Digital thread integration is on every executive’s agenda—even if they don’t call it as such… Integration requirements are not new, especially across the PLM and ERP landscape; however, in recent years, they became an essential source of competitive advantage by joining the dots across the enterprise, a.k.a. data continuity. As integration platforms and integration bridges emerge, they also require configuration and customization between enterprise digital platforms, from PLM to ERP and others.
Controlling digital platform customization is mandatory but not sufficient anymore. It is essential to review such customization in context of master data strategies and the wider integration landscape.
In this post, I highlight the importance of enterprise data governance, the decision-making authority for data-related matters: from defining and maintaining standards and policies related to master data and meta-data integrity, monitoring data-related performance and managing change.
Typically, enterprise data governance is typically part of enterprise data management (EDM) as it used to be called a while back. EDM relates to how enterprise data is defined, managed, integrated and retrieved, both for internal and external consumption. Critical success factors of EDM include data accuracy, traceability and consistency, in a context of change management.
In a nutshell, data governance includes three core perspectives related to authoring and traceability standards, but also ETL, change and downstream data consumption:
- Master data accountability, quality and transformation governance
- Meta-data traceability throughout the data lifecycle
- Data authoring and downstream orchestration
Master data accountability, quality and transformation governance
Data standards include structures, formats, interfaces, dependencies, security, etc. and how data is recorded (create-read-update-delete), shares, converted and transformed. The foundation of enterprise data governance relates to master data authority: where data is mastered at source, in which system, by which business or functional owners, as part of which process, etc.
Defining data and data transformation quality standards is the mandate of the enterprise data governance: it starts from selecting, integrating, configuring, customizing and maintaining digital platforms. It includes mapping how such platforms enable data authoring and transformation.
Meta-data traceability throughout the data lifecycle
As data matures through the product lifecycle, processes and workflows contribute to govern how data feeds into other business functions and enterprise platforms.
Meta-data traceability includes the following abilities:
- Identifying data sources, native / master and derived / slave formats.
- Understanding how data gets enriched through the product lifecycle, and various business processes and interactions.
- Assessing data changes and contextual impacts from changes.
- Searching for the right version or format and being able to search through historical data and branching.
- Following data integration points or services, push-pull interfaces and service contracts; putting this in context of enterprise integration landscape, a.k.a. the Digital Thread.
- Understanding what is customized at the platform or application level versus at the integration points to achieve this traceability and automation.
- Assessing potential bottlenecks, infrastructure dependencies (especially at the integration points) and validating non-functional requirements which are often critical but poorly validated.
Data authority and downstream integration orchestration
Tracing data from authoring platforms to consuming platforms or application is important to identify accountable functions for each data domains: linking to both upstream and downstream applications, but also the end-users operating through these solutions.
With PLM solutions, it is often critical to assess how data is used downstream from a given function or application; this helps frame potential duplications or non-value-added activities in “working around” technical or system limitations—especially those created by design due to the lack of big picture thinking. The role of an enterprise data governance is precisely to identify such limitation and make informed decision when it comes to:
- Deciding on customizing a given platform or applications.
- Deciding on where capabilities or functions are mastered for a given data set.
- Deciding on integration points and interface behaviours to reduce complexity, redundancy and duplication—focusing leveraging master data principles to control customization within and across platforms.
What are your thoughts?
This post was originally published on Momentum-PLM on 14 December 2020.
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