Modern PLM Data Management Stack

Modern PLM Data Management Stack

If you’re like many manufacturers, you’re probably still relying on legacy PLM systems to manage your product data. But as today’s products become more and more complex, these older systems are no longer able to keep up. In order to stay competitive, you need to move to a modern PLM data stack. This will allow you to take advantage of the latest technologies, including big data and the cloud, and get a better understanding of your product performance. So what should you look for in a modern PLM data stack? And how can you make the switch? Keep reading for answers to these questions and more.

Status Quo: From Proprietary Databases to SQL/RDBMS

PDM and PLM industry went a long way over the last 20-30 years improving data management and developing scalable platforms. The data management architecture of these solutions goes back to the time when PDM/PLM developers didn’t trust and could not rely on commercial database products. Therefore early PDM and PLM used proprietary solutions, developing a variety of data stores using file formats and embedded databases and management tools. However, the end game of experiments with proprietary data management tools was to switch to industry standards adopted by large manufacturing companies. The decision was not only technical but also political. Usually IT organization was a watchdog of technology adopted by a company and PDM/PLM systems looked at how to pass the “IT Police” test. The last one is much easier if you run on top of the industry standards – IBM, Oracle, Microsoft, etc.

Cloud and Cambrian Explosion in Data Management

Over the course of the last 10-15 years, we can see explosive growth in the variety of data management solutions and related technologies. It started from global web platforms and other cloud development that allowed to separate technology used to build a solution from the delivery process we’ve seen in enterprise software products. This change was the biggest contribution to the way the company started to manage product data and develop product lifecycle management systems to support business processes.

Massive development of a variety of data management solutions, databases, data processing tools, data storage, analytics, machine learning, and others created an ecosystem contributing to new PLM system development. A combination of new databases and cloud contributed to the foundation of polyglot persistence data architecture allowing to use of multiple databases together (thanks to modern microservice architecture and modern databases and storage solutions)

In my almost a decade-long blog – PLM and data management for the 21st century, I was talking about how different data management systems can be used more efficiently to support the development of a new level of PLM systems. So, what can be included in a modern PLM data stack, and how it can impact the development of new PLM platforms.

Data Stack

Digital transformation is on the top priority list for many industrial companies. What data management technologies can help them to build their digital future? This is a question that I’m receiving from many PLM architects working in the industry and developing PLM products as well as providing services to manufacturing companies.


Back in the 1990s, storage was either a document (eg. CAD file) or a database. Nowadays, we live at a time when storage is cheap and can easily scale to your needs. All PaaS platforms (AWS, Azure, GCP, and others) are providing reliable storage platforms that can be available for different system needs and configurations. These storages are reliable and provide safe mechanicals for data availability, redundancy, accessibility, security, and capacities.


The number of databases available for development was skyrocketing over the last 15-20 years. It was a result of massive web development that demanded a different type of data management complexity, scale, and sophistication. It resulted in the appearance of many NoSQL databases. As for the moment, I’d call three main types of databases: Key-value databases; Document store; Graph databases. Beyond these three types of databases, there are good old SQL (Relational) databases represented by a large set of mature and scalable solutions.

All these databases are not standing still and are continuously developed. So, don’t be surprised to see some elements of NoSQL databases coming to Relational databases and vice versa.

Analytics and Intelligent Services

PLM industry is moving from application to data. Data is quickly becoming more and more important and, therefore, having tools that can help you to process the data is becoming critical when you think about modern data stack.

The number of analytics software is skyrocketing. Check G2 Analytics – it can be a good starting point for you to explore. If you’re running one of the enterprise platforms such as SAP, Microsoft, or Oracle, you might leverage the analytical tools provided by each of them (all these vendors made acquisitions in this space).

CAD / PLM services and Platforms

Vendors in the CAD and PLM industry are moving from developing desktop tools and enterprise applications to the online platform. For many of them, it is a balancing act between declaring that their existing platforms will be approaching the end of life in the foreseeable future. Nevertheless, new platforms are starting to show up and you should pay attention to them. A few examples here – are Autodesk Forge, DS 3DX, OpenBOM, PTC Atlas (Onshape), Siemens Xelerator, and some others.

Legacy Applications and Solutions

Don’t write off the existing tools. Integrating existing tools is an important element of your data management strategy. When it comes to design, engineering, and manufacturing systems, the importance to acquire existing data is extremely important. After all, I bet 90% of the design is still done using traditional desktop CAD systems while data is stored in files. Existing PDM /PLM / ERP systems are good sourcing of information you need to move forward. Integrating them will make your solution entrance easier.

What is my conclusion?

The time when PDM and PLM developers were looking for “the best database” to solve the problem is over. While these databases (mostly SQL) continue to be reliable platforms for legacy PLM systems, modern PLM solutions will be built on top of a new data management stack including technologies and tools capable to provide the next level of scale, intelligence, and data management sophistication. As manufacturing is moving toward providing services and manufacturing networks, there is an urgent need to transform existing PLM applications into modern multi-tenant platforms capable to provide data and lifecycle management services solving complex manufacturing problems – managing of complex systems and products containing mechanical, electronics and software components, solving supply chain challenges and answering the demands of mass configurations and global product delivery. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital cloud-native PLM platform that manages product data and connects manufacturers, construction companies, and their supply chain networksMy opinion can be unintentionally biased.


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