Data-driven businesses build robust DataOps pipelines to create innovative solutions and optimize their decision-making processes. Within an organization’s DataOps ecosystem, production-grade data pipelines require continuous data assessment to maintain high-quality data that is complete, accurate, and available 24/7. Hence, modern organizations leverage data monitoring solutions to ensure reliability and maximize the uptime of their data pipelines.
Let’s discuss what data monitoring is and explore how it helps data professionals maintain high-quality data.
To ensure a reliable, consistent, and trustworthy data pipeline, you need a holistic overview of all your data: Who’s using your data? How is it being used? Where does it come from? And who is responsible for the source data?
Data monitoring is the proactive process of measuring and evaluating your data to gain visibility over its usage and quality at every stage of the data pipeline(data extraction, transformation, orchestration, etc.). It also helps you examine the effectiveness of your data governance practices by providing visibility into data usefulness (uniformity, completeness, accuracy, accessibility, etc.).
While manual data monitoring is possible, you will require specialized tools for advanced data tracking — tools that will enable you to automate your data evaluation processes.
Think of data quality like water purity. Just as pure, clean water is essential for maintaining good health and well-being, high-quality data is essential for making accurate and effective decisions in business. However, contaminated water can go unnoticed for a long time until the harm becomes evident — and the same goes for low-quality data.
Imagine your data deteriorates over time without proper monitoring. You might have multiple pipelines running and a number of business decisions driven by them, but what if the business is actually making important decisions based on the wrong data? How successful would that business be?
That’s why data professionals spend significant time and resources preparing data for analysis or operationalization. However, without constantly monitoring your data, you can’t ensure that the data you’ve ingested and modeled is actionable, consistent, and of high quality.
This means data monitoring is vital for allowing today’s data-driven organizations to obtain actionable data whenever and wherever they require it. Data monitoring empowers data professionals with a comprehensive overview of their data landscape, enabling them to ensure a fast and steady flow of high-quality, reliable data.
It is best practice to routinely evaluate your pipelines so you can tackle data deficiencies at their source in due time. Otherwise, you could miss critical data issues like redundancy, missing values, data mismatch, ambiguity, and data drift, resulting in data quality degradation over time. Data mismatch refers to inconsistencies in data stored in different systems. Data drift, on the other hand, refers to a situation where the distribution or characteristics of a data set change over time. By enabling you to mitigate these problems, data monitoring can help preserve the data to significantly reduce maintenance overheads and improve the uptime of your pipelines.
Here is a list of six key benefits of monitoring data pipelines:
Faster debugging and minimized inconsistencies: Data monitoring captures data issues as they happen, which enables you to debug issues faster and reduces the risk of cascading data inconsistencies that can wreak havoc in your data pipeline and lead to ill-informed decision-making.
Increased business efficiency: Data monitoring empowers organizations to make decisions based on accurate and high-quality data, which can significantly boost your business performance and efficiency.
Stable and standardized data pipeline: Data monitoring is governed by set business parameters and thresholds that ensure data standardization across all pipeline stages: creation, transit, and utilization.
Faster retrieval of actionable data: Using monitoring to maintain a reliable data infrastructure enables you to provide fresh data with speed and agility, so your data teams can operationalize it promptly.
Reduced data preparation time: Data can be structured or unstructured, and it can come from various data sources. Hence,data preparation involves collecting, organizing, and transforming all data according to business requirements. It is a complex and time-consuming task, particularly when working with large amounts of data. To overcome challenges, data experts can use pipeline monitoring at every data preparation step to ensure consistency across data sets.
Cost-effective: Data monitoring saves your organization from exhausting and exceeding its budget by proactively identifying data quality issues at their source to avoid pipeline downtime and minimize maintenance.
While the concepts of data observability and data monitoring converge, there are some critical distinctions between the two.
Data observability is a DataOps practice that measures, interprets, and manages data health across all applications, systems, and servers, enabling organizations to detect and resolve data anomalies at the point of occurrence. Data health refers to the volumes, freshness, schema, distribution, and lineage of data. These are direct indicators of the quality of the data in your pipelines.
Differences between data observability and monitoring include the following:
Data observability is a contextual assessment of overall business data quality that tells you how and why data issues occur. In contrast, data monitoring is a system that checks data quality based on established rules to determine what goes wrong and when, and if a violation is detected, it sends alerts to data administrators.
Observability is a broader concept that encompasses processes like data monitoring. It features improved visibility capabilities that evaluate the state of an organization’s internal systems. While data monitoring only alerts you to data problems, data observability allows you to detect the problem’s source and obtain details about it. On top of that, observability uses machine learning techniques to recommend solutions to data problems along with details of their source.
Another key difference between monitoring and observability involves their approach to anticipating potential anomalies. Data monitoring utilizes predetermined criteria to identify data issues. On the other hand, observability takes a proactive approach to tracking unknown issues and forecasts possible abnormalities through the collection of metrics that span the whole IT landscape.
The bottom line is that data observability and data monitoring can work together to help businesses detect problems within their organization’s data ecosystem.
To prevent low-quality data from source to output, you need to have an effective data monitoring solution in place. You can streamline the process by implementing the following data monitoring practices in your organization:
Enable comprehensive data analysis: Setting up effective data monitoring starts with a complete analysis of all your organization’s data. The goal here is to ensure there’s no duplicated data, that the data is complete and accurate, that the pipeline is flowing as it should, and that the relevant stakeholders who need access to the data have it.
Ensure consistent data flow: Your business deals with data from various sources. You need to constantly monitor and transform incoming data according to your business requirements to maintain its quality throughout your pipelines. For instance, check data patterns and formats for each record, check value distribution, and identify outliers.
Establish access control: For optimal data usage, the data must reach the teams that can use it to drive actions or make meaningful decisions. A monitoring system should ensure that all business stakeholders have unhindered access to data they can utilize efficiently. For instance, implement role-based access control within the team to govern data access according to each employee’s needs.
Detect and predict data issues: It is crucial to stop data anomalies at their source so they don’t cascade along your pipelines. Set up data tests and alerts to monitor data quality issues so they can be addressed as soon as they occur. On top of detection, your data analysts can use predictive analysis based on machine learning to detect data anomalies before they occur.
Having the right tool that resonates with your organizational goals is essential to establishing a constructive data monitoring strategy. Y42’s Modern DataOps Cloud can take over the burden of data monitoring for your data team. With automated data tests and alerts, Y42 enables data professionals to keep up with the status of their data, allowing them to quickly debug issues and become more confident in the quality of their data.
Haziqa is a data scientist with extensive experience in writing technical content.