May 25, 2024

How to Elevate Master Data Management

This document outlines the critical steps for effective master data management using the SEIRI, SEITON, SEISO, SEIKETSU, and SHITSUKE methodologies. It emphasizes identifying and removing unnecessary data, organizing essential data, developing procedures for data accuracy, and maintaining data quality standards. By following these structured approaches, organizations can ensure reliable and efficient data management, enhancing decision-making and operational efficiency. This guide provides detailed procedures and responsibilities to establish a robust data management system.

SEIRI - Identify and remove any duplicate or unneeded data only keep the necessary data.

  • How: Identify and remove any duplicate or unneeded data, only keep the necessary data.
  • Why: Removing unnecessary data from master data helps to reduce clutter and improves efficiency by making it easier to find, manage, and maintain critical data.

Steps:

  1. List all master data items, including product numbers, product properties, and quality standards.
  2. Review the usage of those items through system log analysis.
  3. Review and redefine the definitions and usages of the data.
  4. Remove the unnecessary data and confirm with key stakeholders.

SEITON - Catalogue the necessary data in an organized and logical manner to help users easily find information.

  • Source of Truth: Specific data source that is considered the most reliable and accurate within an organization.

Why: It helps to ensure that the right data is used throughout the organization. Using a consistent and reliable source of data helps to improve data quality, reduce errors, and ensure that key decisions are made based on accurate information.

How to do it:

  1. Determine how this data is stored, how it is accessed, and who is responsible for maintaining it.
  2. Establish a clear set of data governance policies and procedures to ensure consistency and accuracy.
  3. Implement proper data validation and verification techniques to ensure that the data is correct and up-to-date.

SEITON - Product Family

  • Product Family: Describes a group of related products that share common manufacturing processes, materials, and characteristics for the purpose of streamlining production and improving efficiency.

Why: Grouping related products into families allows for streamlined planning, scheduling, and execution of manufacturing processes. The use of standardized materials and processes reduces variability in production and helps to minimize the potential for errors and quality issues.

How to do it:

  1. Identify the products that are included in your current product family.
  2. Analyze the manufacturing processes, materials, and characteristics that are common across this product family.
  3. Assess customer demand and market trends to determine if there are opportunities to expand or adjust the product family.
  4. Consider making adjustments to the size or makeup of your product family to optimize production processes, improve product quality, and meet the needs of your customers more effectively.

SEISO - Develop procedures for data creation and validation to ensure data is accurate and up-to-date.

Why: Ensuring new data is accurate, consistent, and useful is critical for decision-making, analysis, and reporting purposes across an organization.

Data Creation Procedure:

  1. Data validation - ensuring the data is accurate, consistent, and complete.
  2. Data formatting - formatting data to be consistent with the rest of the dataset.
  3. Data storage - establishing a centralized location for the storage of data to be easily accessible.
  4. Data entry - guidelines for how new data should be input into the system.
  5. Data security - establishing security protocols that ensure the confidentiality, integrity, and availability of the data.

SEIKETSU - Master Data Owner

  • Master Data Owner: A person or department within an organization who is accountable for the accuracy, consistency, and completeness of master data records.

Why: Responsible for ensuring the accuracy, consistency, and completeness of master data records, which is essential for effective decision-making and reporting.

Responsibilities:

  1. Setting data standards and policies: Define the standards and policies for data creation, validation, and maintenance.
  2. Monitoring data quality: Check the necessary new data, ensuring that the modification of data follows the data standards and policies.
  3. Maintaining data consistency: Ensure that data is consistent across the entire organization and that it aligns with business processes.
  4. Acting as a liaison: Collaborate with other departments, stakeholders, and vendors regarding the maintenance and exchange of data.

SEIKETSU - Product Management System

  • PDM: A software platform that manages product data and process information throughout the lifecycle of a product.

Functions of PDM:

  1. Centralized Data Management: Provides a centralized location where all product data can be stored, accessed, and managed by authorized personnel.
  2. Version Control: Tracks changes made to product data throughout its lifecycle.
  3. Change Management: Allows users to propose, review, and implement changes to product data.
  4. Collaboration: Facilitates collaboration between different teams involved in product development.
  5. Workflow Automation: Automates workflows such as approval processes and workflows for engineering change requests.

SHITSUKE - Build up the mindset

  • Mindset of Data Master: Building up the mindset to understand the importance of data master helps individuals recognize the value of accurate and consistent data in critical business functions.

Why:

  1. Helps individuals recognize the value of accurate and consistent data in critical business functions such as finance, operations, and customer engagement.
  2. Without the right mindset towards data master, individuals may not understand the impact of data quality issues on business performance.
  3. Developing the mindset towards data master will help individuals to recognize the importance of assigning accountability for data quality.
  4. A strong understanding of the importance of data master can help organizations to be proactive in identifying data quality issues.

