Data Quality in the world of Data Engineering

Data Quality in the world of Data Engineering
July 7, 2021 | Data Quality

Data Quality in the world of Data Engineering

Introduction to data engineering

Unlike other pipeline systems in our world, data pipelines are not designed to carry oil or gas. They need to be engineered to work seamlessly and are regularly monitored. How do you ensure that your data pipeline is not only generating high-quality data but also in a meaningful way? When was the last time that you tested your pipeline for data quality? Quality controls are critical to the success of big data projects. They help ensure that the data is properly processed and stored.

In theory, everyone is responsible for the quality of data. But in reality, everyone needs to take ownership of the problem. Data engineers are tasked with investigating the issue, informing others about it, and helping make the right decisions. The more people you know who have access to the data, the more they tend to think about how it can be used. For instance, if a business leader is looking for sales data for a specific product category, they might ask: Can we look at this data across all products? You can’t rely on the data from pipelines to fully observe what’s happening in real time. Without accurate benchmarks, you can easily get off a cliff. This guide about data quality in data engineering will help you identify and address the various issues that arise when it comes to data quality. It also talks about the various types of data quality issues that arise when working with pipeline data.

Characteristics of data quality

In data engineering, data quality is often a different concept from academic conceptions for data engineers. Instead of focusing on statistical notions, they need a more credible and straightforward map to help them identify issues with pipeline data. We’ve condensed the typical data quality dimensions into just four. We prefer the term “data quality” to refer to an ongoing system that is required to be managed.

Fitness

No two companies are identical. So, fitness is always an eye-opener for anyone who uses it. To test your fitness, take a sample of records and see how they perform for you.

Within fitness, look at:

  • Accuracy—does the data reflect reality
  • Integrity—does the fitness remain high through the data’s lifecycle?

Lineage

It helps you identify if your data health problem stems from your provider or pipeline.

Within lineage, look at:

  • Source—is my data source provider behaving well?
  • Origin—where did the data already in my database come from?

Governance

These are the tools you can use to control what happens to your data, or restrict it.

Within governance, look at:

  • Data Controls- How should data be governed and open?
  • Data Privacy- Who has sensitive information (PII)?
  • Security—who has access to the data? Can I control it? With enough granularity?
  • Regulation- Can we track and prove we’re compliant?

Stability

Your data may be fit, but is it accurate? If it is, then it may vary widely, or it can be stable. Stability is often neglected when it comes to data observability tools. There are many areas where data observability can help.

Within stability, look at:

  • Consistency—is the data going in the same direction? If it does, does it mean the same thing?
  • Dependability—the data is present when needed.
  • Timeliness—is it on time?
  • Bias—is there bias in the data? Is it representative of reality?

Good data quality for data engineers

Good data quality for data engineers comes when you have a pipeline that addresses all four data quality dimensions. This includes ensuring that the data is stable, consistent, and fit to purpose. As a data engineer, you have to tackle all aspects of data quality. This article about data quality in data engineering will help you identify the various components of data quality and build effective pipeline solutions. If you’re not optimizing for one dimension, you may be loading data that doesn’t match up with the ideal quality. This can cause fitness to suffer. On the ISS, engineers must keep a balance between oxygen, water, and pressure. Getting too low can kill an astronaut.

In our previous blog series on the DQLabs.ai blogs section, we have talked about data quality management, which entails the processes adopted by organizations to ensure data quality. The processes are geared towards deriving useful insights from data to draw accurate business results. Some which include;

Accurate gathering of data requirements

This is an important aspect of having good data quality. It aims at satisfying the requirements and delivering the data to clients and users for the purpose the data is intended.

Monitoring and cleansing data

Monitoring and cleansing data involves verifying data against standard statistical measures. It involves validating data against matching defined descriptions and uncovering relationships within the data. This step also checks for the uniqueness of data and analyzes it for reusability.

Access control

Access control goes hand-in-hand with audit trails. People within an organization without proper access may have malicious intent and can do grave harm to vital data. Systems should ensure audit trails are clear and tamper-proof. These are not only a safety measure, but also help to trace a problem, when it occurs.

Validate data input

A good system should require input validation from data from all sources – known and unknown. Data sources could be; users, other applications, and external sources. To enhance accuracy, all data should be verified and validated.

Remove duplicate data

Sensitive data from a repository in an organization can find its place in a document, spreadsheet, email, or in shared folders where users without proper access can tamper with it, and introduce duplicates. Cleaning up stray data and removing duplicates ensures data quality and integrity.

Conclusion

In the world of data engineering, we need data engineers to operate like other pipeline engineers — to be concerned and focused on the quality of the data running through their pipelines. To coordinate with the data science team or to implement standard testing. This can be as simple as schema validation and null checks. Ideally, you could also test for expected value ranges, private or sensitive data exposure, or sample data over time for statistical testing (i.e. testing distributions or other properties the data at large should have).