The SQA2 Blog: Software Quality
Data quality requires having data that’s accurate, consistent, complete, and useful. And, data relies on quality software to ensure accuracy. Think of it like this: If your software can’t be trusted, neither can the data it provides, making your decisions based on that data questionable at best. High-quality software and data work hand in hand to ensure better decision-making, fewer errors, and increased operational efficiency, leading to improved return on investment by minimizing costly mistakes. Organizations that recognize the strategic value of clean, accessible data and high-functioning software turn quality into a competitive edge.
Why Do Data and Software Quality Matter for ROI?
When your software and data are solid, you make smarter decisions. Reliable, quality software and data reduce errors and streamline processes. In the end, that boosts your return on investment because you avoid expensive mistakes and optimize performance.
Who Benefits from Data and Software Quality Assurance?
Jeremiah De Leon is the VP & Software Quality Architect at SQA2. He explains, “Accurate software and data matter across many industries. They drive better decision-making, improve efficiency, and reduce risks. Poor quality leads to costly errors, regulatory issues, and damaged trust, affecting everything from patient care to financial stability and customer satisfaction.”
The Importance of Software Quality in Healthcare
Doctors and hospitals rely on patient data and the software that manages it to make critical decisions. Reliable software means more accurate treatment and smoother billing processes. It also enables better decision-making, especially in clinical trials and research.
Software glitches or failures have substantial consequences. They can lead to misdiagnosis, improper treatments, and delays in care. This can result in increased costs, patient safety risks, and legal liabilities due to regulatory non-compliance.
The Importance of Software Quality in Finance
Banks and financial institutions depend on flawless data and reliable software for managing risks and reporting. It means they can make sound decisions and adhere to regulatory compliance (e.g., adhering to laws like SOX or Basel III).
Errors in software can lead to incorrect risk assessments, financial losses, and non-compliance with industry regulations. This can damage a company’s reputation and result in significant legal and financial penalties.
The Importance of Software Quality in E-commerce
In e-commerce, software quality helps businesses manage inventory, tailor customer experiences, and keep orders flowing smoothly. If something goes wrong, you’re looking at stock problems, delivery mishaps, and unhappy customers, ultimately affecting brand loyalty and sales.
The Importance of Software Quality in Manufacturing
From the supply chain to the production floor, manufacturers need quality software. It keeps manufacturing processes running efficiently, from supply chain management to product quality assurance.
But, unreliable software can lead to production delays or material shortages, while faulty quality control can result in defective products. Both scenarios lead to increased costs, customer dissatisfaction, and potential recalls.
The Importance of Software Quality in Insurance
Insurance companies use software to process data, assess risks, and set prices. With high-quality software, insurers can achieve accurate risk assessments, better pricing models, and improved claims management. It helps ensure regulatory compliance and enhances customer trust by providing accurate and timely policy information.
Meanwhile, unreliable software could result in overpricing or underpricing policies. Errors in claim records could even lead to incorrect claim denials or approvals, damaging customer relationships and exposing the company to costly regulatory issues.
How Do You Measure Data Quality?
The reliability of your data depends on the quality of your software. De Leon explains, “Measuring data quality involves assessing factors like accuracy, completeness, consistency, timeliness, and relevance through automated and manual checks against defined requirements and standards.” Here are some of the key steps:
Step 1: Data Profiling and Auditing
The initial assessment involves performing a data audit to assess its current state and check for potential issues, such as missing or inconsistent values. This step also includes a data duplication check to ensure that no duplicate records exist, especially in critical fields like customer IDs or transaction records. Data profiling tools can automate this process.
Step 2: Step Validation at Ingestion (Data Source)
As data enters the data store, applying validation checks ensures correct formats, valid data types, and adherence to pre-set business rules (e.g., date formats, numeric ranges) — a process known as source data validation. This step also assesses data completeness to make sure that all necessary fields are populated, with no missing mandatory data.
Step 3: ETL Process Checks
The accuracy of transformation phase tests the validity of applied business rules (e.g., aggregations, conversions). Automated tests should verify accurate data transformation without introducing errors. This step also checks the referential integrity to confirm that the relationships between data sets (e.g., foreign keys linking to primary keys) remain intact, with no existing orphaned or mismatched records.
Step 4: Automated Testing Throughout the Pipeline
Automated data quality checks require setting up automated validation steps at various points of the ETL pipeline. These checks continuously monitor data quality for completeness, accuracy, and conformity to business rules. Automation ensures data validation at scale and in real time, identifying defects early in the process. In addition, outlier detection automates the detection of anomalies, such as revenue figures that fall outside expected bounds, to quickly flag potential issues.
Step 5: Post-Load Validation (Data Mart)
Business logic validation reviews the reports so that they accurately represent the data, including calculated metrics like revenue or performance indicators. This critical step tests the final output to verify that business rules, calculations, and aggregations applied in the reports reflect the true state of the data.
Step 6: Data Surface Validation (Reports)
Consistency and accuracy checks validate that the loaded data matches the data from the source and transformations, for example, comparing row counts and verifying key metrics for correctness.
How Can Data and Software Quality Impact Your ROI?
Data and software quality issues can negatively impact decision-making, lead to financial losses, and trigger compliance nightmares. Some of the most common problems include:
- Missing data: This impacts data processing and reporting accuracy.
- Incorrect data: Incorrect data can negatively impact business decisions, lead to financial losses, and possible legal ramifications.
- Duplicate data: This impacts the accuracy of reporting as well as data storage costs.
- Incorrect calculations: This can lead to inaccurate reporting.
- Data format errors: This can lead to processing issues, inaccurate reporting, and calculation errors when analyzing data across different software systems.
Real-World Impact of Data and Software Quality
De Leon has seen the real-world impact of data and software quality for a Business Intelligence BI project. According to him, “We were testing the full data pipeline, starting from the ingestion of raw data into a Data Store, followed by the ETL (Extract, Transform, Load) process that moved the data into a Data Mart. Finally, the data surfaced in a report that displayed key metrics, including product revenue.
“Everything seemed fine, but one report showed a negative revenue figure for a specific product, for a product known to be profitable. It turned out a transformation error in the data processing stage flipped the revenue. Although individual tests passed, the overall system failed to catch this mistake. This error would have led to bad decisions if not caught, showing the importance of testing the entire data flow, not just individual steps,” adds De Leon.
By checking data and software quality in your strategy, you can enhance the efficiency and effectiveness of your quality assurance processes. These strategies will help ensure that your software meets the highest quality standards.
At SQA2, we offer high-quality software quality assurance engineers to help you improve your releases. Our services are designed to make your software launches more efficient, allowing you to launch reliably and with fewer defects. Our processes and tools follow best practices, ensuring you deliver projects on time, every time. Partner with SQA2 to get more done faster.
