The power of a common data ontology

A call to a model-based enterprise

In today’s dynamic market environment, manufacturers are encountering increasing demands to lower product costs, manage complexity and achieve rapid delivery—while upholding product quality and reliability. While many legacy manufacturers are familiar with these challenges in hardware development, the increased demand for smart devices and mechatronic systems has emphasized the significance of software throughout the product life cycle. 

This increased focus on software-defined products has resolved some challenges (for example, rapid feature releases through over-the-air updates), but also introduced new issues (for example, alignment of hardware and software development processes). 

To effectively address these challenges, sustain their competitive advantage, and grow their market share, organizations on the cutting edge are adopting a model-based enterprise (MBE) approach—underpinned by a common data ontology to optimize operations and enhance data-driven decision-making. At its core, MBE leverages digital models to drive business processes and aid decision-making. 

However, to ensure the full realization of benefits from adopting model-based practices, we've found that organizations need to establish a backbone digital thread that connects data and systems across the enterprise. 

Successful instantiation of the digital thread throughout the enterprise is dependent upon establishing a common data ontology. This interconnected framework allows valuable data to flow seamlessly between systems and disciplines, empowering business leaders to make informed decisions for their companies.

Defining a common data ontology and its value

Fundamentally, a well-defined data ontology serves as a companywide, standard digital language that enables easy information sharing and collaboration. It ensures the data is standardized in description and labels, interoperable across systems, clear, easy to access and of high quality. 

All too often, unique, tool-specific data representations have created roadblocks to interoperability, reporting, analytics, and artificial intelligence (AI). However, with a common data ontology in place, these barriers are removed—enhancing scalability, reducing complexity and promoting interoperability through seamless data exchange across diverse systems.

When done correctly, a digital thread ensures a seamless data flow across disciplines within a product's life cycle—integrating disparate systems, tools and processes, none of which could be done without an ontology as the foundation to build and connect upon. This integration allows domain experts to utilize models from other business silos without needing familiarity with their internal schemas. Moreover, the digital thread is more than just about pulling models from different functional areas. It drives insights and analytics that would not be possible without connecting data between functions. Ultimately, the digital thread enables the connection of requirements, system and geometrical models, software code and test results to create a matrixed view that allows engineers to quickly verify requirements and gain a more holistic perspective of the product development life cycle.

Additionally, a common data ontology enhances the portability of people and programs across an organization, enabling global collaboration and smooth cross-functional integration. It connects multiple sources of truth—ensuring data accuracy and consistency, which builds trust among stakeholders. Improved traceability provides a clear framework for tracking data across the product life cycle, meeting regulatory requirements and ensuring compliance.

Powering a digital thread through a common data ontology

Implementing a digital thread powered by a common data ontology requires careful planning and consideration. Assessing the current state to identify enterprise opportunities is the first step. Evaluating current data management and integration practices helps identify gaps, duplications, inefficiencies and opportunities for improvement.

When defining its common data ontology, an organization should start by identifying key data elements, their attributes and relationships. Establishing governance processes to keep the ontology up to date and relevant is also critical, while precise language is essential for reducing errors. Formalizing semantics in an ontology ensures every word has a single unambiguous meaning, enhancing productivity.

Once the data inventory is nearing completion, there is a noticeable shift to process mapping and value realization. Mapping out key processes that will benefit from the digital thread and identifying the data elements and relationships that need to be captured ensures alignment with business needs. When it comes to solutioning, the "hub and spoke" model proves effective, wherein the hub serves as a central repository of standardized data, and the spokes represent the various tools and systems that interact with the hub. This approach ensures seamless data flow, promoting consistency and interoperability.

Lastly, transforming a traditional organization into a model-based enterprise requires collaboration. No single function or technology investment can deliver the digital thread and MBE objective. The path forward requires a plan to bridge the gap between outdated processes and technologies and the value of future model-based capabilities.

With a strong understanding of your current-state data architecture, a clear vision of where your organization wants to go and cross-functional teams prepared to work together, your team is well equipped to evolve as an enterprise and innovate at unprecedented speeds.

The value of a digital thread

A digital thread powered by a common data ontology offers significant value and is key to differentiating innovators in the manufacturing space. Having touched on the key benefits of a digital thread (that's unlocked by a data ontology foundation), a more direct set of benefits include:

• Reduced manual tasks: Automating data flows frees up resources for strategic activities.
• Minimized churn and rework: Clear data management frameworks reduce delays and errors.
• Enhanced decision-making: Accurate, updated information leads to better outcomes.
• Improved collaboration: Seamless data flow fosters better communication and reduces silos.
• Compliance and traceability: Ensuring that regulatory requirements are met helps avoid penalties.
• Improved change control / change management: Connected data and systems enable better visibility into the impact of change.
• Innovation and continuous improvement: Clear data management frameworks drive competitiveness and industry leadership.
• Boosted employee satisfaction: Employees will engage in value-added activities instead of rework and manual tasks.

With a company's vision for the future clear—what’s needed is a plan to get there. The implementation of a digital thread powered by a common data ontology is considered critical for organizations striving to become model-based enterprises. By providing a standardized framework for data management and integration, a common data ontology enables companies to innovate, drive value and execute with unseen precision and speed.

The path to a model-based enterprise awaits

Adopting a digital thread and a common data ontology is essential for driving efficiency, innovation, and competitiveness in the digital transformation journey. As organizations navigate digital transformation complexities, we find these tools to be critical in ensuring agility, responsiveness and industry leadership.

Author:

Smaran Bhandary DC Principal SCNO US sbhandary@deloitte.com