As a data engineer, do you find your efforts constantly being undermined by poor data quality? No matter the robustness of the pipelines you have built, do you feel like you are always fixing breakages? Well, you’re not alone.
Poor data quality is a pain point for which ownership — even though it affects the whole organization — ultimately falls on data engineers. And as companies’ data needs continue to grow, data quality challenges will only increase.
Imagine you are a data engineer tasked with building an ML pipeline for a customer churn prediction model for your company. To do this, you set up the required workflows for the data scientists to train the model on historical customer data, only to find out that the data is incomplete and incorrect, with numerous errors. The model’s accuracy is now compromised, and your company cannot rely on its predictions. This leads to devastating business outcomes like damaged customer trust and increased costs incurred through having to correct quality issues and retrain the model.
A data engineer’s work is critical for an organization to obtain useful data. But it’s even more critical for the data engineer to have high-quality data to work with.
In this article, we will delve into what data quality means for data engineers and explore actionable strategies that you can use to ensure your data pipelines consist of high-quality data.
The value that data engineers add through their work heavily relies on the quality of data they deliver for decision-making.
Data quality issues cascade between systems, so it’s important to maintain data quality across systems.
Knowing when and where to implement data quality checks is key to preventing quality issues from traveling downstream through the pipeline while keeping costs at an acceptable level.
Your data quality checks must examine data values across all their measurable dimensions to ensure the data is accurate and reliable.
Y42 is the perfect partner for data engineers to build fully managed, high-quality data pipelines.
Data quality refers to data’s accuracy, completeness, consistency, and relevance. It’s a measure of data’s fitness for its intended purpose (a data model, a recurring dashboard, etc.).
Having trust in their data is crucial for organizations because it impacts the effectiveness and efficiency of data-driven systems and decision-making processes. Good data quality empowers data engineers to develop and maintain robust data pipelines that reliably deliver accurate and trustworthy data to end users. If the data used in these pipelines is of poor quality, the resulting insights and decisions could be incorrect or unreliable, leading to low-quality business decisions, reduced efficiency, and increased costs.
Gartner finds that poor data quality costs organizations around $12.9 million each year on average. Economic damages can occur when products are shipped to incorrect customer addresses, sales opportunities are lost due to incomplete or incorrect customer records, or fines are incurred for non-compliance with regulatory requirements.
Therefore, data engineers must work to prevent data quality issues from arising in the first place and develop mechanisms to detect and correct any data quality issues that do occur.
While there are several data quality issues that can impact your data’s reliability, many of them fall under the following common problems:
Incorrect data is data that is factually wrong or inaccurate. It can result from errors in data collection, entry, or processing. Below are some examples of what incorrect data issues can look like:
Redundant data entries
Null or incorrect values in a column
Data that doesn’t conform to the right schema
Abrupt changes in the number of data rows
Incorrect data can lead to misleading conclusions and reporting errors, such as incorrect predictions, recommendations, and financial reporting.
Incomplete data lacks critical information or contains gaps. It can result from incomplete data collection or data entry. Even the most correct data can be useless due to a lack of key predictive factors, such as missing key customer engagement metrics. These missing predictive factors can lead to misinterpretations and difficulties in using data for its intended purpose.
Examples of data architecture design problems include inefficient data modeling and complex table structuring. Poorly organized tables make it difficult to access the right data at the right time, making it hard to integrate data from multiple sources. Inconsistent and inaccurate information may accumulate as a result, which flows through your data pipelines and delivers low-quality outcomes.
Data quality issues can be attributed to the following culprits:
Software updates or changes can cause data quality issues, especially when they are not properly tested or validated. Bad software pushes also occur due to issues with data migration or upgrades, where data is lost or corrupted during the migration process. For example, imagine you are making changes to a schema in a logger using a previously logged column. If that column is no longer logged, you have created a null column leading to incorrect data.
Schema changes are changes made to the structure of a database or data table. Data quality issues occur when schema changes are not properly managed or communicated. For example, adding a new field to a data table without updating all associated applications or processes can cause the data to be incomplete or inconsistent. Schema changes can also cause issues with data integration, as different data sources may have different schemas.
