy, prescriptive analytics can recommend access control policies and permissions for different users or roles. It helps in defining who can view, edit, or delete specific master data records.

• It can also suggest audit trails and monitoring mechanisms for tracking data access and changes.

9. Scalability and Performance Optimization:

• For situations dealing with large volumes of master data, prescriptive analytics can recommend strategies for optimizing the performance and scalability of MDM systems.

• It may suggest database tuning, data partitioning, or caching mechanisms to ensure efficient data retrieval and processing.

Prescriptive analytics in MDM helps in

· addressing data quality

· addressing governance issues

· ensures that organizations follow best practices for managing their critical master data.

· empowering us to make data-driven decisions

· reduce data-related errors and inconsistencies

· improve the overall efficiency and effectiveness of their business processes.

Prescriptive analytics helps improve data accuracy, completeness, governance, and ultimately trust in master data by prescribing actions to address identified issues and risks.

Sash Barige

Jan/20/2022

Further Read:

Leveraging Prescriptive Analytics to Augment MDM:

https://docs.informatica.com/integration-cloud/clouddataplatform/current-version/perspective-for-data-stewards/leveraging-prescriptive-analytics-to-augment-mdm.html

Prescriptive Analytics for Master Data Governance:

https://www.infoworld.com/article/3667347/prescriptive-analytics-keeps-master-data-in-check.html

MDM and Prescriptive Analytics for Customer 360:

https://s3.amazonaws.com/assets. StringVar.com/brochures/Prescriptive-Analytics-for-Customer-360.pdf

Webinar - The Role of Prescriptive Analytics in MDM:

https://www.visionware.com/resources/the-role-of-prescriptive-analytics-in-master-data-management-visions-2021-mdm-webinar-series/

Prescriptive Analytics Use Cases in Data Management:

https://www.erwin.com/blog/Prescriptive-analytics-use-cases-in-data-management