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Data framework
Build the data foundation for a future-ready organisation
Create a clear framework for your product data with a structured data ontology that defines what good data looks like, how it should be maintained, and where the gaps are today. This framework is key to achieving high-quality product data, which improves the performance of all other product-related processes in your company.
A CLEAR STARTING POINT FOR DATA QUALITY
Why Slimstock?
Slimstock helps you create a structured data ontology that reflects your business and product range.
- Define categories clearly, capture and improve a logical structure across your assortment.
- Set company-specific requirements to define your data standards, tailored to your product hierarchy.
- Check whether data is complete, correct and consistent on a continuous basis.
- Create visibility from day one and see where the main data gaps are today.
PUT THE RIGHT STRUCTURE IN PLACE
Benefits of a stronger data framework
Create one clear view of what good data looks like
Define product data in a way that is consistent across the business. With a shared structure in place, teams work to the same standards and expectations are easier to manage.
Make data issues easier to identify
If the product hierarchy and product requirements are clearly defined, it becomes easier to see where data is missing, where values are wrong and where records don’t meet the required standard.
Reduce ambiguity across teams
Many data issues start with unclear ownership or inconsistent definitions. A structured foundation helps bring more clarity to the way data is managed across functions.
Support better decisions downstream
Planning, pricing, replenishment and compliance all depend on reliable product data. A stronger foundation improves the quality of the processes built on top of it.
Prepare for scalable improvement
Before automation can work properly, the underlying data model needs to be right. Data Framework helps you put the basics in place first.
KEY FEATURES OF DATA FRAMEWORK
Built to define, assess and improve data quality
Use your existing product category structure as the basis for defining data requirements at each level of the assortment.
Category attribute modelling and inheritance
Define category-specific attributes for each product group, from general fields such as dimensions and material to more specific requirements, while applying attribute inheritance across broader categories and sub-categories to reduce duplication in configuration.
Data validation across all dimensions
Set rules for ranges, text and patterns, and AI-based validation to cover data completeness, correctness, and consistency across all attributes on a continuous base.
Best-practice constraint library
Based on industry standards, such as GS1 or ETIM, validation rules can be applied where internal definitions are still limited or incomplete.
Assess your current product data against the ontology and identify where the main issues sit across categories, attributes and rules.
Generate an up-to-date view of product data quality at any point in time, rather than relying on one-off reviews.