Information architecture on organizational level – using data groups

23.12.2019 11:14 / by Maria Karancsi_    |    6 min read

Finnish public service organizations face a growing need to describe their information architecture and information portfolio. Its main drivers are EU’s General Data Protection Regulation (GDPR) and the new Information Management Act in Finland (entering into force 1.1.2020). Just as organizations need to describe and maintain their service and application portfolio, they need to understand and describe their information portfolio, i.e. information capital.

The information architecture of projects or business services is usually described in the form of domain concepts (conceptual data entities) and logical data entities, but on organizational level this would result in a model being too fine-grained, including too many entities, beyond understandable. In order to describe the organization’s information architecture, we need a more abstract, higher level concept.

JHS 179 recommendation (regarding enterprise architecture planning and development, created for Finnish public sector organizations in Finnish language) introduces data groups and main data groups, suitable for this purpose. As defined in JHS179, data group is “a high-level collection of data, derived from process and information needs”. Using them enables the organization to understand its information portfolio on a suitable abstraction level. This blog explains how to explore data groups according to JHS179 recommendation using QPR EnterpriseArchitect tool.

The collection of data groups can be visualized using data group diagram, where data groups form a hierarchy. The examples in this article are from a Finnish city’s education and daycare services organization’s data group diagram.

Top level: collection of main data groups

Main data groups are organized into data domains (groups), according to service area (inside the organization).

Example: Daycare and pre-school data

Blogi - Main Data Group - Image

 

 Main data group

”Main data group is a high-level collection of data, derived from process and information needs (JHS179)”. In practice, it is a collection of data groups.

Example: Daycare information

Blogi - Main Data Group 2 - Image

 

Data group

”Data group is a low-level collection of data, derived from process and information needs (JHS179)”.

Example: Daycare children

Blogi - Main Data Group 3 - Image

 

The next step is to consider mapping data groups to the information elements described in projects and services (i.e. to domain concepts and logical data elements mentioned in the beginning). A data group, as the name implies, is a set of interrelated data. The example above (Daycare children) includes children's personal information, address(es) and health information. The content of a data group can be described as a set of domain concepts (conceptual data entities) and their relationships, i.e. in the form of a domain model. The contents of a data group can also be described on a logical level, that is, by means of logical data entities and their relationships, i.e. in a logical data model.

The metamodel below forms a coherent hierarchical chain (from top to bottom).

Blogi - Meta Model - Image

 

Although information modeling is a nice task 😊, one must benefit from it as well. Next, let us see how to discover and use data groups, through a practical example.

How to define data groups?

Data groups can be derived in two ways: bottom-up or top-down. Top-down approach starts from the collection of the organization’s all information, which is broken down to smaller sets, according to a previously agreed criteria, e.g. the organization’s main business lines, service areas or top-level product groups. Breaking down continues to the next levels, until you get reasonable sized information units: data groups.

This blog focuses on the bottom-up approach, which has been used in the previously mentioned city. Its process and application architecture has been already described at some level, providing lots of input for discovering data groups.

  1. The city’s data pools, dealing with personal information, have been described earlier, as required by the GDPR. Most of the personal information has been discovered from those data pools. For example, “Children daycare customer register” data pool includes daycare customers’ personal data and according to the data pool’s description, it includes both children’s and parents’ data. Two data groups have been created based on this: “Daycare children” and ”Daycare children’s parents”. Personal information data groups have been discovered by going through all of the data pools mentioned above.
  2. Personal information data groups were grouped into main data groups according to the services (for example Daycare data, Basic education data), then grouped according to service areas (e.g. Education data). This resulted in the top-level grouping.
  3. Next step was to take one of the existing services and explore its data (other than person data) from all possible sources of information
  • Process architecture: processes, services and information flows between processes and actors (process interaction models, actor interaction models)
  • Information system architecture: application interaction diagrams, layered views and system documentation
  • Service descriptions, for example published on the city’s website
    • Creating first draft of the data groups including non-personal information, based on the above information sources
    • Reviewing and updating the draft together with the relevant applications’ owners and the subject matter experts from the business side (e.g. process & service owners)
    • Separating the data groups, which do not belong to the services in question, but have been identified during step 1 (processing data pools including personal information). These data groups are shown in the below data group diagram’s bottom row (e.g. Work contracts, Worktime monitoring data).
    • If the data group includes personal information, classifying it according to the GDPR as personal information (’GDPR’) or special personal information (‘DPIA’).

    4. Continuing with the next service, until all the services provided by the organization have been covered.

How to benefit from data groups?

In the previously mentioned city, data groups are being utilized in information management, since data groups form the organization-wide information map.

While planning a new service, its related data groups are refined into a domain model. Data groups are also utilized in sketching information flows on high-level. In the early phase of planning the service and its supporting application(s), data groups can be used to describe what information is handed over to another authority or company, and what information the future application is supposed to provide or receive from other applications.

Data groups can be used as well to describe the organization’s registers, data pools and data stores.

Data groups are also useful for finding new analysis and reporting possibilities, for example in predicting future service needs.

In another case, at Traficom (Finnish Transport and Communications Agency), data groups are described on enterprise level, explored using top-down approach. They are linked to projects by instantiating the relevant enterprise-level data groups on the project-level diagrams; i.e. data groups which will be changed or used in the new application/functionality developed by the project in question. In addition, the project-specific domain concepts and logical data entities are linked to the above data groups, refining the data group firstly on conceptual, then on logical level as described in the metamodel. Thus, the project (and its planned outcome) is connected to the enterprise architecture also from the viewpoint of information architecture.

The data group diagram below includes all data groups of the above-mentioned Finnish city’s daycare and education services organization. These data groups are discovered using the previously described bottom-up approach, and utilized now in information management as well as in services development.

 

Blogi - Main Data Group 4 - Image

 

Topics: QPR EnterpriseArchitect

Maria Karancsi_
Written by Maria Karancsi_

Maria works as a Senior Consultant at QPR. Her areas of expertise include Information and process modeling (ArchiMate, JHS179), information architecture, requirements analysis, service creation, tool training (QPR EA) and delivery and project management.