Best Practice

Orchestrating a Data Governance Strategy in Manufacturing

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Manufacturing processes

As manufacturing processes become increasingly digitized, companies in this sector face new data-related challenges, from security concerns to quality assurance, that must be addressed with end-to-end management. The data needed to support a single manufacturing plant for a day is staggering.

Furthermore, implementing a data governance strategy is essential for organizations to ensure the security and quality of their data. Data governance refers to the processes that manage the availability, usability, and integrity of the data within an organization.

If manufacturing companies adopt data governance policies, it can greatly improve the efficiency of their general operations and production processes.

Challenges with Data

Adopting a robust data governance model enables accessibility, confidence, and understanding within the organization. There are many elements to building a comprehensive governance approach.

Data governance in the manufacturing industry can be particularly challenging due to the complexity of the data used. Manufacturing processes across the value chain require structured and unstructured datasets from different sources in different formats and volumes. These distinct sources are often siloed systems not designed to share data. This creates organizational problems that can hinder effective data implementation. Moreover, sourcing multi-format data from multiple outlets introduces security and storage concerns for the manufacturing industry.

Collected data is only valuable if effectively used in decision-making and operational analysis. The quality of data collection and management directly affects any data-driven output's quality. Manual data entry, data silos, and collection from disparate data sources are all prevalent practices in the manufacturing industry that limit the quality, and thus the usability, of collected data. As such, companies must rectify these practices to ensure that their data is accurate, up-to-date, and complete.

As manufacturing relies increasingly on digital tools, companies face new concerns regarding malicious activities from external and internal actors. Organizations must prevent unauthorized access and manipulation of their data to protect sensitive information and intellectual property. However, if their data is not readily accessible, operations will suffer setbacks. Therefore, balancing data agility with data security is a top priority for this industry.

Data Governance Strategies

The best way to address data-related challenges is to implement a comprehensive data governance framework that outlines the processes, policies, and practices used to manage and secure a company’s data. Establishing a framework for manufacturing should take into account existing policies and processes introduced previously to oversee Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), Product Lifecycle Management (PLM), and other enterprise systems. A comprehensive framework ensures a consistent approach across all these while laying the groundwork for establishing a data-driven organization and culture.9 critical practices should be considered when establishing a formal data governance strategy in manufacturing.

Data Stewardship

Determine who will be accountable for managing and maintaining the data. This can include roles like data stewards, owners, and custodians. It's essential to have clear roles and responsibilities defined for each of these roles to ensure accountability for data management.

Data Policies

Establish organizational policies and guidelines for data use, storage, and management. This includes data retention policies, data privacy policies, data access policies, and data security policies. It also includes policies related to data sharing with vendors, distributors, dealers, B2B or B2C commerce sites, and manufacturers’ reps. The policies may also include direct guidance on sources of truth for data related to finance, inventory, bills of material, engineering specifications, etc.

Data Classification

Develop a data classification scheme to identify the most critical data to the organization and assign appropriate levels of protection, access, and retention based on the classification.

Data Quality

Develop processes and procedures to ensure data quality, including data cleansing, data validation, and data reconciliation.

Data Integration

Define processes for exposing data from disparate sources into an enterprise data lake. The data lake, different from a data warehouse, is a repository for storing and organizing data that becomes most valuable when combined with other sources or new business use cases. This includes identifying and cataloging data sources, defining data mapping rules, and establishing data staging, transformation, and loading procedures.

Data Governance Committee

Establish a data governance committee to oversee the data governance program, provide guidance, and make decisions about data management policies and procedures.

Training and Awareness

Train employees on data governance policies and procedures, as well as the importance of data quality, security, and privacy.

Continuous Monitoring

Establish real-time, automated processes for monitoring data usage and quality. Traditional methods of periodic audits are essential to provide oversight; however, realizing the benefit of fast-moving data requires that anomalies and errors are discovered and remediated quickly.

Regulatory Compliance

Ensure the data governance program complies with relevant regulations and industry standards, such as GDPR, CCPA, HIPAA, and ISO 27001.

Building Technologies, Data Governance and Oversight

To build a program that supports these 9 practices, manufacturers should consider investing in data management and security technologies, such as data catalogs, data quality management solutions, and data security solutions. These technologies ensure the data's practical storage, management, and security. With the right tools, an effective management scheme can be successful.

Proper oversight is also needed for data governance to succeed. Companies should ensure that their data governance processes are regularly monitored and reviewed. This certifies that each aspect of the data governance framework functions well and helps quickly identify and address any issues. Monitoring solutions should include automated processes, such as automated data governance dashboards.

Conclusion

The manufacturing industry continues to evolve towards a digitized data landscape, presenting manufacturing companies with many new challenges. Manufacturers can overcome these challenges by implementing a comprehensive data governance framework, investing in the necessary data management and security technologies, and regularly reviewing the data governance process.