Affecting almost every single business in an economy, digitization and the digital transformation are one of the most important developments in recent years. The components driving these developments are the exponential increase of data, interconnectivity, and processing power. Therefore, it is not surprising that many consider data to be the “new oil”. Understanding data as a central asset becomes increasingly important and will determine the success in the digital future. However, effective processing and management of data becomes more complex with increasing volume and interconnectivity. Especially financial institutions are confronted with various data related challenges resulting from regulatory requirements and new players in the market. To successfully tackle these challenges, they need to improve the quality and harmonization of their data and need to be able to reconcile these data seamlessly and efficiently. The foundation to do so is the implementation of a comprehensive data management framework.
This Article is part of our blog article series dedicated to the potentials and challenges of “Digital Data Management” and deals with the central role of data dictionaries in the context of data management.
The data dictionary as part of the DMF approach
Based on our data management experiences, Capgemini Invent developed the Data Management Flywheel (DMF) approach. The DMF provides a structured approach facilitating data sourcing and enables a consistent data collection process in line with central requirements. The DMF consists of nine subsequent building blocks, each being prerequisite to streamline and harmonize the data sourcing processes. The results of the nine building blocks are captured in a data dictionary which is the single source of truth for data definitions.
General purpose & scope of a data dictionary
The Data Dictionary is an overarching tool to document all important information about a firm’s data management such as data architectures, illustrations of data trees and critical data elements. It provides the common language for the understanding of terms, definitions, and data models. It further comprises central data catalogues (functional and technical), anchors data roles and responsibilities, and stores (or at least references) data quality rules. As the single source of truth, it is the central tool to steer all company-wide data management activities and metadata. Commercial data dictionary tools are currently available from multiple vendors but must be customized to the processes and data requirements of the individual organization.
Why the establishment of a central data dictionary is key?
While the DMF methodology is used as a framework, which can repeatedly be applied when data sourcing requirements change, the data dictionary represents the central repository where the results of the data sourcing and information collection are stored and prepared for institution wide rollout.
The information flow between the DMF and the data dictionary runs in two directions. Initially, the existing information is used to determine the data requests together with their relations, reference data, sources and quality requirements. The changes incurred in the project using the DMF are then documented in the data dictionary, keeping it up to date.
Together, the DMF and the data dictionary enable an organization for a comprehensive data management.
We at Capgemini Invent see the establishment of a data dictionary as key success factor and as an opportunity to get into control of the data and start the journey to become a truly data driven company. Our experts will help you along this journey end-to-end: from the as-is analysis to the implementation.
This article was written in collaboration with Leopold Baumgartner.