The Importance of Conduction a Finance Data Quality Assessment within an SAP Landscape

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Data Quality Assessment
Babatunde Michael Oladunmoye

Babatunde Michael Oladunmoye

In the action-packed world of finance, accurate and reliable data is the cornerstone of informed decision-making. Modern businesses rely heavily on data to drive critical processes such as financial reporting, risk management, investment analysis, and regulatory compliance. However, the sheer volume and complexity of financial data make it susceptible to errors, inconsistencies, and inaccuracies. To mitigate these risks, especially within a large and multidimensional Enterprise Resource Planning (ERP) landscape, it is recommended that robust data quality assessment methodologies should be employed to ensure the continued integrity of data. 

The Importance of Data Quality in Finance:  

Data quality refers to the accuracy, consistency, completeness, and reliability of financial data. In the finance sector, even a small error can have significant repercussions. Ensuring high-quality data is paramount. Poor data quality can lead to inaccurate financial reports, wrong risk assessments, and regulatory non-compliance, all of which can result in financial losses and damage the reputation of organizations.  

It is important that Data Quality is closely monitored in businesses that use ERPs like SAP S/4 HANA, especially large organizations that have complex operations, possibly spanning across multiple company codes, multiple locations or countries. Running the SAP S/4 HANA system with a “Laissez-faire” attitude, without paying proper attention to data input, could result in dire consequences for such organizations.  

Conducting a Data Quality Assessment (DQA) on SAP involves examining two primary types of data generated within the ERP system: Master Data and Transactional Data. Master Data includes information about entities like customers, vendors, and materials, while Transactional Data involves records of day-to-day transactions.  

It is imperative to verify that Master Data is created in adherence to SAP best practices and organizational policies established for running the ERP system. For example, it is crucial to scrutinize whether General Ledger (GL) accounts are created within the correct account group. GL accounts associated with general expenses should not be placed in a group designated for Payroll expenses.  

Transactional Data requires thorough investigation to ensure end-to-end adherence to processes and procedures, guaranteeing that financial data input aligns with the system’s design. For instance, it is necessary to validate that all processes related to period-end closing are executed within specified timelines. Neglecting any of these processes may result in the generation of inaccurate financial data in reports, potentially leading to financial losses or regulatory issues.  

Key Components of a Finance Data Quality Assessment

What should be considered in a finance Data Quality Assessment? Below are components that should be examined and practical areas to be checked to ensure a concise and complete data quality assessment exercise.  

Accuracy:  

Accurate financial data is essential for precise calculations and reliable analytics. Here, for example, checks could be done to ensure that account codes correctly reflect the nature of the transactions they contain. The Finance data quality assessment involves validating the data against the sources, identifying and rectifying discrepancies, and implementing mechanisms to prevent errors at the data entry stage.   

Consistency:  

Consistency ensures that data remains uniform across different datasets and time periods. Inconsistencies can arise from data integration, system upgrades, or human error. Checks are therefore carried out for uniformity in naming conventions, classifications, and master data structures. Questions such as “Are coding structures being followed across the whole organization over different periods? Should be asked.  

Implementing standardized data formats, coding conventions, and regular reconciliation processes can help maintain consistency.   

Completeness:  

Complete data is essential for comprehensive analysis. Data completeness is verified by checking that all required data points are present and that there are no gaps or missing values in them. Checks should also ensure that all relevant fields are populated in all master data and transactional data. Using inbuilt tools on SAP S/4 HANA, like validation and substitution, can assist in automating validation checks and aid in identifying and addressing completeness issues.  

Uniqueness  

Duplication of data leads to the unnecessary bloating of data elements within the system. When duplication continues unchecked, it could lead to confusion, redundancies, and possible exclusion of important data during reporting and building of financial models. It is important that a DQA exercise looks into the issue of the uniqueness of master data objects.  

