By Aileen Agricola | February 22, 2016
Excerpt from article featuring Master Data Management at toymaker Schleich for diginomica
…the graph-database structure would allow Schleich to make better sense of the interdependencies and relationships that exist between product data. In other words, graph databases treat the connections between two pieces of data as a first-class object, so you can search and categorise data not only by field, but also by relationships.
In this way, Schleich can get answers, more quickly, to more complex questions: which products in our Wild Life product line have Ingredient X in common and which of these will be sold in, say, Japan, where there’s a particular rule regarding Ingredient X? Weber adds:
More and more colleagues are starting to work with the system and find it useful. They’ll ask, for example, ‘Now show me all the missing chemical approvals for products due for launch in Japan on 1st September.’ And we can answer such questions quickly.
The issue here isn’t that you couldn’t ask the same questions using a relational model. You could – but you’d have to write more complex queries. Plus, graph databases do a better job of storing and searching the incomplete datasets often found in MDM.
Ultimately, Weber selected the Neo4J graph database from Neo Technology to provide the platform for Schleich’s new product data management (PDM) platform and the project kicked off last year. The team started by dealing with demands for information generated in the company’s Quality Control department. Next time, Weber says, he’d do it differently::
My recommendation would be to start at the other end of the process – with product development. Take your product data first, then the details of your bill of materials (BOM) from manufacturing as your second step, and then go deeper and deeper.
Today, staff at partner companies can also enter data into the new MDM platform, while employees in Schleich can get precise information about raw materials being used in each product, check compliance with legal directives and adjust their plans as necessary. The impact of changes to laws on production plans and product release dates can also be quickly assessed. Says Weber:
Using the new platform, we can manage our entire dataset from different systems quickly, easily and very flexibly. In addition to improved transparency in quality assurance, today we can plan and track all processes along the value chain with much greater efficiency.