Industrial Data Management: Successfully Centralizing Data with AVEVA Engineering
In complex industrial projects, data management is a critical factor in ensuring quality, consistency, and overall operational performance. With multiple tools, diverse disciplines, and constantly evolving assets, structuring and maintaining reliable data quickly becomes a major challenge.
AVEVA Engineering stands out as a key solution to centralize and secure data throughout the entire project lifecycle. However, a successful transition is not just about implementing a tool, it also requires the adoption of best practices.
Our experts explore the challenges of data centralization, the key issues related to data migration, and the methods to effectively structure, secure, and maintain a reliable and sustainable data environment.
Centralizing Data to Ensure Reliable Information Across the Organization
Centralizing data ensures that information is reliable, shared, and consistent across all teams, while enabling more efficient collaboration. In AVEVA Engineering, each object, whether a pump, valve, or instrument, has a single, continuously updated version. This eliminates duplicates and inconsistencies, which are a major source of errors in complex projects.
All project data is consolidated into a single database accessible to all disciplines, improving coordination across teams. At the same time, object attributes remain consistent and synchronized, even when multiple teams are working in parallel. This real-time synchronization reduces discrepancies and supports more reliable decision-making.
Ultimately, data centralization provides significant time savings. Teams no longer need to search for, compare, or correct scattered information—they can rely on a single source of data, improving efficiency and overall productivity.
Challenges to Address and Pitfalls to Avoid
Migrating existing databases to AVEVA Engineering presents several challenges, often linked to the diversity of data sources and varying data quality. Information typically comes from tools such as Excel, Access, or legacy systems, with very different levels of structure. As a result, data may be incomplete, inconsistent, or duplicated, and naming conventions often differ between teams. In addition, objects are not always structured consistently or aligned with the AVEVA Engineering data model, making integration more complex.
To avoid common pitfalls, it is critical not to import data “as is” without proper cleansing or to mix different versions of the same file. Working in silos should also be avoided, as it reduces overall consistency. Ensuring compatibility with the target data model is essential, as is adopting a phased migration approach. Attempting to import all data at once without prior testing significantly increases the risk of failure.
A Clear and Robust Data Structure
A well-defined data structure is essential for effective data management. Involving all disciplines in its definition is a key success factor, as it ensures alignment and adoption across teams. Structuring data in AVEVA Engineering requires a clear and shared organization from the outset, typically based on a logical hierarchy such as sites, units, systems, and equipment.
Each object should be defined through a single data sheet containing its key information, ensuring consistency and avoiding duplication. Simple and standardized naming conventions should be applied across all teams to maintain clarity and consistency.
In addition, the use of controlled value lists helps reduce unnecessary variations and improves overall data quality. Proper documentation of the structure is equally important to ensure that all users follow the same rules and best practices.
Ensuring a Secure Single Source of Data
Ensuring a reliable “single source of data” requires strong data governance. Each object must be assigned a unique and persistent identifier that does not change over time, preventing ambiguity and duplication. Data security also relies on clearly defined access rights, ensuring that only authorized users can modify specific information based on their role.
To minimize risks, direct deletions should be avoided. Instead, elements should be marked as “obsolete” to preserve historical data while preventing data loss. In addition, full traceability is essential to track who made changes, when, and why.
Finally, implementing a structured validation process ensures that all modifications are reviewed and approved before being applied, reinforcing data reliability and integrity.
A Clean and Sustainable Database
Maintaining a clean and sustainable database requires ongoing effort and structured governance. Data should be regularly reviewed and updated to reflect changes in the client’s assets, such as modifications, retrofits, or decommissioning activities. This ensures that information remains accurate and relevant over time.
Consistency in naming conventions is essential to maintain clarity and avoid deviations. Training teams also plays a key role in promoting best practices and reducing data entry errors.
Finally, regularly removing obsolete or unnecessary data helps prevent database clutter and maintain performance. Assigning discipline-specific data owners to validate sensitive changes further strengthens control and ensures long-term data quality.
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