The implementation of a robust Master Data Management Process to mitigate against this possibility should be recommended. SAP Master Data Governance Module provides this functionality and more for organizations to take advantage of.  

Reliability  

Reliability is a crucial component of finance data quality assessment that underscores the importance of consistency, accuracy, and trustworthiness in financial information. Reliable data can be depended upon for making informed decisions and conducting analyses with confidence.  

Reliability also refers to the trustworthiness of financial data. The DQA involves evaluating the data source’s credibility, establishing data lineage, and implementing validation rules, ensuring that figures from multiple reporting platforms are plugged in correctly to the right sources on the ERP system. Regular audits and validation checks help maintain the reliability of financial data.  

S/4HANA Cloud

Executing a Finance Data Quality Assessment Exercise 

Below are recommended steps to follow in ensuring the successful execution of a Finance DQA Exercise.   

Define Objectives and Scope:  

Clearly outline the goals and scope of the data quality assessment project. Identify specific objectives, such as improving accuracy, ensuring compliance, or enhancing data governance. Define the scope by specifying the data sources, systems, and processes that will be evaluated.    

Build a Cross-Functional Team:  

Assemble a multidisciplinary team involving members from finance, IT, data governance, and compliance. A diverse team ensures a comprehensive understanding of both business requirements and technical intricacies.  

Understand Data Requirements:  

Conduct a thorough analysis of data requirements. Engage stakeholders to determine critical data elements, key performance indicators (KPIs), and regulatory compliance standards. This step establishes a foundation for assessing data quality against defined criteria.   

Data Profiling and Exploration:  

Utilize data profiling tools like SAP Analytics Cloud, Power BI, or Qlik Sense to examine the characteristics of the financial data. Identify patterns, anomalies, and outliers. This step helps in understanding data distributions, discovering data issues, and setting benchmarks for data quality.   

Create Data Quality Metrics:  

Define measurable data quality metrics aligned with project objectives. Metrics may include accuracy rates, completeness percentages, and timeliness benchmarks. Establishing clear metrics facilitates the quantifiable assessment of data quality improvements.   

Implement Data Quality Rules:  

Develop and implement data quality rules based on industry standards, regulatory requirements, and business needs. These rules act as automated checks to validate data accuracy, consistency, completeness, and reliability. Regularly update and refine these rules as needed.   

Data Cleansing and Enrichment:  

Address identified data issues through cleansing and enrichment processes. This may involve correcting inaccuracies, filling in missing values, or enriching data with additional information. Ensure that cleansing processes align with business rules and do not introduce new errors.  

Establish Data Governance Framework:  

Strengthen data governance practices by documenting policies, procedures, and responsibilities. Clearly define roles for data stewards and establish protocols for data quality monitoring, issue resolution, and ongoing maintenance.  

Automate Monitoring and Alerts:  

Implement automated monitoring tools to continuously track data quality metrics. Configure alerts for anomalies or deviations from established benchmarks. Automation ensures real-time visibility into data quality and enables prompt response to emerging issues. Tools like SAP Analytics Cloud could be deployed to deliver the required automated reports.  

Conduct Regular Audits and Assessments:  

Schedule periodic audits and assessments to ensure the sustained quality of financial data. Evaluate the effectiveness of implemented measures and identify areas for continuous improvement. Regular reviews also support proactive risk management.  

Document and Communicate Results:  

Document the findings, improvements, and ongoing challenges discovered throughout the finance data quality assessment project. Communicate results to stakeholders, ensuring transparency and fostering collaboration for future data quality initiatives.   

Conclusion:  

Executing a finance data quality assessment project is a strategic investment in the reliability and accuracy of financial information. By following a structured approach such as outlined above, organizations can not only identify and rectify data quality issues but also establish a robust foundation for informed decision-making and regulatory compliance.   

Embracing a proactive stance towards data quality reinforces the resilience and competitiveness of financial institutions in an age where accurate data is synonymous with success.  

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