By: Tom Gill, Practice Manager, PLM Enterprise Value & Integration, CIMdata Inc..
Master data and the aging toolsets that manage it present a unique opportunity for Product Lifecycle Management (PLM). The opportunity is updating and enhancing the master data, which can be crushed under the demands of today’s hectic business environments. The remedy is PLM enablement, and it is intended to rescue master data from irrelevance and transform it into a foundation for digitalization.
The big enablement issue is that gathering master data is a manual process. Even worse, because Master Data Management (MDM) tools are often cobbled together, ad hoc, few companies know the change history of this master data—which is why a lifecycle view is so important.
Master data is the repository-of-record for knowledge about products, personnel, processes, customers, business partners and other stakeholders, locations, suppliers, regulators and much else—any released data, specifically people, things, places and concepts.1
In ways great and small, it is vital to the smooth operation of the enterprise and keeping its many parts in sync. For example, data needed to produce and sell a product includes requirements, bills of material (BOMs), 3D models, CAD drawings, catalog updates, marketing collaterals, packaging, distribution, manuals, compliance certificates and much else.
Master data began to collect and codify lists and manage them. Over time, it exploded into an unwieldy mix of data types and formats, much of it quite technical, relied upon throughout the extended enterprise. Sources vary widely—PDM systems, PLM solutions, spreadsheets, Microsoft Word documents and SharePoint repositories plus MCAD, ECAD, and other data repositories. Also included here are the many BOMs to define product configurations—as-designed, as-planned, as-built, as-delivered and as-maintained with serial numbers.
Loose connections and bad data can have major financial impacts such as lapses in regulatory compliance. PLM enablement provides traceability and verification to guard against discrepancies, errors and duplication.
Transactions and Configurations
Any examination of master data reveals profound upheavals in usage—from “archival” to “transactional” and, soon, to “predictive.”
Archival data is “permanent,” in that it captures the enterprise’s knowledge at a specific point in time. Additions usually were routine but changes were infrequent. To support fast-changing business processes such as product innovation and day-to-day operations, transactional data is needed. More sophisticated than lists, transactional data changes frequently and unpredictably. Securing the correct, current version of needed information is transactional, as is finding the latest product or system configuration. Even more complex is analytical and forward-looking predictive data.
Permanent, transactional or predictive—all three are vital to enterprise digitalization. Transactional and predictive data can greatly stress current master data solutions and MDM developers are struggling to catch up.
Permanent, transactional, or predictive uses of
master data —all three are vital to enterprise digitalization.
Nevertheless, these transformations to master data and MDM tools are more than just a new digital challenge for IT; they are also a big opportunity to enhance the business. Unfortunately, much of MDM data is dependent on unintegrated or poorly connected systems. Hence, MDM updates from product data are commonly manual, which can make transactional and predictive data unreliable.
Enablement Issues: Manual Processes and Constant Change
In general terms, master data is comprised of information that must be known and understood for making sound and timely business decisions on which the success of the business rests. This information may be structured or unstructured (such as video and PDFs) and can be metadata, or hierarchical (tree-like, linked data records).
Underlying all this is the constant change in the business environment due to innovation, disruption, obsolescence, reorganizations, acquisitions, and divestitures. These are huge drivers in the need for MDM data that is of better quality and made available faster.
These challenges offer a new way to build vital support for PLM across the enterprise by supplying the rest of the business with more and better information on which to base decisions. CIMdata’s PLM Knowledge Council considers MDM data structures and tools to be good candidates for enablement. Moreover, enablement offers paths to identify and develop best practices and to raise these priorities within IT organizations.
End-To-End Lifecycle Support
As a product innovation platform, PLM provides end-to-end lifecycle support for all product-related data and processes, not just master data. This means integration and transparency plus traceability backwards and forwards in time from any point in the lifecycle.
For aging solutions and tools such as MDM that are still in everyday use, PLM:
- Captures data, reviews, and approvals at every stage from the conceptual, i.e., the release process (the point of the data’s creation) across the information lifecycle from preliminary to production-ready to after-sales support. In addition, capturing product-related master data at its sources can be automated.
- Manages data, classifying it by type or function, or other categories. Searches are more comprehensive, sharing is facilitated through well-defined processes and access is secured to a single logical repository.
- Driving most master data with PLM also simplifies and clarifies update processes. After all, IT and every other unit of the enterprise use master data, generate it and update it.
- Enables data and files to be referenced rather than copied, from which both security and transparency benefit.
- Resolves toolset and format incompatibilities while overcoming the boundaries of organizations, platforms and technical disciplines.
The PLM Enablement Opportunity
Managing master data with PLM enablement can give users and managers significant help. Made easier are the data wrangling tasks in sourcing, contracting, manufacturing, sales, marketing, service and maintenance. Also benefiting are specialized tasks in finance, human resources, workplace safety and health and even the executive suite. Mistakes and other business issues that arise when new-product information is not immediately available can be eliminated.
For older toolsets, PLM enablement is often the best way to upgrade business processes that are dependent on master data. The data is cleaner and discrepancies can be addressed quickly thanks to traceability back to data sources. Users gain confidence ... and competence.
In an ideal world, master data would have been kept in hand by information-governance committees. Yet many enterprises and IT units have no plan for how to obtain good data or how to strategically manage that information so it stays good over time. And despite ample evidence to the contrary, MDM implementers seem to have assumed that good data would always be readily available.
Ideally, these insights should start a much-needed conversation about what master data is, and is not. More often, however, this lack of planning and oversight creates “silos of expertise” that hoard or lose information.
PLM enablement is built on the concept of a holistic product innovation platform—a digital platform that is open, readily configurable, scalable, adaptable, and that can share product-definition information with MDM solutions.
This is where PLM has always excelled—and can ensure that MDMs deliver what they originally promised and, in today’s environments, much more.
Gaining Support From It
Essential PLM capabilities are available “out of the box;” implementing them, however, usually requires some configuration and an integration effort2 with help from IT. For business-process owners, PLM project leaders, and teams, master data enablement means building alliances with information technology (IT) units outside PLM’s core domain in engineering and new-product development. CIMdata Knowledge Council members point out that any proposal requiring cooperation between business units and asking to use others’ resources must consider the impacts the affected business units.
To be successful, proposals must tell a compelling story about how master data quality and currency affect business performance—that is, the story must resonate in the enterprise. Facts and figures by themselves rarely win big approvals and support.
An essential part of this resonance is building credibility. Successful enablement proposals avoid overselling benefits. Implementation efforts are not low-balled. Needs for resources and technical skills are not played down or minimized. Promises for go-live dates are realistic.
PLM’s support for the company’s vision makes MDM enablement investments even more worthwhile. Successful PLM enablement ensures that master data lives up to its original promise and provides a new degree of MDM stability to MDM—a unique opportunity to meet the needs of today’s ever-changing business environments.
NOTES
1. A thorough description of master data, “The What, Why, and How of Master Data Management,” can be found at https://msdn.microsoft.com/en-us/library/bb190163.aspx. The article was written in 2006, so some terminology is dated.
2. Most applications written over the last two decades have an application program interface (API), integrative functions typically written in C, C++, C#, Java. APIs are relatively stable but code needs to be tweaked, tested, and validated. All can be very time consuming.
CIMdata, an independent worldwide firm, provides strategic management consulting to maximize an enterprise's ability to design and deliver innovative products and services through the application of Product Lifecycle Management (PLM). To learn more about CIMdata's services, visit http://www.cimdata.com.