Supply Chain workflows in large enterprises can be extremely complex with many moving parts and several human-centric tasks and convoluted processes that are dependent on various functions, departments, and teams.
This degree of complexity makes traceability and trackability quite tricky, which is why organizations often rely on ERP systems and custom-implementations to enhance workflows using technology-centric processes.
ERP Systems built by SAP, Oracle, Infor & Microsoft also include specific modules for supply chain management, with many of them offering specialized modules for manufacturing, logistics and distribution.
What is Data Governance and How Does it Help?
The idea of a golden record, a single source of truth, so to speak emerged sometime in the early 2000s. Since then, the master data concept has been heavily adopted.
Mainly due to the various benefits of centrally managing and maintaining data records,
master data ensures that a single org-wide record will capture all the necessary information and details of a given entity. The type of master data will depend on the entity, which can include:
- Materials or MRO Parts & Spares: Core raw materials, consumables, and spare parts essential for manufacturing and maintenance operations.
- Vendors or Suppliers: Entities that provide goods or services, including manufacturers, distributors, and service providers.
- Customers or Distributors: Businesses or individuals who purchase products or services, including resellers and end-users.
- Employees: Workforce-related records covering personal details, roles, payroll, and organizational hierarchy.
- Products: Finished goods or services offered to customers, including SKUs, configurations, and specifications.
- Equipment: Physical assets used in operations, including machinery, tools, and infrastructure with maintenance tracking.
- Services: Intangible offerings such as consulting, maintenance, or IT support, often linked to contracts or work orders.
These systems were originally built to manage data and ensure real-time insights at a plant, location, function or even at an equipment level.
However, enterprises quickly realized that the processes and technologies were only as good as the data that resides within these source systems.
The Need for Data Governance
As operations began scaling at companies that implemented these systems, the data quality invariably began deteriorating, which invariably led to a multitude of issues as detailed below:
Duplicate Records
With unclear access controls and a lack of context, data stewards and supply chain procurement professionals generally have unfettered access to request for materials, spare parts, consumables and even onboarding of suppliers. A clean master record for each domain is utterly necessary. Here is a list of vendors providing supplier master data software solutions.
Due to a near absence of governance controls, this would invariably lead to the creation of duplicate records.
However, sourcing teams generally act on such requests and in most cases, these duplicates would lead to mismanaged procurements and overstocking of materials, consumables, MRO parts, and even multiple vendors across plants and facilities.
Research report published by McKinsey details that inventory overstocking costs businesses upwards of $1.1 Trillion annually, including inventory carrying costs, damages due to improper storage, etc.
Maverick Spend
One of the main issues with an ungoverned master database is the conflict of information across multiple records. These inconsistencies can paint an inaccurate picture of the actual state of the master data.
This can inadvertently lead supply chain teams, maintenance crews, and data management specialists to believe that a certain part, raw material, or consumable is available at a given production facility, when the required part is different from the one recorded and maintained in the system.
Unfortunately, by the time the teams realize this inconsistency, it is generally too late to prevent significant damage and leads to a phenomenon known as “Maverick Spend”.
Simply put, maverick spend occurs when teams scramble to make last-minute, urgent procurements, and due to the unplanned nature of the spend, end up making unjustified or frivolous purchases outside pre-approved procurement channels for what would otherwise be a simple, straightforward, and relatively low-cost purchase.
A recent study by WBR Insights and SDI revealed that nearly 91% of procurement leaders viewed Maverick Spend as a challenge, with 39% seeing it as very significant. [Source]
Production Downtime
Due to the reasons detailed above, it’s easy to see how the absence of any given consumable, part, or raw material can result in a complete halt in production activity. This can be detrimental to the entire manufacturing supply chain and such stalls can have a spillover effect on other aspects of production.
A 2016 study by IBM found that poor data quality strips $3.1 Trillion from the US economy annually due to lower productivity and higher maintenance costs.
