Maintaining data quality: 3 key actions to improve data pipelines

Clean and accurate data is absolutely essential for making informed and effective decisions. With the increasing complexity and expansion of data platforms, the task of managing multiple data sources becomes challenging and requires careful attention. Initially, you may believe that your data is reliable, as it was agreed upon in your data contracts. However, ensuring its consistency and integrity over time can be a daunting task. Additionally, it is important to proactively identify any potential issues or breaches of the established data contracts.

Monitoring data quality made easy
Monitoring data quality made easy

In the following discussion, we will explore the significance of data quality in data pipelines. We will specifically focus on practical strategies and actions that can be implemented to ensure the ongoing reliability of data and to promptly address any problems that may arise.

What are data pipelines?

Before we continue, let's briefly discuss ETL (or ELT) pipelines. ETL (Extract, Transform, Load) pipelines are essential components of most data platforms. They perform the heavy lifting by extracting data from various sources, transforming it, and then storing it elsewhere.

Common tools such as Azure Data Factory, Talend, Airflow, or even custom scripts are often used to handle this process.

It is important to understand that these pipelines consist of a series of interconnected steps, where the output of one step often serves as the input for the next. This results in the creation of data products, which play important roles in analysis, machine learning, decision-making, and other data-driven processes.

As you can see, ensuring high-quality outputs at each step is of utmost importance.

Common data quality problems

Data quality issues can gradually infiltrate any data platform due to faulty source systems, unreliable data providers, glitches in the ETL pipeline, and other factors.

These problems manifest in different ways, such as incomplete and inaccurate data, inconsistencies in format, and the presence of duplicates. Outliers and non-standardized data add complexity, while data security and staleness pose risks to integrity. Poor governance and neglected business logic further exacerbate the challenge.

It is crucial to address these issues in order to maintain dependable and meaningful data.

Practical steps

The key challenge is to catch data quality issues as early as possible in your data pipelines and take steps to prevent them from evolving into major problems. To make this more practical, here are three essential actions to ensure data quality in your data pipeline:

Set clear boundaries: Establish well-defined interfaces or data contracts within your pipelines. Often, such boundaries exist but are buried in high-level documents created at the project's outset and then forgotten. Revisit and formalize these boundaries to ensure that everyone involved understands the data's purpose and structure.

Define fine-grained rules: Specify detailed rules that data from these domains must adhere to. Start by exploring data governance initiatives, as they often contain valuable data definitions and identify data owners. Clearly define what your data should look like and know who to contact if quality issues arise. This clarity streamlines the process of resolving data problems and allows you to automate these on actual datasets.

Effective communication: Ensure that data quality issues don't go unnoticed. The results of data quality checks should be communicated to data owners or individuals responsible for fixing them. This communication process is primarily business-oriented, emphasizing the importance of maintaining data quality and taking prompt corrective actions.

By implementing these practical steps, you'll significantly enhance data quality in your data pipeline and minimize the risk of data-related challenges down the road.

The Mesoica advantage

Mesoica is a data quality platform that integrates easily with new and existing data platforms, aligning with data governance initiatives. It works with various storage technologies and can handle unstructured data like Excel, PDF, and emails. Adding data quality checks and enabling business-oriented feedback loops is just a simple API call away.

Mesoica is a comprehensive data quality platform that seamlessly integrates with data pipelines, ensuring effective alignment with data governance initiatives. It is data source agnostic, working with different storage technologies and unstructured data formats like Excel, PDF, and emails.

Another key advantage of Mesoica is its ability to incorporate data quality checks into data pipelines with a simple API call. This adds robust data validation mechanisms to identify and address potential issues in the data. This ensures high-quality output in each step of the data pipeline for accurate analysis, machine learning, and decision-making.

The solution also emphasizes effective business-oriented feedback loops. It facilitates clear communication with data owners or responsible individuals when data quality issues are detected. Promptly addressing these issues helps maintain data reliability and integrity.

By incorporating data quality checks and enabling effective feedback loops, Mesoica empowers organizations to make informed decisions based on reliable and accurate data.

Mesoica’s data quality platform is designed to meet the evolving needs of today's organizations. By using our platform, you can continuously monitor data, identify trends, flag regressions, and foster communication and collaboration around data. Our platform is built to scale with your organization's growing data quality maturity needs and provide peace of mind. Start your journey towards becoming a truly data-driven organization today. Visit our website or contact us to learn more about how Mesoica can empower your organization to anticipate, prevent, and continuously improve data quality.