A Complete Guide to Data Governance in Manufacturing
Data powers manufacturing – From driving more efficient and effective collaboration among manufacturers, suppliers, and distributors to improving customer experiences and monitoring environmental impact and supplier performance, data powers the decisions manufacturers make every day.
However, manufacturers often struggle to realize value from their data. Plagued by challenges related to climate change and supply chain disruptions, many manufacturers fail to capitalize on the immense value their data holds. In fact, according to a survey from the World Economic Forum, in collaboration with Boston Consulting Group, only 39% of manufacturers have successfully scaled data-driven use cases beyond a single value stream.
For manufacturers to excel, they must take control of their data and use it to overcome the challenges preventing them from realizing greater efficiency and excellence – They can do so by implementing data governance.
The Importance of Data Governance in Manufacturing
Data governance in manufacturing is no longer an option: It’s a strategic need that not only safeguards an organization’s data assets, but also reveals new opportunities for growth, efficiency, and competitiveness.
Data governance leads manufacturers to new levels of sustainability, profitability, and efficiency. When their data is governed, manufacturers gain insights, drive data-driven decisions, and identify innovation opportunities that optimize inventory, increase collaboration with suppliers and distributors, safeguard customers, and protect the planet.
Using data governance, manufacturers can overcome persistent challenges stemming from:
- Poor data quality
- Data security and privacy concerns
- Compliance with regulatory mandates
- Difficulties sharing data with suppliers and distributors
Implementing data governance requires manufacturers to establish a framework that includes best practices, data quality standards, compliance measures, key performance indicators (KPIs), and defined organizational responsibilities. Doing so will give manufacturers the structure and guidance needed to launch a successful data governance initiative.
KPIs for Measuring Manufacturing Data Governance
Before embarking on a manufacturing data governance initiative, it’s imperative that manufacturers establish KPIs. Not only will this enable manufacturers to take stock of their current state, but it will also motivate them to set goals for how they want to improve their data – and their operations – in the future.
The following KPIs are a good place to start when initiating a data governance program:
- Data quality metrics: It’s important for manufacturers to not just understand the full picture of their data’s quality, but also the journey of that data and its changes over time. By revealing where data is incomplete, outdated, duplicative, incorrect, or trapped in a silo, manufacturers can prioritize their governance program to focus on the data that require the most attention, helping them to improve its accuracy, reliability, and relevance.
- Compliance and regulatory metrics: Regulatory metrics, such as those related to data privacy, cybersecurity, and environmental regulations expose potential vulnerabilities so manufacturers can quickly take action and resolve the issues before facing financial penalties or reputational damage. Compliance metrics also shed light on employee adherence to internal procedures and controls, another important measure to monitor for compliance.
- Production efficiency metrics: As manufacturers look to become more efficient, it’s important that they identify opportunities to streamline operations, reduce costs, enhance product quality, and increase productivity. By understanding critical metrics such as overall equipment effectiveness (OEE), cycle time (including downtime), yield rate, throughput, scrap and rework rate, and labor productivity, manufacturers can spot and correct inefficiencies in their production line and correct them before they cause disruption.
- Supply chain metrics: Monitoring supply chain metrics related to delivery and fulfillment times, costs, environmental impact, supplier performance, and vulnerabilities associated with geopolitical, economic, or natural disaster risks is critical. Doing so enables manufacturers to optimize and enhance supply chain efficiency, reduce costs, and avoid the supply chain breakdowns experienced in recent years.
- User adoption and engagement metrics: By understanding where a data governance program is successful (and where it is not!) manufacturers can prioritize where to invest more time and energy to win over skeptics. Understanding adoption and engagement also sheds light on which data sets thes business uses and updates frequently as well as those they rarely access.
- Data governance maturity metrics: The more mature an organization’s data governance practices become, the better they are at managing data, ensuring its quality, and complying with regulations. Data governance maturity considers how well users adopt the tools and technologies implemented to support data governance. In addition, maturity metrics measure higher-level benefits such as the ability to mitigate risk, improve collaboration with suppliers and distributors, and avoid fines or reputational harm.
Best Practices for Implementing Data Governance in Manufacturing
When it comes to getting started with data governance, we recommend that manufacturers follow these six steps:
1. Determine organizational readiness and needs
Start by conducting a thorough assessment of the organization’s current data management practices, processes, and culture. Then, identify and document gaps, challenges, and areas that need improvement. This assessment will surface the most critical needs for the organization, helping manufacturers to prioritize the implementation plan and demonstrate quick wins and ROI. For some manufacturers, a quick win means reducing cycle and downtimes, while for others it could be reducing environmental impact.
2. Design and develop a data governance framework
Utilize data governance frameworks to define the processes and standards for how the organization classifies, manages, and stores data. This is a critical step to drive greater consistency and alignment across the organization, as well as to ensure compliance with regulations such as GDPR, CCPA, and others.
3. Define the roles, responsibilities, and decision-making processes
Consider appointing new roles such as data stewards and data owners, and create a data governance council to oversee the initiative. Finally, plan for change management including the communications, training plans, coaching, and support that stakeholders will need for the initiative to be a success.
4. Adopt technology to support data governance practices
Take control of data and accelerate data governance success by implementing a data knowledge catalog. By providing a one-stop shop with a customizable repository for storing accurate, up-to-date data, manufacturers can make better decisions, promote sustainability and profitability, and remain competitive. The best data knowledge catalogs include the key capabilities needed to support data governance including a Business Glossary, data lineage tracking, data visualization tools, and natural language search.
5. Get key stakeholders on board
Secure buy-in from stakeholders and provide clear and consistent communication across the business – Doing so fosters greater collaboration and trust across the organization and ensures that executives, business leaders, business users, and IT are informed on the initiative and prepared for the policy, process, and technology changes coming their way.
6. Monitor progress and make adjustments
Review metrics and KPIs to determine where and how to improve governance programs. Additionally, it’s important to consider feedback, evolving business needs, and regulatory changes in order to assess how to refine governance programs for greater business impact. By embracing a culture of continuous improvement, manufacturers can ensure their data governance initiatives remain relevant and continue to positively impact the business.
Data governance helps manufacturers take control of their data so they can achieve new levels of excellence by revealing new opportunities to increase efficiency, enhance productivity, and grow the business. And today, it’s no longer a choice. It’s a strategic necessity.