Three Steps to Enhance the Mindset:

  1. Educate teams on data best practices and availability of the data.
  2. Build a data culture.
  3. Establish Data Governance Policies.

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How to Improve Main Data Management

SEIRI - Identify and remove any duplicate or unnecessary data, keeping only necessary data.

  • How to do it: Identify and remove any duplicate or unnecessary data, keeping only necessary data.
  • Why: Removing unnecessary data helps reduce confusion, improve efficiency, and makes finding, managing, and maintaining key data easier.

Steps：

  1. List all main data items, including product numbers, product attributes, and quality standards.
  2. Review the usage of these items through system log analysis.
  3. Review and redefine the definitions and purposes of the data.
  4. Remove unnecessary data and confirm with key stakeholders.

SEITON - Organize and catalog necessary data in a logical manner to help users easily find information.

  • True Source: The specific data source within the organization considered most reliable and accurate.

Why: It helps ensure that correct data is used throughout the organization. Using consistent and reliable data sources helps improve data quality, reduce errors, and ensures key decisions are based on accurate information.

How to do it：

  1. Determine how this data is stored, accessed, and who is responsible for maintenance.
  2. Establish a clear set of data governance policies and procedures to ensure consistency and accuracy.
  3. Implement appropriate data validation techniques to ensure the correctness and currency of the data.

SEITON - Product Series

  • Product Series: Describes a group of related products that share common manufacturing processes, materials, and characteristics to streamline production and improve efficiency.

Why: Grouping related products into series helps simplify planning, scheduling, and execution of the manufacturing process. Using standardized materials and processes helps reduce variability in production and assists in minimizing the possibility of errors and quality issues.

How to do it：

  1. Identify the products contained within current product series.
  2. Analyze common manufacturing processes, materials, and characteristics among these product series.
  3. Evaluate customer needs and market trends to determine if there are opportunities for extending or adjusting product series.
  4. Consider adjusting the size or composition of product series to optimize the production process, improve product quality, and more effectively meet customer needs.

SEISO - Establish data creation and verification procedures to ensure data accuracy and currency.

Why: Ensuring new data is accurate, consistent, and useful is crucial for decision-making, analysis, and reporting.

Data creation procedure：

  1. Data verification - Ensure data is accurate, consistent, and complete.
  2. Data formatting - Format data to be consistent with the rest of the dataset.
  3. Data storage - Establish a centralized location so that data can be easily accessed.
  4. Data entry - Guidelines on how new data should be entered into the system.
  5. Data security - Establish security protocols to ensure confidentiality, integrity, and availability of data.

SEIKETSU - Primary Data Owner

  • Primary Data Owner: The person or department within the organization responsible for the accuracy, consistency, and integrity of primary data records.

Why: They are responsible for ensuring the accuracy, consistency, and integrity of primary data records, which is crucial for effective decision-making and reporting.

Responsibilities：

  1. Set data standards and policies: Define the standards and policies for data creation, verification, and maintenance.
  2. Monitor data quality: Assess the necessity of new data and ensure that data modifications comply with data standards and policies.
  3. Maintain data consistency: Ensure that data across the entire organization is consistent and aligned with business processes.
  4. Act as a liaison: Collaborate with other departments, stakeholders, and suppliers to maintain and exchange data.

SEIKETSU - Product Management System

  • PDM: A software platform that manages product data and information about the entire product lifecycle process.

PDM Functions：

  1. Centralized data management: Provide a centralized location where all product data can be stored, accessed, and managed by authorized personnel.
  2. Version control: Track changes to product data throughout its lifecycle.
  3. Change management: Allow users to propose, review, and implement changes to product data.
  4. Collaboration: Facilitate collaboration between different teams during product development.
  5. Workflow automation: Automate workflows, such as approval processes and engineering change request workflows.

SHITSUKE - Establish the Right Mindset

  • Data Owner Mindset: Establishing the right mindset and understanding the importance of being a data owner helps individuals recognize the value of accurate and consistent data in critical business functions such as finance, operations, and customer engagement.

Why：

  1. Helps individuals recognize the value of accurate and consistent data in critical business functions such as finance, operations, and customer engagement.
  2. Without the correct data ownership mindset, individuals may fail to understand how data quality issues impact business performance.
  3. Developing a data ownership mindset helps individuals recognize the importance of assigning data quality responsibilities, which is crucial for maintaining accurate and reliable data.
  4. A deep understanding of the importance of data ownership enables organizations to proactively identify data quality issues and implement preventive measures.

Three Steps：

  1. Educate the team on data best practices and data availability.
  2. Establish a Data Culture.
  3. Develop Data Governance Policies.