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Validation and Building Query Chains to Sync Financial Reporting (Granite)

Validation and Building Query Chains to Sync Financial Reporting (Granite)

June 22, 2022
solution-sepreator
“RightData is simple to use. You can easily build data connections across various source systems, design queries/query chains, reconciliations and configure batch job scheduling. It includes an email notification system and exception reporting that is used for our SOX control evidence.” Granite Financial Transformation Manager
Industry:
General Construction Contractor
Company Size:
<10,000
Data Sources:
JD Edwards E1 (Oracle), SQL Server Operational Data Store (ODS), SQL Server Tabular Model
RDt Use case:
Data integrity validation for financial warehouse and Sarbanes-Oxley (SOX) compliance
Departments supported:
Finance, legal and enterprise-wide compliance
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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 RDt platform to develop automated data quality control processes to verify the integrity between Granite’s transactional system and financial reporting systems. RDt 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 RDt’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 RDt, 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. RDt data quality software was easy to use and continues to perform SOX and deep compliance functions across the enterprise.

Learn more about RDt

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.