The Scope Covered by the HOPEX Data Governance Solution
HOPEX Data Governance has adopted an approach in accordance with data governance. As such, it offers a set of features that cover the following dimensions:
• data discovery, through ready-to-use connectors
• the definition of a business glossary based on the terms and their definitions
• data architecture
• the specification of business rules and the management of compliance with the regulations that apply to the enterprise
• the implementation of data lineage to monitor the path of information
• data quality evaluation
• data analysis, via standard reports supplied
Data Discovery
Through the HOPEX Data Discovery tool you browse different data sources and define the metadata to import into your data catalog.
Business Glossary
HOPEX Data Governance allows you to draw up an inventory of enterprise terms and generate a business glossary that you can use to consult their definition, as well as their synonyms and components.
Data Architecture
Three Modeling Levels
The HOPEX Data Governance solution covers the three levels of data modeling for an organization:
• Business (conceptual) level: used to define the business architecture concepts and generate glossaries. These concepts can be implemented by objects at the logical level and be described by data models.
See
Introduction to the Creation of a Business Ontology.
• Logical level: intended for clients seeking to develop general business-oriented models. Here it consists of modeling data of a domain, application or business process. It represents what we wish to do and where we want to go, irrespective of technical questions related to implementation. Data is represented in a data model or a class diagram.
See:
Modeling Data dictionaries.
• Physical level: consists of defining models intended to persist in a DBMS. It comprises detailed specifications for production of the physical diagram of the repository. It is represented by the relational diagram.
The physical level also defines the way in which data is stored and how it can be accessed. It enables use of data by DBMSs.
See
Modeling Databases.
Data Category
You can classify repository data by category. A dedicated tree lists the various categories and associated data. Data thus classified can be used in the HOPEX Privacy Management solution specific to sensitive data and compliance with the GDPR.
Design Workflow
As a data designer (information asset manager) or creator (which concerns all IA profiles), you can launch a workflow on certain objects of the data architecture (such as a data lineage or a data domain) to track their design, their update and their validation.
Workflow reports allow you to view the number of objects that are found at each workflow step (number of objects undergoing design, analysis, etc).
Use of Data
To specify which business information is handled at the architecture level of the IS, HOPEX Data Governance offers a “Realization” function that connects the business information to the IS objects.
You can also specify which processes and applications use which data, whether this concerns business, logical or physical data.
Data Quality and Compliance
To guarantee reliable and complete data, HOPEX Data Governance provides tools to define responsibilities for data, the rules and standards with which the enterprise must comply, and assess to what extent the data meets the requirements of the organization and its stakeholders.
Definition of Responsibilities
When data is designed, managers are defined. They are notified of update or validation requests in the workflow framework of design or evaluation concerning the data in question.
Definition of rules and standards
HOPEX Data Governance allows you to create an inventory of regulations and enterprise rules and to precisely define which articles or sections with which the various information and entities of an organization must comply. The solution supplies in particular content from the BCBS 239 banking regulations and the Solvability (Solvability) II regulation.
Traceability of Data (Data Lineage)
Through data lineage, you can represent the various processing procedures for the data to facilitate the identification of errors and reduce the risk of non-compliance. This allows the Data Steward and Data Owner to ensure the quality of the data used.
Evaluation of data
HOPEX Data Governance is used to ensure the quality of data using evaluations. For this, the solution provides an evaluation model that assesses the data in six areas: completeness, uniqueness, timeless, accuracy, consistency and validity.
Evaluations deal with metadata. When information exists in third-party tools on data instances, experts such as the data scientist or the data quality manager can relay this metadata information in HOPEX Data Governance.
For example, the "Account Manager" information is used in different data processing tools. The expert who observes missing data in some records of this information, for example the details of a person, can note this lack of completeness in HOPEX at the metadata level (the "Account Manager" class for example) so that an action plan can be created to correct the situation (via mandatory fields for example).
The evaluation can take place directly in the data properties dialog box or via an evaluation questionnaire sent to the managers of the data in question.
Analysis reports
Analysis reports are dynamic reports that are used to analyze repository data: data completeness, data use, responsibilities, etc. HOPEX Data Governance supplies standard reports by default that allow you to check the quality, the use and the compliance of your data.
Data Governance vs Information Architecture
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Functionality
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Data
Governance
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Information Architecture
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Metadata discovery, through ready-to-use connectors
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X
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Data catalog management for discovered metadata, with a dedicated search engine
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X
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Business Glossary with terms and their definitions, with a dedicated search engine
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X
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X
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Data architecture (business, logical and physical layers)
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X
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X
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Database Modeling
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X
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X
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DB schema Generation
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X
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X
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Data compliance management, via business rules and regulations
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X
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Data Lineage (functional and technical)
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X
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Data quality policies and assessments
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X
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X
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Data analysis via standard reports
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X
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X
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