Using third-party tools to accumulate various types of data is common for many businesses. Doing so helps them to get the most out of their data with limited resources. However, this also raises data quality concerns as you don’t control the data coming from external sources. If the data is invalid, API changes are miscommunicated, or the API doesn’t comply with your quality regulations, you will end up ingesting low-quality data into your system. For example, if an API returns data in an inconsistent format or with missing fields, data integration issues can occur, which lead to data quality problems.
It is important to set up data quality checks early in your data pipelines, ideally during the data acquisition phase. This is because data quality issues can be introduced at any stage of the data lifecycle, and early detection is key to preventing downstream problems. For example, you can use data profiling and cleaning processes to identify and correct data quality issues before they have a chance to propagate throughout the pipeline.
But what about the data that moves down the pipeline? While data quality checks can be crucial for ensuring data quality in your pipelines, effort is required to write and maintain these checks. So, how do you decide where to set up the checks?
Here are some recommendations for places you can implement quality checks, depending on your pipeline requirements:
Long-execution-time pipeline: If your pipeline has a long execution time and includes complex intermediate steps to generate data assets, you should implement quality checks at the beginning and end of each pipeline stage (data capture, storage, transformation, transfer, consumption). In this way, you can test the quality of data going into and coming out of each stage and prevent errors and inconsistencies from propagating downstream.
Short-execution-time pipeline: If your pipeline has a short execution time and data timeliness is crucial, it can be difficult and time-consuming to go through quality checks at every stage of the pipeline. In this case, you can set up tests right before the data is integrated into the larger data environment so that only high-quality data makes it through to the analytics processes.
Now you know where your pipeline can benefit from data quality checks, it’s important for you to understand which types of quality checks you can use to solve your issues. Here are some of the data quality checks you can implement to prevent bad data from breaking your pipelines.
You can use completeness checks to verify that all required data fields are there, that no values are missing or that there are no null values, and that the number of records in a dataset matches your data expectations.
These checks validate the data’s structure. For instance, you can use syntactic checks to verify that a date field conforms to a “YYYY-MM-DD” format, or that an email address field contains “@” and “.” characters.
You can use validity checks to verify an email address, check if a project’s start date is before the end date, or make sure that your standards for acceptable data value ranges are respected.
Semantic checks verify that data values fall within acceptable ranges, or that data is properly labeled or categorized. For example, you can use these checks to ensure that the temperature value falls within the acceptable range for a given climate.
You can implement consistency checks to see whether a customer’s order details match across multiple systems, or that the total quantity of a product ordered does not exceed the available inventory.
Uniqueness checks assess whether there are any duplicate records or values in the data. For example, you can check for the uniqueness of customer ID values in a customer database or identify duplicate orders in your orders table.
You can set up accuracy checks to verify that data values are correct and that calculations or formulas are accurate. An example would be checking the sales data against invoices or receipts to make sure they’re free from errors.
Data quality is a critical aspect of data engineering that significantly impacts the efficiency of data-driven business decisions. As Gartner’s senior director analyst, Melody Chein, puts it: “Data quality is directly linked to the quality of decision-making.”
Therefore, to drive better business outcomes, you must implement robust data quality checks at the right places in your data pipeline. This will ensure that your data pipeline delivers correct, complete, and efficiently modeled data for reliable analytics.
But how can data engineers build and maintain high-quality data pipelines? With a fully-managed DataOps tool like Y42. With Y42, data engineers can:
Create fully transparent pipelines that they can monitor across every data operation, ensuring they can fully trust their data.
Use Y42’s APIs to set up entire pipelines programmatically.
Implement software engineering best practices in pipeline construction and maintenance.
So, if you are a data engineer who is constantly troubled by data pipeline failure alerts due to bad data, Y42 is the mission control you need for your pipelines. Book a call with our data experts today and learn more about how Y42 can take you to production-ready data pipelines and beyond.