Empowering Data Management Systems with Data Quality Testing

In the era of rapid technological advancements, the volume of data generated by individuals and electronic devices is expanding swiftly. To keep up, companies are shifting to digital tools for managing this data, moving away from old-fashioned databases that can’t handle the load for making informed decisions.

However, these transformative processes also introduce significant challenges, particularly in managing diverse data sources within organizational structures. The root cause of these challenges often stems from poor-quality data. The strength of your data infrastructure is directly proportional to the quality of the data it handles. To establish a robust data architecture, it is imperative to seamlessly integrate data quality testing throughout the entire system, minimizing potential issues.

What are Data Management Systems?

Data Management Systems

Data Management Systems represent a contemporary evolution of traditional database systems. They encompass functions such as ingestion, storage, maintenance, processing, and seamless delivery of high-quality, synchronized, and accurate data to stakeholders. These systems establish end-to-end data flows through automated pipelines. The fundamental workflow of general data management systems involves:

  • Ingesting raw data from diverse sources like phone sensors, online payment transactions, and third-party providers.
  • Verifying the quality and accuracy of the ingested data to ensure adherence to backend logic.
  • Storing and maintaining raw data on cloud databases or on-premise systems.
  • Processing raw data in alignment with business requirements using transformation scripts.
  • Creating data products, especially in artificial intelligence or business analytics projects.
  • Automating the entire pipeline to deliver synchronized data to stakeholders within specified timeframes.

The Importance of Data Management Systems

In today’s landscape, big data and business analytics not only dominate discussions but also offer effective solutions to various challenges. To thrive and expand rapidly, competitive companies must embrace data-driven decision-making, necessitating the establishment of robust data management systems in collaboration with their data teams. Without such infrastructure, achieving favorable project outcomes becomes unattainable.

Let’s consider evaluating the importance of data management systems with this example of launching a mobile game and investing in advertising. Monitoring daily data on advertising expenses and game revenues becomes crucial. If expenditures surpass income, strategic adjustments are imperative. To facilitate informed decisions like these, a continuous, high-quality, up-to-date, and accurate data flow is essential in data management systems. Without it, businesses risk making erroneous decisions, potentially leading to financial losses and more.

What is Data Quality Testing?

Data Quality Testing refers to the application of check steps to ensure that the streaming data entering the system meets specific quality criteria. These testing processes can be either manual or automated, with automated testing pipelines saving considerable time and reducing manual errors. Key criteria for data quality testing include:

 

  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness

Source

Data Quality Testing Best Practices

Data Quality Testing Best Practices

Envision a scenario wherein you operate an e-commerce platform designed for the global sale of products. In pursuit of establishing a robust data management system to monitor product sales data, it is imperative to consider the perpetual accessibility of the website, operating 24/7, and its capability to facilitate transactions from any location worldwide.

In light of the diverse array of streaming data continuously entering the system, the implementation of the following guidelines is important for ensuring the quality of data through comprehensive testing processes:

Check Data Volume Trends

In instances where the scale of your customer base and sales surpasses a considerable magnitude, resulting in an influx of substantial data into your systems, latency issues may arise within your data pipelines. This phenomenon has the potential to create a deceptive perception of reduced sales when scrutinizing sales data. To mitigate such discrepancies, it is advisable to execute the provided SQL code sample on transactional data, enabling the tracking of trends in data volume.

SELECT date(transaction_time), count(DISTINCT transaction_id)
FROM transactions
GROUP BY 1
ORDER BY 1 DESC

Check Duplicate Records

In the event of an error or bug within your backend architecture, the inadvertent creation of duplicate records in the data flow may occur, potentially leading to an erroneous perception of inflated sales figures. To ascertain the accuracy of product sales records and identify potential duplicates, it is recommended to employ the provided sample SQL code for a thorough examination.

SELECT transaction_id, count(*)
FROM  transactions
GROUP BY 1
HAVING count(*) > 1

Check Logical Error Records

In the absence of unit testing procedures within backend systems, there is a risk of the ingress of logically flawed records into the system. Notably, discrepancies may arise, such as the occurrence of negative or positive price values in a refund transaction, which is permissible, while in a purchase transaction, a negative price is logically invalid. To identify and rectify such logical errors in records, one may execute the provided sample SQL code for thorough validation.

SELECT *
FROM transactions
WHERE transaction_type = “sales” AND price <= 0

Check Null Value Records

In practical scenarios, user data often exhibits inconsistencies. For instance, due to various reasons, data such as the color or country information of the products offered for sale may be transmitted to the system with empty values. Consequently, upon reviewing reports, the observed sales figures may inaccurately appear lower than expected. To address this issue, one can identify and manage Null value records by executing the provided sample SQL code below.

SELECT *
FROM transactions
WHERE country IS NULL OR color IS NULL

Conclusion

Engaging in data-related endeavors and leveraging data for decision-making proves immensely valuable, contingent upon the presence of high-quality data. This fundamental truth remains immutable despite the sophistication and visual appeal of data management systems. This article has highlighted the contemporary significance of data-driven decision-making, underscored the imperative of modern data architectures, and elucidated methodologies for evaluating the quality of incoming data through illustrative case studies.

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