A Global Automotive and Mobility Manufacturer Unifies Data with Multidomain MDM

A global mobility manufacturer has built a broad portfolio of transportation and mobility solutions to support customers around the world.  

As the world turns toward battery electric vehicles (BEVs), the company is adapting to new economics and new technologies such as electrification, intelligent integrated platforms and integrated battery chains to support the world's transportation needs.  

To accomplish these goals, a new digital strategy was required, and the company's community of data experts were at the forefront of an organization-wide drive for change.

"Digital strategy requires end-to-end design and decision-making to commit to transformation," said a chief engineer at the company. "Digital strategy provides a holistic solution across business domains and regions and also requires an investment decision from a management perspective."

A key part of this enormous undertaking was addressing the company's data environment.

Challenges: Legacy systems and siloed data create barriers to enterprise-wide visibility  

Growing volumes of siloed data  

With around 200 sites across the world and over 3,000 suppliers, data at the company is both generously supplied and sometimes a challenge to use to its full extent. Sharing data between business units is difficult, and even when it's gathered, making use of it in a holistic manner is a challenge. The scale and maturity of the organization has also resulted in a proliferation of over 1,000 systems that create, share and process data.

The transition to software-defined vehicles  

The company is moving to a new business model built around product, customer and social value, and an early part of this work is to move toward software-defined vehicles (SDVs), effectively products that can add functionality in the future via software updates.

This, along with the volumes of data such vehicles collect and communicate, represents a data challenge of its own.

Fragmented data across departments

But first, the company had to fix a systemic problem. While its departments collected a lot of data, that data was not connected. Data from development, engineering, purchasing and logistics teams was scattered over more than 1,000 systems, hardly an unusual issue for a company with a long history, but also a huge opportunity for improvement. Part of this challenge was organizational as well. For many years, domains and regions had driven the process, leading to a decentralized approach to data. 

Meeting ESG regulations and net-zero goals  

The company's commitment to net-zero manufacturing meant it also had to begin gathering multiple environmental data points from its suppliers, compounding this challenge. The organization has long published an environmental, social and governance (ESG) report, but new regulations called for more logically structured data across all business domains. The challenge was the scale and complexity of the organization, spanning a global network of locations, hundreds of affiliated organizations and over 3,000 suppliers.

Driving sustainability in the supply chain  

This last point also links to the company's supply chain. The drive to achieve long-term sustainability goals has been embraced by employees, demonstrating a strong commitment to continuous improvement. With tens of thousands of part categories and continuous import and export of SKUs around the world, employees noted that improving packing materials, methods, documentation and box sizes could reduce both cost and environmental impact significantly.  

The opportunity: Building a modern data platform

Combined with a lack of consistently structured master data, the team realized they had a golden opportunity to reduce the delays inherent in processing large volumes of data and create the foundation for a modern data platform.

To summarize, the company was experiencing the following challenges:

• New initiatives put more demand on the collection and processing of more data. A commitment to move to carbon-neutral processes meant that new uses were being created for data, but the capability was not present to do so effectively.
• A complicated data integration story meant that even if teams could gather data together on one platform, its full value was still unrealized.
• The huge scale of operations meant that over 1,000 systems were producing enormous volumes of data whose value was not being fully utilized.
• Lack of structured master data meant that even when data was collected from the company's array of systems, processing and deducing value from the data took too long.

Building a connected data foundation  

From proof of concept to live MDM in three months  

The company implemented Stibo Systems’ STEP platform for multidomain master data management (MDM), starting with a proof of value (POV) and proof of concept (POC) before adopting and launching the MDM platform.  

A culture of collaboration and connectivity  

The path forward required both a technological and cultural shift across the organization. "After many years, the company had data in over 1,000 systems. The company decided to turn to MDM to connect all this data together. To achieve this mission with MDM and realize more value from our data, we turned to our own culture, building a two-way approach with the data community within the company," said a senior engineering leader.

While automotive manufacturers tend to focus on the product data domain during an MDM implementation, the company was able to take a view of its entire ecosystem. For example, while its development, engineering, purchasing and logistics business units all collected and processed a great deal of information, it was difficult to connect the different datasets together. The data team started with the product domain and gradually expanded its efforts to include location, supplier and customer data.

The team took a two-way approach to address the issue of data siloed across multiple domains and regions. From the top down, the data engineering team aligned the data within the organization and developed systems to make best use of the data. The users and creators of the data at the other end of the process worked to collect and consolidate datasets, so the MDM could start to do its job of ensuring the information, data and insights the company collects can be accessible to the entire organization.

Not only did the company eliminate its data silos, but it ensured that the transformation that took place, while guided from the top down, was heavily reliant on feedback from the shop floor.

Tackling ESG and supply chain challenges  

The organization can now start to build a strong data management platform for itself, its affiliates and its 3,000-plus supplier ecosystem.

The company also addressed its packaging and shipping practices, an issue identified by employees as a point of improvement. Using MDM, the organization can now begin to gather the data needed to systemically lower its supply chain costs and environmental impact.

Unifying data from multiple domains

Feedback from employees is vital to the success of the transformation, and key to that was the two-way approach. Its foundation was the cross-domain data committee comprising representatives from multiple teams across the company. This approach also extends outward to suppliers and other external stakeholders.

The result has been a central repository that collects fundamental data from core business systems, combined with product and market data, and makes it available for use throughout the organization as needed.

Three core benefits of transformation

The team unlocked three core benefits from the transformation project that have set the company on a path to success:

• Adoption of MDM: To unite data across business units and domains, the team addressed data silos and new data use cases. This approach maximizes the production of results while also minimizing the costs of doing so.
• A two-way approach to transformation: The company understood that while the digital strategy was driven from the top, it would not be successful without the active participation of the whole company.
• Centralization for scalability and flexibility: Building a central data hub allows individual business units more democratic access to enterprise data for innovation.

Building a future-ready data foundation 

The company's data team built a connected data ecosystem to support its ambitious future vision of sustainable manufacturing and safer journeys. It did this with the full support and involvement of the organization and its workforce, ensuring that feedback came from both above and below to build systems that delivered value for all. The next stage in the process, continuing to capitalize on the value unlocked via MDM, will be to support the supply chain and other third parties.  

The company's journey with MDM is more than a technological upgrade. It is a strategic investment in a future where data-driven insights empower innovation, collaboration and sustainable growth across its entire ecosystem.