Every business that builds complex products faces a common challenge: document management. Each design, manufacturing, and maintenance stage relies on its own form of documentation and databases specific to the tools and processes, siloing valuable product data into text, spreadsheets, CAD models, manufacturing plans, and others. Several issues can arise with this approach because none of these disconnected documents and data sources are interoperable. They are often inconsistent with each other and vary widely in accuracy and quality. In addition, correlating data from multiple sources takes lots of manual work, is error-prone, and still might not lead to usable or accurate insights.

However, today’s businesses can’t afford to silo their data. Because of growing customer demands, complex supply chains, and constantly evolving compliance requirements, enterprises need the ability to rapidly implement changes across the product lifecycle without the frustration of data inconsistency and manual work.

Instead of using isolated forms of static documentation, today’s businesses must move towards a data-centric approach: managing access to all data and their relationships from a centralized location and in context. This enables full product traceability and enables more effective processes of collaboration, innovation, scalability, configuration, change, and compliance for the entire organization.

Document-centric vs. data-centric

When you think about traditional product lifecycle management (PLM), managing documents effectively might be the first activity that comes to mind. However, the key to effective lifecycle management in today’s fast-paced manufacturing environments is to shift away from static documents to structured data models with open APIs. To see why breaking free of documents is critical for today’s enterprises, let’s take a look at the key differences between document-centric and data-centric approaches:

Document-centric approach

In a document-centric approach, information gets stored and managed in separate and mostly static documents. These documents are stored independently in formats like text files, PDFs, Word documents, flat graphic documents, and spreadsheets. The data within these documents is often unstructured or semi-structured, making it challenging to locate specific information, compare data from different documents, or convey the dependency of data between these documents.

To use the product data stored within documents, users must retrieve entire documents and search through them to find the specific information they need. This process is time-consuming, inefficient, error-prone, and devoid of context.

A document-centric approach also makes it challenging for teams to collaborate. It often forces teams to manually share information over email or team chats, further scattering valuable product data across the organization. In addition, many of these documents are static, meaning they won’t reflect the latest changes and will perpetuate inaccurate information.

While this approach might have worked in the early days of PLM, the complexity of the typical product lifecycle in today’s document-centric approach causes even more of a disconnected document sprawl, data inconsistency, and a lack of coordination between teams.

Data-centric approach

On the other hand, a data-centric approach focuses on storing and managing information centrally, with fully traceable structured data models between the models and their elements and managed in the context of the product’s lifecycle. Pieces of data such as supplier records, inventory, financial information, parts, and sensor data are independent elements that team members can easily query and manipulate. This approach typically leverages relational databases with open APIs.

This strategy enables teams to use queries and quickly retrieve specific information in context efficiently, and without avoidable human errors. It also allows users to trace dependencies between elements that otherwise could be missed. Finally, it allows efficient data analysis and reporting, and generation of interactive (dashboards) as well as static documents (external reports).

5 benefits of a data-centric approach

A data-centric approach improves several areas of product lifecycle management activities, including:

1. Efficiency in data retrieval and analysis

Data-centric platforms enable teams to retrieve and analyze data much faster. Team members can use queries to quickly pull specific information from large, structured datasets, saving time and improving efficiency.

Since all data is accessible from a central location and traceable across all data, it’s far more accurate and up to date than siloed documents. Changes in data are automatically reflected across all integrated applications, reducing redundancy and errors.

2. Improved decision-making

Data-centric platforms enable real-time data processing and analytics, allowing businesses to make informed decisions based on the latest information. Rather than parsing through separate documents, teams can easily integrate their data-centric platform with analytics tools and business intelligence platforms.

3. Scalability and flexibility

A data-centric approach sets a solid foundation for future business growth. Data-centric platforms are designed to handle large volumes of data and can scale efficiently as the amount of data grows. Some of these platforms offer the same functionality on-premises or in the cloud, further enhancing scalability and lowering the cost of ownership. Open APIs facilitate the reuse of data across different applications and platforms, making every piece of data more useful for the organization’s growth.

4. Compliance and security

A data-centric approach sets a solid foundation for future business growth. Data-centric platforms are designed to handle large volumes of data and can scale efficiently as the amount of data grows. Some of these platforms offer the same functionality on-premises or in the cloud, further enhancing scalability and lowering the cost of ownership. Open APIs facilitate the reuse of data across different applications and platforms, making every piece of data more useful for the organization’s growth.

In addition, it’s far easier to secure a centralized platform than several isolated documents scattered across your organization. You can implement robust security measures around your data-centric platform to protect sensitive data from unauthorized access and breaches.

5. Innovation and competitive advantage

Using a data-centric approach prepares your organization for innovation. A data-centric approach with open API supports the integration of advanced technologies like artificial intelligence and machine learning, which rely on structured data for training and optimization.

Companies that leverage data-centric platforms also gain a competitive edge, as centralized access to all product information can optimize operations, enhance customer experiences, and identify new market opportunities.

6. A robust and flexible digital thread

Using a data-centric approach is fundamental to modeling and managing an effective and centrally managed digital thread. This is because relationships between various structured data models and their elements are defined and managed in the digital thread. In addition, the digital thread allows for managing the relationships’ lifecycle, which is critical to defining the context of traceability.

Nissan: A real-world story of data-centric success

Nissan Motor is an excellent example of how a data-centric approach can work in the real world. When Nissan began manufacturing electric cars, it had to deal with many complex, in-vehicle software variants. The teams found themselves manually copy-pasting data between various systems and losing critical information. This manual work led to errors and inconsistencies.

To overcome these challenges, Nissan adopted a single continuum for tracking digital assets along with software features throughout the product lifecycle. Because of this centralized, data-centric platform, the Nissan team can quickly identify how a single change in the product’s software-driven functionality will affect software variants across the family of electric vehicles. The centrally managed digital thread also eliminates the need to copy-paste between different data repositories, minimizing errors and wasted time. Overall, this new approach to data management enables the Nissan team to easily manage and reuse complex software variants, preparing the enterprise for future growth and success.

Facilitating a data-centric approach with Aras Innovator

Aras Innovator®, our customizable, cloud-based PLM platform, has a proven architecture for building an extendable and flexible digital thread using structured data. The platform includes an open API and solutions for modeling, populating, and synchronizing its structured data with data from stand-alone external tools, static documents, and other data management solutions.
We support a data-centric and digital thread-managed approach with:

  • An extensible data model with open APIs to connect all PLM services to a central digital backbone
  • Low-code development and DevOps features for agile development of new applications for your PLM environment
  • Advanced digital twin capabilities to manage product and asset changes in real-time
  • Centralized data management for proving compliance and creating audit trails

See a data-centric approach in action by watching our on-demand presentation, More Freedom For Your Product Data.