Data management framework for the implementation of data management in enterprises
The digital transformation of businesses has brought about a fundamental change in many industries. Companies increasingly consider data a business critical and competition relevant asset enabling new kinds of products and services, as well as data-driven strategies. Managing data as a strategic asset is a challenge, as it requires from companies to revise established data management approaches and concepts in order to be more effective in gaining value from data. Data management itself is required to broaden its scope in order to cover not just master data, but analytical data and other data types as well, while extending the original focus on data quality to additional aspects, such as data compliance, data security and privacy, or data risk.
In a joint effort, comprising more than 15 European companies as well as researchers from three European universities, the Competence Center Corporate Data Quality (CC CDQ) has developed a reference model for data management in the digital economy: the Data Excellence Model. The data management framework offers support and guidance for practitioners in the implementation of data management by defining major design areas, while at the same time supporting the transformation into a digital and data-driven company.
Structure of the Data Excellence Model
Given the understanding of data as a strategic resource for the digital economy, the structure of the data management framework builds on the principles of performance management and the logic of management cycles. The reference model specifies design areas of data management in three categories: goals, enablers, and results, which are interlinked in a continuous improvement cycle.
Goals break down the overall aim and purpose of data management by outlining necessary business capabilities and data management capabilities and explicating them in the form of a data strategy;
Enablers help to achieve the goals specified with regard to six design areas: people, roles and responsibilities; performance management; processes and methods; data architecture; data lifecycle; and data applications;
Results indicate to what extent the goals are achieved in terms of two quantifiable aspects: data excellence and business value; and
Continuous improvement allows adjustment of goals and enablers, ensuring the dynamic nature of the model.
Goals
By first taking a look at and understanding the business capabilities required and then defining and developing appropriate data management capabilities, data managers can align their activities with business.
Business capabilities are sets of skills, routines, and resources a company needs to have in order to achieve business objectives. They describe what a company does (or should do) at its core, rather than why or how it does things.
Data management capabilities are sets of skills, routines, and resources a company needs to have in order to support business capabilities through data management. Typical data management capabilities refer to, for example, data ingestion, data transformation and harmonization, data processing, data provision, data modeling, and data access.
Data strategy defines the scope and objectives of data management and specifies the roadmap for providing the data management capabilities required.
Enablers
Enablers define the design areas of data management which are to be considered for providing the data management capabilities required.
People, roles and responsibilities defines the skills and organization to ensure effective data management and consistent use of data across the entire organization.
Performance management defines the measures to monitor and control the performance (i.e., progress and outcome) of data management with the help of a key performance indicator system.
Processes and methods defines procedures and standards for managing and using data properly and consistently.
Data architecture defines the conceptual data model, specifies which data is stored in which application, and describes how data flows between applications.
Data lifecycle defines data objects and documents, and reviews data sources, operational data activities (i.e.,ranging from data acquisition and creation to data archiving), data consumers, and data use contexts.
Data applications defines the software components supporting data management activities.
Results
The results of data management are twofold: first, and most obviously, data management has a direct impact on the data itself, defined as "data excellence" in the reference model; second, data management adds value to business, defined as "business value" in the reference model.
Data excellence refers to the impact of data management on the data itself, first and foremost with regard to data quality (defined as “fitness for purpose”), but also with regard to additional data related aspects, such as data compliance, data security and privacy, or data risk.
Business value refers to the impact of data management on business with regard to financials, business processes, customers, and organizational growth.
Usage of the Data Excellence Model
Please note that the Data Excellence Model as developed by the Competence Center Corporate Data Quality (CC CDQ) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This CC-BY-SA license states that you can use, copy and redistribute the Data Excellence Model data management framework, provided that you give appropriate credit (i.e. indicating the Competence Center Corporate Data Quality /CC CDQ as the author of the model), provide a link to the license on your website, and indicate all changes you made to the Model.
You may adapt the Data Excellence Model to the requirements of your company by, for example, renaming the design areas or changing the colors. If you do so, you must publish your version of the data management framework under the same license the original model is distributed under.
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