P&G’s Master Data Management (MDM) and metadata were not in sync among the operational reporting systems for global and regional users, who were managing and modifying data for specific regional instances. In addition, data leakage was a major concern, which limited propagation to other systems in the enterprise.
Because of the complexity of P&G’s data platforms – 48 SAP instances in a 4-tier landscape with multiple downstream application servers – the identification and reconciliation of data problems were taking time and effort. The result was that many different business units were using their own process to fix the problems, which drove the need for data governance with a data quality platform and toolset.
P&G’s data governance team leveraged the RDt data quality software platform to optimize their data quality assurance and control of their master data, including over 32 unique SAP instances and billions of records. Prior to the implementation, analysts would download all data offline on a weekly basis, combine multiple sources and manually reconcile inconsistencies in the data and variance. After an initial assessment of data quality assurance and control (DQA/DQC), a streamlined plan was developed to retire the existing third-party tool.
We know that using a comprehensive data quality platform enables trusted data. The true impact for this use case was that P&G could set policy and procedure to ensure this across the entire enterprise and unify data governance and limit data leakage and many different business unit duplication and risk. With the added benefit of auditability for data performance, the payoff is high for data stewards at P&G.
RDT provides a comprehensive approach to MDM data quality control cycle by using the elements of Define, Build, Operate, Monitor and Evaluate. The image above depicts the steps involved, and the actions in the inner circle are enabled using RightData’s platform.
Data stewards can use these processes to unify the software with policies and governance across the enterprise. As an example, after build phase, you can schedule the elements of the quality control plans (QCPs) that feeds the dashboard to management.
RDt is a comprehensive platform for data quality, risk, or compliance needs. Learn more or contact us to chat about your needs.
RDT Data Quality: A no-code data quality suite that improves data quality, reliability, consistency, and completeness of data. Data quality is a complex journey where metrics and reporting validate their work using powerful features such as:
Database Analyzer: Using Query Builder and Data Profiling, stakeholders analyze the data before using corresponding datasets in the validation and reconciliation scenarios.
Data Reconciliation: Comparing Row Counts. Compares number of rows between source and target dataset pairs and identifies tables for the row count not matching.
Data Validation: Rules based engine provides an easy interface to create validation scenarios to define validation rules against target data sets and capture exceptions.
Connectors For All Type of Data Sources: Over 150+ connectors for databases, applications, events, flat file data sources, cloud platforms, SAP sources, REST APIs, and social media platforms.
Data Quality: Ongoing discovery that requires a quality-oriented culture to improve the data and commit to continuous process improvement.
Database Profiling: Digging deep into the data source to understand the content and the structure.
Data Reconciliation: An automated data reconciliation and the validation process that checks for completeness and accuracy of your data.
Data Health Reporting: Using dashboards against metrics and business rules, a process where the health and accuracy of your data is measured, usually with specific visualization.