Validation and Building Query Chains to Sync Financial Reporting (Granite)
The Need
Granite Construction, a large American general contractor, construction management, and materials production company, manages complex data warehouses and real-time financial dashboards that pull data from disparate sources with multiple views, and models. As a public company, Granite adheres to strict regulatory standards for financial reporting and Sarbanes-Oxley (SOX) compliance. Ensuring data quality is critically important, but continually challenged with the daily volume and variety of data flowing across the entire enterprise.
The Solution
Granite leveraged RightData’s DataTrust platform to develop automated data quality control processes to verify the integrity between Granite’s transactional system and financial reporting systems. DataTrust proactively identified data quality risk and helped timely remediation by preventing the out-of-compliance risk.
Granite connected to the data sources which included JD Edwards E1 (Oracle), SQL Server ODS, and SQL Server Tabular Model output via MS Excel. Using DataTrust’s reconciliation feature, Granite built queries and query chains to reconcile E1 to ODS and E1 to the Tabular Model on a scheduled basis to confirm the data sources are in sync.
A major point is that in the event of an error, the error reports identify the unique keys needed to investigate the out-of-balance condition. Shown in the diagram is a top-level view of the SOX Compliance Workflow.
Daily risk assessment and compliance is fundamental to efficient enterprise management. With penalties so high for non-compliance, data quality assessment and remediation across the workflow is a must for every organization.
SOX compliance and legal obligation is just a smart business practice. The Sarbanes-Oxley Act (SOX) established rules to protect the public from fraudulent or predatory practices by corporations and other business entities. In addition, safeguarding data and governing access to internal financial systems also reduces the risk of data cyber threats from insiders or cyberattacks. Finally, the automation by a data quality platform, such as DataTrust, saves time and money as well as creates an audit control for the entire process.
The RightData Edge
Granite reported that the RightData team provided expert advisement during setup and troubleshooting for unexpected use of the software. DataTrust data quality software was easy to use and continues to perform SOX and deep compliance functions across the enterprise.
Learn more about DataTrust
DataTrust is a comprehensive platform for data quality, risk, or compliance needs. Learn more or contact us to chat about your needs.
DataTrust 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 discover 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.
