Data Glossary Data Catalog-use cases image


The centrality of data in business process management has led to a proliferation of Data Management initiatives, often dictated by contingencies and without a coherent vision.

Misunderstandings between IT and Business generate conflicting relationships that, especially in the most complex realities, can cause the Shadow IT phenomenon. This means the creation of Information Silos and duplication of information, increasing costs and decreasing Data Quality.

The importance of data (not only personal data) has come to the attention of national and supranational government authorities and criminal organizations; the lack of knowledge of Data Assets and data management processes can lead to a compliance deficit, with deleterious effects in terms of economics, security and image for companies.

Data glossary & Data Catalog


The absence of governance tools such as Business Glossary and Data Catalog generates a number of potential problems:

  • Unnecessary, duplicate, inconsistent data collection. With increased costs and risk of inconsistency
  • A lot of time spent searching for the right data
  • A poor data governance risks to increase costs and also make business processes inefficient
of company data
reaches quality standards
Harvard Business Review
of company data
is not successfully used for a strategic purpose


  • Bringing order to the Data Repository is the objective of adopting a Data Catalog, while the Business Glossary enables the entire company to be made aware of the available information assets.
  • The Business Glossary typically consists of a hierarchical structure, in the definition of which a multidisciplinary team participates: IT, Business, Compliance.
  • The Data Catalog, on the other hand, can be fed manually, alongside project documentation, or automatically by crawling through Data Repositories.
  • The linking of Data Catalogues and Business Glossaries allows the semantic meaning to be assigned to the data. This linking can also be done manually, or automatically by applying rules on Data and Metadata (Data Classification)


In the following example Quantyca supported a customer operating in the utilities market in the implementation of a Data Governance programme.

Blindata Platform collects metadata by crawling from the different storage components of the Data Platform (AWS Aurora, AWS Redshift, Hive, TDV).

Data Quality probes monitor the data residing on AWS Redshift, AWS DocumentDB, TDV.

Quantyca: implementazione del programma di Data Governance.

The full route

1. Setup
Definition of the technological infrastructure and configuration of the application. Connection to data sources
2. Land
Definition of the horizontal boundary (which business processes and which systems) and vertical boundary (the level of detail of the mapping). Definition of the rules for feeding the Business Glossary and the Data Catalog. Definition of roles and assignment of responsibilities
3. Expand
Process iteration with perimeter extension and refinement/revision of processes and procedures.


Increased efficiency in data management projects
Diffusion of Data culture
Improving communication and cross-functional relations
Formalising Data Ownership
Improvement of regulatory compliance

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