In today’s rapidly evolving industrial landscape, organisations face mounting pressure to deliver products to market faster, maintain high quality, mitigate costs amidst supply chain volatility, and comply with increasingly complex global regulations. These challenges are intensified by the need to compete in global markets and discover new sources of revenue.
Companies that design, manufacture, operate, and support physical products are seeking ways to maximise asset performance, reduce costs, and generate value in innovative way.
Asset Data Reality
Let’s imagine your company has three product lines designed at separate innovation centres and manufactured in plants across the US, Europe, and Asia. Products are sold both directly through national sales organisations and indirectly via importers, dealers, and resellers. Over a decade, you’ve pushed $5 billion worth of products into the market. And recently, the CEO announced an acquisition, adding a fourth product line with its own sales channel and a $1 billion installed base.
Service providers and asset owners now face the challenge of maintaining a total installed base of $6 billion while anticipating a generation of more complex and digital products. Despite various business applications, the core issue is fragmented product lifecycle data, varying in completeness leading to sub-optimal decision making.
Fragmented lifecycle data
Traditional systems such as ERP, MES, PLM, IoT, and field service management often face integration challenges, resulting in data inconsistencies, operational inefficiencies, and suboptimal decision making that negatively affect cash flow and EBIT.
While many organisations have implemented MDM and BI tools, these solutions frequently lack the necessary context and user accessibility for broad adoption, leading to slow and costly insight generation.
As a result, executives are often required to make decisions in volatile environments without dependable data, which can impede their ability to drive product innovation or deliver efficient and profitable services.
Impaired decision making
When considering our sample company—featuring four product lines, an installed base of $6 billion, and a product lifecycle spanning 10 years—the volume of data generated is both substantial and continually increasing.
“We are surrounded by data, but starved for insights”
— Jay Bear —
Examples of poor decision making caused by fragmented lifecycle data for executives:
- Design: Lacking insights from previous versions hinders product improvement.
- Sales: Unclear product performance makes it hard to assess portfolio relevance or identify top and underperforming products/customers.
- Quality: Incomplete field data prevents verification of product performance and timely corrective actions.
- Service: Inefficient maintenance results from missing As-Designed prescriptions and As-Built records.
A different approach to achieving an asset data foundation
When traditional ERP, MES, PLM, and FSM systems are too complex to integrate, and MDM or BI tools lack context, businesses can turn to a modern alternative: an AI-powered Asset Data Foundation managing the product lifecycle data holistically.

The AI-powered Asset Data Foundation establishes an integrated layer between various system-of-records for product lifecycle data and an application platform. This enables users across design, sales, quality, and maintenance functions to access comprehensive data, supporting informed operational, tactical, and strategic decision-making.
The Asset Data Foundation recognises the different forms data takes at its original sources, understands the context, and is equipped to clean and validate this data, creating a more reliable and enhanced source of truth.
To facilitate real-time insight extraction and support informed decision-making from comprehensive and unified datasets, maintaining optimal performance and responsiveness is essential. Consequently, data vectorisation is incorporated within the Asset Data Foundation layer to provide advanced data processing capabilities.
Business value for Engineering and Service
While several business functions within an enterprise can gain from an Asset Data Foundation throughout the product lifecycle, Engineering and Service are the primary contributors. Engineering is responsible for setting the product’s intent during its digital lifecycle, whereas Service oversees its real-world performance during the physical lifecycle.

Organizations frequently invest significant resources in the design and development of high-quality products, which are subsequently introduced to the market. In a sell-and-forget business model, establishing an asset data foundation may not be a priority. However, when shifting to a sell-and-service approach, a robust asset data foundation becomes essential.
In our example company, $6 billion in products are introduced through various go-to-market channels. Achieving complete visibility of asset location, condition, and usage via the asset data foundation would enable Service to reach world-class standards in installed base monetisation. Additionally, the asset data foundation would offer access to design intent details, supporting efficient and proactive service delivery.
At our sample company, four different product lines are developed across four separate locations and produced in three different regions. By leveraging asset data regarding visibility, condition, and usage from the same installed base, Engineering can more efficiently and effectively drive innovation for both new and existing products.
See also PTC blog: The power of a strong data foundation






