Why use a Data Dictionary?
A successful business requires well structured, high qualified product content to maintain consistent data across all channels and sustain brand integrity. This is needed because exceptional content, whether it be online or in-store, has proven to improve conversion rate and sales growth.
However, bridging in-store and online product content can be challenging at times due to multiple fragmented solutions and disconnected requirements. Leveraging 1WorldSync will ensure that your product content remains both consistent and tailored to the needs of your recipient.
In order to prepare an efficient data exchange between your data sources and targets, your product content needs to be analyzed, structured and documented in an accurate manner.
Why exchange data?
Many recipients ask for input and different requirements and formats; it’s important to identify what the differences and/or overlap is.
A data dictionary can help you cover these requirements, in the best case before (but also during or after) implementing a solution. A data dictionary also supports your product content management processes. Other terms used for the term data dictionary are product data lexicon, mapping, specification, profile etc.
How to structure a Data Dictionary?
The following topics are recommended to be part of a data dictionary:
- overview information e.g. company name, related system, version number
- legend of the tab names, their columns and colors
- in order to reference all following items, all attributes, valid values or validation rules should be provided with a key ID.
- what information? business name, business definition and
- which source or recipient? who is responsible (internal owner) and related systems
- incl. all attribute metadata e.g. attribute type, unit of measure, no. of characters
- their source and/or target mappings like GDSN path
- for attributes with single or multi select (codelists)
including codelist name, code ID and descriptions – if necessary – in different languages
optionally complex or hierarchical codelists like business unit (1st level) brand name (2st level), series name (3nd level), brand owner name and its GLN.
their source and/or target mappings i.e. GDSN code
Validation rules (optional)
- what are the business rules?
- requiredness: mandatory/ optional
- other conditions under which the attributes are
- reference to attributes or valid values
How can you create a Data Dictionary?
Start by answering the following questions:
- What information? Collect all product related attributes and valid values of your business. Find the business names and a business definitions. What is the need?
- Which stakeholder? Interview all internal and external data providers and data recipients. Differentiate between sources and targets.
- What governance process between attributes/data and stakeholders should be defined? Clarify internal owner and related systems to be distinguished between states “as-is” and “to-be”.
- Which metadata? Analyse all metadata and structure of each single attribute and their valid values. Typical: Data type, size, looping group, multi-value, unit of measure, language dependency, or code naming and their definitions.
- How does it work technically? Map each attribute and valid value to the source and target systems. Differentiate between mappings, transformations and derivations.
- Which quality rules? Find your data quality related business rules and transform these into technical validations. Is the attribute mandatory in general or per product class? Or could it be left optionally or be completely removed?
Discover 1WorldSync Services Experts in Product Content Management
Enabling more than 25,000 global brands in 60+ countries, 1WorldSync understands that no matter where you conduct business, trusted content and data quality are essential.
1WorldSync’s Professional Services Team can help you to provide the comprehensive data dictionary of your product related business. One piece is conducting an assessment in order to answer the basic questions. Another part of delivery is the attribute analysis itself in order to map your data requirements into a data dictionary.