Many manufacturing companies are asking, “What is FAIR data,” because many still struggle to get the insights they need out of the organization's data. It's not uncommon for employees to manually sift through high volume data in various formats across multiple disparate systems to find the relevant info they need to make decisions. And, we know of more than a few manufacturers that are keeping old systems from retiring because of regulatory requirements that require them to access that data. Pharmaceutical manufacturing companies in particular find it difficult to get their arms around all their data from disparate systems. However, the problem isn't limited to just that sector: automotive manufacturing, continuous flow, food and beverage, and many other industries continue to express how hard it is to use structured, semi-structured, and unstructured data from a variety of sources. Businesses from nearly every manufacturing sector are looking at data hub solutions to corral data from a variety of sources that includes measurements, text, images, social media, time-stamped, machine data, spatial-temporal (time/location data), operational data, and much more.
If your company struggles to capture insights from disparate systems, it may be time to explore FAIR data, and the implications for manufacturing companies looking to thrive in a data-driven world. FAIR data, which stands for Findable, Accessible, Interoperable, and Reusable, ensures that data is easily discoverable, accessible, and usable across different platforms and applications. By leveraging FAIR data principles, your organization can promote data sharing, transparency, and collaboration, leading to faster, better decision-making and innovative solutions. In today's competitive market, where data is perhaps one of your company's most valuable assets, embracing FAIR data practices can revolutionize operations, drive growth, and enhance the overall efficiency of organizations.
What is FAIR Data?
If you want to learn more about how FAIR data principles and a FAIR data platform can help your manufacturing company, watch the video to learn why companies look to FAIR data principles for faster, better decision-making.
Anyone asking, “What is FAIR data,” should look to the underlying principles: Findable, Accessible, Interoperable, and Reusable data.
What does “Findable Data” mean?
Findable data is a crucial aspect of FAIR data principles as it lays the foundation for effective data utilization. In the context of FAIR data, the findability of data ensures that information is easily located and accessed by relevant stakeholders within an organization. "Findable" data practices means that manufacturing companies can streamline their data retrieval processes, reduce the time spent searching for information, and enhance overall operational efficiency.
What does “Accessible Data” mean?
Accessible data ensures that information is easily retrievable and usable across various platforms and applications. For manufacturing companies, having accessible data means streamlining data retrieval processes, reducing search time, and enhancing overall operational efficiency.
What does “Interoperable Data” mean?
Interoperable data means that information can be seamlessly shared and utilized across different platforms and systems. By adopting interoperable data practices, organizations can break down data silos, facilitate knowledge sharing, and drive innovation.
What does “Reusable Data” mean?
When data is reusable across various platforms and applications, it allows for the seamless sharing and utilization of information, regardless of use case.
Impact of FAIR Data on Manufacturing Companies
FAIR data principles ensure that critical information is readily available for manufacturing processes, while enhanced data quality guarantees accuracy and reliability. Compliance with regulatory considerations and the establishment of robust data governance frameworks align with industry standards, promoting efficient data management. Moreover, leveraging FAIR data for innovation and growth empowers companies to make data-driven decisions, accelerate research and development, and drive competitiveness. Implementing FAIR data practices in manufacturing involves creating structured data management systems, educating employees on data best practices, and fostering a data-driven culture. In a digital age where data is a key asset, FAIR data principles revolutionize manufacturing operations, paving the way for sustainable growth and success.
To successfully adopt and integrate FAIR data principles in a manufacturing company, establishing structured data management frameworks is essential. This allows for compliance with FAIR data standards and ensures that critical information is readily available for manufacturing processes. Additionally, investing in a proven FAIR data platform can enable your organization to eliminate manual data cleansing and normalization with spreadsheets.
Next Steps and Recommendations for FAIR Data in Manufacturing
If your organization is eager to equip employees for better, faster decision-making, you can perform a quick evaluation with a data hub solution. At minimum, the solution should conform to FAIR data principles, allow teams to eliminate manual data manipulation, and handle high-volume data in various formats across multiple disparate systems.
Our digital transformation advisors recommend building a business case to help your management team understand why a data hub solution can help the organization corral data from a variety of disparate sources to leverage structured, semi-structured, and unstructured data. If you need help building a business case for executive management consideration, we can help you:
- Articulate the business problem that the organization wants to solve (instead of focusing on technology)
- Align the business problem with strategic or operational objectives
- Quantify the problem in terms of what the company will gain if it invests in the change, what it will lose or cost if it does not make a change, and what the cost of delay might be (can be financial, operational, customer service-related, or other strategic metrics)
- Enlist input and support from other functional areas (including manufacturing operations, R&D, supply chain, scheduling, IT, and others)
- Document what the organization currently does (approved business processes and potentially “hidden” manual processes)
- Illustrate how the organization will do things differently in the future
- Identify and address potential roadblocks
