Most data management practitioners are aware of the challenges in orchestrating an effective and collaborative conversation between business and IT professionals about corporate data. What’s required is an on-ramp to engage business users in a meaningful conversation about data, which will enable them to ‘paint a picture’ of the data that is important to them.
To be useful, this picture needs to represent the system’s technical metadata –accurately capturing the business interpretation of the data buried deep within the system architecture. Data modelling is often used as the ‘palette’ of choice to convey this business view of data.
Generally speaking, data modelling has four layers (see figure), each of which serves a distinct purpose and audience. The first two layers - Subject Area and Conceptual models - are intended for conversations and communication with business users. These layers convey the main business and data concepts, highlight key entities and attributes, and are ideal for communication (but not for database design).
The bottom two layers - Logical and Physical - are more for IT / IM design, development, communication and execution purposes.
In the past, leveraging data models as a communication vehicle has achieved mixed results. Common criticisms are that the modelling output is too far removed from the physical system logic – making it difficult to action anything on the modelling output – and once the models are done they are too static and hard to update or change based on new business requirements or changes to the system of record. The good news is there is a way to address both challenges.
With the right mix of people, process and technology, an effective top-down / bottom-up hybrid modelling solution can be developed and deployed. For example, companies can leverage two best-of-breed applications to effectively bring business and IT users together to effectively collaborate and capture the essence of the business’s priorities, and run multiple parallel modelling workshops for different systems where all teams can arrive with the consistent same look and feel for every model, for every system.
The result of this business and IT modelling collaboration provides clear line-of-sight and traceability from the priorities identified by the business, to the tables and fields in the originating systems of record.
By combining the unique features and functions of an information governance solution and a case modelling tool (UML design tool), companies can quickly create a comprehensive, integrated solution.
To do so, it is necessary to develop a custom toolbar and import/export templates integrating both solutions. Combined, these assets ensure that 1) data models are created with the same consistent look and feel and, 2) data exchanged between the two applications is done in a comprehensive, controlled and validated way.
The functionality provided by each platform solution components is as follows:
Information Governance Solution / Catalogue
Case Tool
Companies should engage data specialists familiar with both the granular data captured in systems of record, as well as information governance and case tool integration. The data specialists need to lead facilitated workshops with organization Business, Information Management and Information Technology stakeholders to create subject area and conceptual models for any system.
Additionally, the data specialists need to follow a hybrid - top down and bottom up – approach where models are reverse-engineered from system data dictionaries and technical metadata, and multiple sources of input are leveraged for validation throughout the modelling process.
With a tight technology integration and methodology established, companies can benefit greatly from data modelling, including engaging system business and technology stakeholders alike to effectively collaborate in a hybrid modelling approach. Another key benefit is rapidly creating data models via a dynamic modelling solution infrastructure. This makes it easy to change and refresh models on a continuous basis (with applied, appropriate governance), as well as objectively linking what is modelled at the business level - subject area and conceptual level – to where the related data resides in physical systems of record.
Finally, enterprises can effectively leverage the modelling output as input for related data management initiatives, including (but not limited to):
By using the hybrid data modelling approach to create data model diagrams, teams can effectively create a reliable ‘line-of-sight’ for their corporate data. Using the data from physical systems of record, helps to rapidly establish and objectively link items of importance identified by business users to where these things actually reside in physical systems of record.
The end result is a picture of the forest – as well as the trees – that can provide tangible input to a roadmap of enterprise data management initiatives.
To be useful, this picture needs to represent the system’s technical metadata –accurately capturing the business interpretation of the data buried deep within the system architecture. Data modelling is often used as the ‘palette’ of choice to convey this business view of data.
Generally speaking, data modelling has four layers (see figure), each of which serves a distinct purpose and audience. The first two layers - Subject Area and Conceptual models - are intended for conversations and communication with business users. These layers convey the main business and data concepts, highlight key entities and attributes, and are ideal for communication (but not for database design).
The bottom two layers - Logical and Physical - are more for IT / IM design, development, communication and execution purposes.
In the past, leveraging data models as a communication vehicle has achieved mixed results. Common criticisms are that the modelling output is too far removed from the physical system logic – making it difficult to action anything on the modelling output – and once the models are done they are too static and hard to update or change based on new business requirements or changes to the system of record. The good news is there is a way to address both challenges.
Reverse Engineered, Hybrid Data Modelling
With the right mix of people, process and technology, an effective top-down / bottom-up hybrid modelling solution can be developed and deployed. For example, companies can leverage two best-of-breed applications to effectively bring business and IT users together to effectively collaborate and capture the essence of the business’s priorities, and run multiple parallel modelling workshops for different systems where all teams can arrive with the consistent same look and feel for every model, for every system.
The result of this business and IT modelling collaboration provides clear line-of-sight and traceability from the priorities identified by the business, to the tables and fields in the originating systems of record.
Best-of-breed Technology Platforms
By combining the unique features and functions of an information governance solution and a case modelling tool (UML design tool), companies can quickly create a comprehensive, integrated solution.
To do so, it is necessary to develop a custom toolbar and import/export templates integrating both solutions. Combined, these assets ensure that 1) data models are created with the same consistent look and feel and, 2) data exchanged between the two applications is done in a comprehensive, controlled and validated way.
The functionality provided by each platform solution components is as follows:
Information Governance Solution / Catalogue
- import system data dictionary to kick-start (validate) business subject area, entity and element conversations
- profile system objects, tables and records
- create drop down configurations to tag (classify) entities prior to export to the Case Tool
- define the subject areas and entities (input to the business glossary)
- export / import to the Case Tool
- establish traceability from Case Tool artefacts to systems of record
Case Tool
- leverage entity classifications (captured in the information governance solution) to seed and arrange entities for data models in a unified and consistent way - for all systems
- create entity associations with multiplicity
- create noun-verb relationships between entities
- visually discuss/debate entity relationships
- export models as Word or Pdf documents for additional discussion & collaboration
Data Modelling Solution Workshop Methodology
Companies should engage data specialists familiar with both the granular data captured in systems of record, as well as information governance and case tool integration. The data specialists need to lead facilitated workshops with organization Business, Information Management and Information Technology stakeholders to create subject area and conceptual models for any system.
Additionally, the data specialists need to follow a hybrid - top down and bottom up – approach where models are reverse-engineered from system data dictionaries and technical metadata, and multiple sources of input are leveraged for validation throughout the modelling process.
Data Modelling Solution Approach Benefits
With a tight technology integration and methodology established, companies can benefit greatly from data modelling, including engaging system business and technology stakeholders alike to effectively collaborate in a hybrid modelling approach. Another key benefit is rapidly creating data models via a dynamic modelling solution infrastructure. This makes it easy to change and refresh models on a continuous basis (with applied, appropriate governance), as well as objectively linking what is modelled at the business level - subject area and conceptual level – to where the related data resides in physical systems of record.
Finally, enterprises can effectively leverage the modelling output as input for related data management initiatives, including (but not limited to):
- Subject area and entity definitions => business glossary
- Classified entities => centralized master data creation and maintenance
- Unique entity ids => algorithmic cross system entity matching
- Data elements => data quality requirements
- Entity associations => business rule requirements for current and future state systems
- Subject area models => structure data governance organization / business data steward roles.
By using the hybrid data modelling approach to create data model diagrams, teams can effectively create a reliable ‘line-of-sight’ for their corporate data. Using the data from physical systems of record, helps to rapidly establish and objectively link items of importance identified by business users to where these things actually reside in physical systems of record.
The end result is a picture of the forest – as well as the trees – that can provide tangible input to a roadmap of enterprise data management initiatives.
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