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Why You Need a Supply Chain Data Governance Framework

What is supply chain data governance?

Supply chain data governance manages and regulates data shared between organizations and vendors in a supply chain. In practical terms, it deals with questions relating to such considerations as what information is required, who owns the information, who is responsible for its accuracy, and who is able to access, view or modify information and under what circumstances. In concrete terms, the process establishes guidelines, procedures and techniques for collecting, storing and exchanging information about suppliers in the form of agreed, documented policy.

Supply chain data governance aims to ensure that every aspect of data in the supply chain is accurate, provided in full and consistent. It also ensures full compliance with relevant laws, regulations and standards.

Having robust data governance structures in place enhances an organization’s supply chain efficiency and effectiveness, minimizes risk and uncertainty and empowers vital stakeholders by providing them with everything required for better decision-making.

To achieve successful supply chain data governance, collaboration and coordination among all stakeholders in the supply chain, including suppliers, manufacturers, distributors and retailers, are essential.

Additionally, leveraging technology tools and platforms is necessary to facilitate data sharing and analysis across the supply chain.

What are the aims of data governance?

Organizations should articulate the core aims of their data governance frameworks before defining the frameworks themselves. These discussions should include all key stakeholders involved in each individual process, so the requirements of each are met in full.

The following provides an overview of the main aims of a data governance framework:

Data quality

Enhanced data quality sits at the heart of a data governance framework, as it ensures that organizations can assess data continuously and that stringent standards are followed. It enables organizations to avoid the perils associated with incomplete and inconsistent data.

Data classification

Implementing a data governance framework with the associated definitions around data classification ensures that organizations can understand and identify the various types of data they have and how best to utilize it. It ensures that erroneous and inconsistent data can be easily found, and that appropriate data can be provided with the required level of protection while having a holistic view of the data lifecycle.

Data sharing & regulatory compliance

Organizations must be able to access data across various applications that are shared between an array of stakeholders. Data governance frameworks ensure this is possible, while providing accountability.

This also helps ensure that regulatory compliance is considered and can be tightly adhered to across the organization.

What are the benefits of a data governance framework?

There are numerous advantages to organizations in implementing a data governance framework in line with industry best practices.

On a practical level alone, having these policies in place makes the training of employees easier, quicker and means that the language is the same across the business. Further benefits include:

Enhanced data quality

The key benefit of a data governance framework is that the organization’s supply chain data is accurate and consistent. This avoids doubts during decision-making processes, meaning organizations can react quickly and adjust techniques and strategies through data utilization. 

Better informed decision making

This assured confidence in the overall consistency and trustworthiness of the organization’s data empowers critical stakeholders across the business to make better informed decisions. This includes those related to inventory management, demand planning, and logistics, as well as many other associated areas.

Gain competitive advantages

When carefully planned and implemented, better-informed decisions resulting from having a data governance framework positively impact the organization and can ensure a clear and distinct advantage over competitors.

Minimization of risks

Investment in effective data governance ensures that supply chain risks are minimized. This helps avoid disruptions in the supply chain, including compliance-related issues and security breaches.

Protection of data assets

The organization’s data assets are fully protected as they are only accessible to those authorized to access the data. There is also the added benefit that the data is backed up and disaster recovery plans can be designed and implemented in case of technical system issues.

What are the four pillars of data governance?

Four pillars play a crucial role in the success of any data governance initiative:

• Data stewardship
• Data quality
• Master data management
• User cases

Data stewardship

Data stewardship is a fundamental aspect of any data governance policy. This is because it involves defining who the owners and custodians of information are within the organization.

These stewardship teams will often include database administrators, business analysts and other business personnel familiar with the organization’s data practices.

Data quality

Improving the quality of an organization’s data is usually one of the main reasons behind adopting and implementing data governance. The aim is to achieve accurate, complete and consistent data across the organization, including ensuring the data is thoroughly cleansed to remove errors and duplication.

Master data management

Master Data Management is a data management practice executed closely alongside data governance. By utilizing supplier data management software, organizations can establish a consistent master data set for customers, suppliers, products, and other business entities across the organization.

The complexity of Master Data Management has limited its adoption, but smaller-scale projects driven by data governance are increasing in popularity.

Use cases

Data governance is critical for managing organizations’ operational systems and digital transformation initiatives. It also supports other corporate processes, such as risk management, business process management and mergers and acquisitions.

Data governance will become increasingly important as data usage grows and new technologies emerge. Additionally, privacy protections are now central to data governance due to high-profile data breaches and regulations like GDPR.

How to create a data governance framework

For any data governance project to be successful, data managers and stewards need to agree on clear definitions that will be used across the organization. Organizations must ensure that any data governance framework can remain flexible to meet the complexity of their data and the ever-changing technological landscape.

There are, however, some core principles that must be followed in the creation of a robust, yet agile, data governance framework.

Identify assets & processes

When creating a data governance framework, the first consideration is identifying data, processes and relevant timelines. This establishes a vital foundation while outlining where data is stored and the different methods and departments within the organization that require the data.

Create context

Ensuring an internal business glossary is defined and created to be agile is critical when creating a data governance framework. This should include determining data terms, rules, policies and descriptions related to data.

The value of data governance lies in quickly defining and managing business data assets that align with the organization’s unique needs. This includes data sharing agreements, compliance tagging, data classification schemes and metrics that help increase visibility and understanding of the organization’s data assets.

Analyze data quality

Organizations must understand the quality of their data to ensure they have the confidence to gain insights that can be executed across the business.

All stakeholders involved in the processes need to have access to defined data quality rules so that any discrepancies are identified upon analysis. As part of the framework, quality scores should be defined and used to ensure that those within the organization have access to data of the highest quality for their particular need.

Embrace automation

Embracing automation must be a key consideration when creating a data governance framework. It ensures that the framework remains accurate and up to date and ensures that each stakeholder can maintain their confidence in it.

In summary

Adopting a supply chain data governance framework is essential for organizations to optimize operations, streamline decision-making processes and maintain a competitive edge in a data-driven business environment. The approach fosters improved data quality, ensuring accurate and timely insights that drive strategic growth.

_{Article Updated November 2023}