The data dictionary 1 × 1

A successful company requires well-structured, highly qualified product data in order to maintain uniform processes in all areas and to maintain brand integrity. This is necessary because having complete and valid data, whether online or in-store, has been shown to improve conversion rates and sales growth.

What is a data dictionary used for?

A successful company requires well-structured, highly qualified product data in order to maintain uniform processes in all areas and to maintain brand integrity. This is necessary because having complete and valid data, whether online or in-store, has been shown to improve conversion rates and sales growth.

However, merging in-store and online product data can sometimes be difficult due to multiple fragmented solutions and inconsistent requirements. Atrify's solutions ensure that your product data remains consistent and is tailored to the needs of your customer. In order to achieve an efficient data exchange of your data sources and targets, your product data must be precisely analyzed, structured and documented.

What are the advantages of a data dictionary?

Many recipients have different requirements for the data content and its formats; it is important to recognize what the differences and / or overlaps are.
A data dictionary can help you meet these requirements, ideally before (but also during or after) implementing a solution. Such a directory also supports your processes in the area of product content management. Other terms for the data dictionary include product data dictionary, mapping, specification, profile, etc.

How do you structure a data dictionary?

The following topics are recommended as part of a data dictionary:

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  • Information overview such as B. Company name, associated system, version number
  • Legend of the tab names, their columns and colors
  • In order to reference all of the following elements, all attributes, valid values or validation rules should be provided with a key ID

Attributes

  • Which information? Internal attribute name, business definition, and sample values
  • Which source or which recipient? Who is responsible (internal owner) and which systems?
  • All attribute metadata includes, e.g. B. Attribute type, unit of measure, number of characters
  • Your source and / or target mappings such as GDSN path

Code lists

  • For attributes with single or multiple select
  • Contains code list name, code ID and descriptions - if required - in different languages
  • Optionally complex or hierarchical code lists such as business area (1st hierarchical level), brand name (2nd hierarchical level), series name (3rd hierarchical level),
  • Brand owners and their GLN
  • Their source and / or target mappings, e.g. GDSN code

Validation rules (optional)

  • What are the business rules?
  • Requirement: mandatory / optional
  • Other conditions under which the attributes are
  • Reference to attributes or valid values (code lists)
How can you create a data dictionary?

Start by answering the following questions:

  1. Which information? Collect all of your company's product-related attributes and code lists. Find the internal attribute name and a business definition. What are the benefits and needs?
  2. Which stakeholders? Survey of all internal and external data suppliers and recipients. Distinguish between sources and destinations.
  3. Which governance process between attributes / data and stakeholders should be defined? Clarification of the internal owner and the associated systems, which must be differentiated between the actual and target states.
  4. Which metadata? Analyze all metadata and structures of the individual attributes and their valid values. Typical: data type, size, (repeatable) grouping, multiple selection, unit of measure, language dependency or code names and their definitions.
  5. How does it work technically? Assign each attribute and each valid value to the source and target system. Differentiate between mappings, transformations and derivations.
  6. Which quality rules apply? Find your data quality related business rules and translate them into technisc
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