Many of the challenges discussed above can be minimized by addressing data quality concerns at the source itself with thoroughly thought-out data governance processes and supported by modern technology solutions like autonomous retrieval, information processing, and Artificial Intelligence.
Prevention of Repetitive Cleansing
Regardless of whether a governance system is in place, the data quality would still erode and it’s only a matter of time before the relevant teams realize this issue. What typically ensues is a rather expensive data cleansing exercise—the objective of which is to deduplicate, restructure, and complete the master records.
However, a cleansing exercise is resource intensive, human-driven, and can be expensive, especially if done frequently. A well-planned governance system would prevent these issues from cropping up in the first place, thus minimizing the need for repetitive cleansing, leading to significant cost savings for the company.
Analytics & Insights
Large, complex organizations with diverse supply chains leverage ERP, EAM, & Master Data and the reports and analyses therein to periodically review and develop insights into consumption, production, efficiency, spending, and resource allocation. The insights developed are only as good as the source information and this underscores the need for data governance to ensure the continual sanity of data.
Supply Chain Data Governance Strategies
After supply chain teams conclude that data quality across their ERP, EAM, and master data are problematic, the generally accepted route to resolving this entails:
- A sample assessment across a sample dataset;
- A detailed cleansing exercise that spans all their Master Data Modules;
- Implementing a data governance strategy after a detailed understanding of the process, org hierarchies, and industry-specific requirements.
We will be focusing primarily on strategies pertaining to #3.
Data Enrichment
A lack of discipline and structure at the time of making entries and requests is one of the main challenges that data governance aims to resolve. This lack of discipline leads to incomplete or absent information for any given data master record.
In the case of Customer Master, this could be the person’s address. In the case of the Material Master, this could be the manufacturer name or part number, and in the case of supplier master data software [learn more] this could be tax information details like TIN number or DUNS number.
The point is that all master data records require a comprehensive set of details that should ideally be updated by the “requestor” or the “data steward”.
However, this is rarely the case due to unavailability of information, human-error, and lack of supporting technology.
Within the last decade, however, autonomous crawlers, retrieval systems, and lately, Artificial Intelligence technologies can autonomously find, extract, and update the necessary details for a Master Data Record.
The source of enrichment can be first-party data sources, a collection of third-party catalogues, and even the open web.
The best part is that specifically-trained AI models can retrieve this information from unstructured sources and the source data architecture need not necessarily follow a predefined format.
Data Democratization and Controlled User Access
Modern organizations are shifting towards data democratization—enabling employees across departments to access and leverage data for informed decision making. However, democratization must be balanced with security controls.
Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) help define who can access what data while maintaining governance integrity. Implementing self-service analytics platforms with embedded governance ensures data is utilized responsibly without compromising security.
This approach fosters innovation, accelerates decision making, and reduces dependency on IT teams while maintaining data consistency and compliance.
Embedded Data Quality Frameworks
Data governance is no longer a separate function but an embedded process within data workflows. Companies are integrating data quality checks at ingestion, transformation, and consumption stages to maintain high data reliability.
Implementing automated data validation, deduplication, and enrichment mechanisms ensures that only high quality, consistent, and accurate data is used for analytics and business operations.
A proactive data quality framework helps organizations prevent costly errors, enhances operational efficiency, and builds trust in data-driven insights.
AI-Driven Data Governance
While we’ve already covered the role that Artificial intelligence plays with respect to enrichment, data governance and data management in general is evolving rapidly with the rise of AI-first systems.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming data governance by automating data classification, anomaly detection, and policy enforcement. AI can proactively identify data quality issues, recommend governance actions, and streamline compliance processes. For instance, AI-driven metadata management helps categorize and track data lineage efficiently.
Automated governance reduces human errors, improves decision making, and ensures that policies are consistently applied across large data ecosystems. Additionally, AI enhances security by detecting unauthorized access patterns and potential data breaches.
Final Thoughts
Organizations implementing AI-driven governance benefit from higher efficiency, reduced operational risks, and improved compliance with evolving regulations.
