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Data Trust for Compliance and Audit (RPMI)

Data Trust for Compliance and Audit (RPMI)

June 22, 2022
solution-sepreator
“RightData gave back 50% of business analyst time using the impact of its RDt software. Trusted and true data really makes a difference for strict compliance in our business as well.” RPMI Business Analyst
Industry:
Financial Services - UK Pension Fund
Company Size:
<250
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:
Business analysts and compliance
Download

The Need

RPMI Railpen is one of the UK’s largest and longest established pension funds. Their source data used a custom developed data quality and testing solution in preparation for downstream data management analytical processes. Business analysts managed data quality control (DQC) and run queries on data sources and marks, then feed that data to a Tableau dashboard, and use a custom developed tool to verify the data.

The manual process directly impacted the team’s productivity and consumed an estimated 50% of the business analyst’s time. Along with costing valuable time, the manual approach exposed other risks in the areas of compliance, credibility and financial audit. A comprehensive solution was needed to feed accurate data the Tableau dashboard, along with compliance and financial governance.

The Solution

RPMI trusted RightData to automate their financial data reconciliation and validation processes, giving back hours of productivity to their analysts. RPMI analysts were tasked with manually reconciling and validating financial data. The proactive identification and alerting process, along with a detailed validation exceptions report helped to reduce the time to find the resolution of data quality issues. Benefits included ensuring 100% data accuracy with no programming needed, more time for the analysts, and risk mitigation for the entire data flow for the enterprise.

The Impact

The Railpen data quality team can confidently attest to the quality of the data through process and audit – that means true reporting onto Tableau dashboards are considered timely and accurate. In addition, because automated data quality processes replace manual inspection by data analysts, the software approach saves time and money.

The RightData Edge

The RDt suite of tools allows Railpen to work forwards with proactive quality processes and business rules, as well as backwards for important audit. Using the flexible RDt platform, along with the expertise of RightData support has allowed Railpen to continually improve both the process and the data quality 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.