Data is a key element of any PLM system. Therefore, to define the data model is the first step in many implementations. It sounds as something simple. However, there is implied complexity. In most cases, you will be limited by the data model and database capabilities of PLM system. So, what is the right data model and database technology for PLM system?
Spreadsheet Data Model
Historically, it became the most commonly used data model. And the reason is not only because Excel is available to everybody. In my view, it happened also, because a table (spreadsheet) is a simple way to think about your data. You can think about table of drawings, parts, ECOs. Since almost everything in engineering starts from Bill of Material, to think about BOM table is also very simple. The key reason why in many cases spreadsheet model became so wide-accepted are simplicity and absolute flexibility. Engineers love flexibility, and this data model became widely popular. This is a data model you can find used by most of DIY (do it yourself) PLM projects.
Relational Data Model
This data model was developed by Edgar Codd back more than 50 years ago. Many commercial databases are using this model today. This is something you probably know as RDBMS. All existing PLM systems in production (as of today – 4/2016) are relying on one of commercially available RDBMS systems. However, RDBMS has weak points when it comes to management of highly structured data. To change data schema is complex process. To overcome this problem, PLM vendors came with flexible data models back in 1990s. It is an abstraction model all PLM systems are using on top of RDBMS. Almost all PDM/PLM systems in production today are using object abstractions developed on top of the relational data model.
The challenges of Spreadsheets and Relational Databases
Despite spreadsheets and RDBMS are proven and used by many mainstream applications, they are far from perfection. One of the PLM demands is flexibility. Spreadsheet model can deliver that, but gets very costly within the time. Relational data model can combine flexibility and support manageability of data. However, to make changes in these models is costly. Identification, openness and expandability is problematic in relational data models opposite to some other web-based solutions.
Alternative data management tools – NoSQL, Linked Data, etc.
Recent decade of web development created many innovative data management solutions. The moment of time database stopped to be a decision factor to be used by IT during sales process, engineers unlocked the potential of open source data management solutions. Key-value stores, Document databases, Graph databases – this is only a short list of so called NoSQL data management tools.
There is a growing amount of solutions trying to adopt a variety of new data platforms. NoSQL comes to the place as an alternative solution to RDBM, but most often used alongside existing databases to focus on a particular data storage or retrieval problem.
When speaking about alternative data management tools, it is important to mention Semantic Web – W3C. The collection of semantic web standards (RDF, OWL, SKOS, SPARQL, etc.) provides an environment where application can query that data, draw inferences using vocabularies, etc. Part of these standards something called Linked Data – a collection of data set in open formats (RDF) that shared on the web.
How to compare different database technologies?
The following table can give you an idea of how to compare database technologies and make a decision about what is a right choice for your PLM systems and/or implementation.
Many of the database technologies used by PLM vendors these days are outdated and came from the past 20-25 years. There is nothing wrong in these technologies. They are proven and successfully used in many applications. However, in order to achieve the next level of efficiency and embrace future of PLM, new horizons need to be explored. Data flexibility, openness and interoperability – these elements are absolutely important in the future of PLM. Options to use data management technologies developed for web from past 10 years need to be explored.