By Karl Darr | Member
In the Continuum of Data to information to knowledge to wisdom, it is the increasing maturity, quantity, and quality of context that enables data, information, or knowledge to elevate to the next level. Proper application of the correct semantic data structures provides electronic data with the context that is necessary to achieve the higher levels of business intelligence, which is required to synchronize cross-functional operations around current, accurate product information.
The correct application of semantic structures delivers significant benefits of great value and interest to CFOs, COOs, CIOs, and to all product information stakeholders. Commercial technical product data sustainment best practices indicate that applying semantic structures at the creation of the information is much more efficient and effective than applying semantic structures after the information has been created. More and more companies are trying to capture product information as product creation occurs. Consequently, common approaches have evolved from engineering disciplines into the technical communications market. The incremental nature of the investment to deploy these solutions also helps drive their popularity.
While this approach might appear to be the easiest way, most of the time it lacks the ability to apply the correct semantic data structures needed to capture product information in all its contexts. While engineering often has a more mechanical view of the product, technical documentation teams are confronted with products’ complete and comprehensive complexity. So they have to document not only the product’s mechanical aspects, but also more often the product’s functional processes, which in addition to hardware components increasingly include electric, electronic, and software components. Indeed, the importance trend lines comparing software and electronics to mechanical components in today’s products can be visualized as follows:
Figure 1. Component value shifts over time
This also helps explain the increasing demand for coverage of “Mechatronics,” the combination of mechanical, software, and electronic capabilities.
The proper use of the contextually correct semantic technologies is absolutely critical when delivering a single-source solution, capable of delivering both mechanical and functional perspectives of product information from the same data set.
For example, in the case of ABS (anti-lock braking system) and ASC (anti-slip control), the same mechanical parts are involved, but in a completely different process with a completely different result or function—ABS when braking, ASC when accelerating. In this situation, you can only understand the appropriate sense needed for technical documentation when a part is documented within the context of its functional deployment. This requires a structured environment that not only allows for the storage of product information in the context of the product itself, but which also captures product data at the same time in its own logical context with all its logical data-interconnections. It is the semantics in the structured environment that provides this context and allows a computer to automatically assemble the correct meaning.
It is further clear that throughout a product lifecycle, various product perspectives exist and must be managed by applications that provide the appropriate system functionalities needed to capture these perspectives. Thus, product information sources often include a variety of classic product data silos including CAD, PDM, PLM, Spare Parts Databases, ERP systems, etc. It is important that these silos and other enterprise data sources are integrated and synchronized with a single-data-source solution that is used to create product documentation. When this happens a Product Information Lifecycle Management (PILM) solution is attained.
In a properly implemented PILM, where the data is stored with the appropriate metadata (contextual semantics), all of the information “knows” what it affects and what affects it. So the correct information is much more available and accessible. Also, the information can be delivered with semantic tags that can return intelligence on the use of the delivered information for analysis and appropriate action. In this manner, the way service information is used, for example, can automatically flow back into the organization to help the appropriate people adjust prognostics and diagnostics information and processes.
If something in the system changes, everything that could be affected by that change is automatically alerted through the PILM’s synchronization, along the entire product chain. The PILM reflects the organic nature of information as it changes over time, along with the complete history of the evolution as it occurs. Any status change for any information unit can automatically trigger a workflow event, which is very helpful in synchronizing operations around current, accurate product information. Delivering this capability elevates the importance, priority, and status of the technical communication team in most companies. However, this is only possible when data is correctly attributed, i.e., maintained in the context of the product and structured in the context of the information’s purpose—which is generally not conceived for a specific document format.
The PILM captures all product data in complete context as it emerges in and from its various siloed data sources, and then future proofs the information by enabling it to dynamically deliver product knowledge concurrently in any document format including any version of S1000D, DITA, Interactive Electronic Technical Manuals (IETMs), training materials, maintenance schedules, etc., which may be needed now (e.g., hard copy, CD, DVD, Web, etc.), or that may emerge in the future (e.g., smart phones, wireless e-readers, tablet computers, etc.).
Creating a system that can capture all product information in its context as it arises while automatically attributing the information with the contextually correct semantic syntax is clearly a complex undertaking. However, properly addressing this challenge is a necessary step in delivering a true, single-source information management system that is capable of automatically generating product information in the form, format, quality, and language it is needed in, and delivering it when and where it is needed.
Karl Darr (karl.darr@gmail.com) is a Silicon Valley-based market development consultant for enterprise information systems.
