How to Build a Data Monitoring System
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As organizations become more data-driven, decision-makers increasingly use analytical and predictive systems to aid them in understanding their data. However, to ensure trust in these systems, a data quality strategy and set of standards should be in place. Many organizations use data monitoring systems to ensure their data meets a set of predefined standards and also to detect new data anomalies and outliers.
Building a data monitoring system from the ground up can be very challenging, and significant effort and resources are needed to design, develop, and maintain such a system. In this article, you'll learn about the implications of creating a data monitoring system and the various aspects that should be considered in this process.
1. Use the Right Data Metrics
A data metric is a standard of measurement to assess the data that's being monitored. Metrics are typically defined relative to a standard value, which could be the best possible scenario (or the worst case). These metrics must be selected carefully and be appropriate for your particular use case. For data quality, they include completeness, uniqueness, freshness, validity, accuracy, and consistency.
The common metric known as the anomaly score is a good example of how you can set your metrics. The anomaly score can be in a range from 0 to 100, where the closer to 100 the value is, the more anomalous the data being monitored is, and the more likely that an error is occurring.
2. Set Appropriate Thresholds for Data Metrics
After selecting the right metrics for your use case, your data managers should receive updates about the state of the system to help monitor it effectively and efficiently. One way to go about this is through thresholds and setting up notifications that let data managers know when data falls out of expected ranges.
For instance, if a metric is set to validate a discount code but the transactions fall outside the discounted range, the data managers would then be notified to take appropriate steps to handle the situation. In addition to predetermined and expected behavior in data, thresholds can also be created in response to certain trends and seasonal variations. For instance, if your system's data volume is known to peak during festive periods, you can build a predictable model to determine future trends and measure your data quality against a predictable threshold
To detect anomalies, you would establish thresholds at which you want alerts to trigger. These thresholds represent the point where the problem is severe enough to require an alert. Multiple thresholds can be set to communicate different degrees of severity and when these thresholds are reached, the appropriate data owner is immediately notified.
Designing thresholds requires proper knowledge of the data, possibly from business stakeholders, profiling the data, or deploying machine learning techniques that learn from the data and its trends to impose the right thresholds at the right time.
Improperly designed thresholds can lead to an overwhelming number of alerts for errors that may not be serious or even need any attention. Inevitably, this would lead to your alerts becoming useless.
3. Integrate with Every Step of Your Data Pipeline
As you design your data monitoring system make sure you you can integrates it within every step of your data pipeline.
This integration should tie into your data ingest and onboarding, and into every step of your data transformation, data warehouse or data lake systems, as well as any application APIs along the way, or even data streaming queues depending on the nature of your data stack.
You data monitoring logic should be able to measure the quality of the data from complex and semi-structured sources as easily as it is able to monitor data in warehouses and analytical databases. The sooner and the more upstream your data quality issues are identified, the lower the cost of its implications are downstream. Given that the structure and format of the data varies from system to system, all of this comes at a high cost of engineering work.
Additionally, consider hosting these services on cloud servers or even local servers and implications of building backup, recovery, security, and other infrastructure considerations.
4. Detect Issues Before They Become Real Problems
After your monitoring system integrates with your data pipeline, computes the data quality metrics, and defines alerting thresholds, your data owners should be able to see alerts within your system or get notified in their preferred channels.
To inform and communicate data quality issues with the broader team of data owners or data consumers you should set up notifications and integrate alerts into daily communication and productivity tools such as Slack, email applications, and so on. This helps you reach desired recipients early and before data quality issues cause severe downstream implications.
5. Create an Interactive Dashboard
Having a central dashboard for the data monitoring and alerting system is crucial for ease of use and daily tracking, analyzing, investigating, and remediating data quality issues. Without it, you'd have to code to retrieve raw data points that measure the various metrics within your system.
For example, you would have to query a zip code field to see the number of rows that match a specific pattern vs. another. With hundreds of tables and thousands of fields in question this can easily become unmanageable, and without visualizations and interactive investigations, this data is not easily understandable, particularly when the system has been running for a long time.
Interactive visualizations help communicate important points succinctly and in an intuative way. For example, drilling into an anomaly in the data can narrow it down to the specific data points that are contributing to this particular anomaly. Additionally, with dashboards all recorded metrics can be out into time series to provide a one-stop overview of the state of a system through time, with the ability to drill down into the details and route causes behind the scene.
6. Make it Scalable
As should be the case with every system, data monitoring systems need to be scalable. With the growth of any organization, its volumes of data typically increase. To monitor this increasing volume over time and to analyze its historical trends, the data monitoring system should be equipped to scale without an impact on performance.
If a monitoring system is not scalable, it could easily run out of compute power with an unprecedented increase or a complex calculation. This halt in the system could be costly for the organization.
As you design, develop, and deploy your monitoring system, scalability must always be in the foreground of your mind to ensure continuous and consistent operation when the system is live.
Are You Sure You Want to Build a Data Monitoring System from Scratch?
The build-out of your data monitoring system is the first cost to consider. Even greater, though, is the cost of more engineers for maintenance and management.
Let Telmai do the heavy lifting involved in both, with a no-code interface you can use to set up data monitoring in your pipeline and integrate with your data regardless of their structure or format.
- Telmai comes out of the box with 40+ different metrics, along with machine learning and intelligent algorithms to detect previously unknown data quality issues.
- Telmai provides an intuitive and interactive visual interface for monitoring your data, and it also integrates with common communication channels such as Slack or email.
- Telmai's architecture is Spark-based, so it scales as your data grows.
Try out Telmai today.
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Data Observability
Data Quality
Leverages ML and statistical analysis to learn from the data and identify potential issues, and can also validate data against predefined rules
Uses predefined metrics from a known set of policies to understand the health of the data
Detects, investigates the root cause of issues, and helps remediate
Detects and helps remediate.
Examples: continuous monitoring, alerting on anomalies or drifts, and operationalizing the findings into data flows
Examples: data validation, data cleansing, data standardization
Low-code / no-code to accelerate time to value and lower cost
Ongoing maintenance, tweaking, and testing data quality rules adds to its costs
Enables both business and technical teams to participate in data quality and monitoring initiatives
Designed mainly for technical teams who can implement ETL workflows or open source data validation software
Start your data observibility today
Connect your data and start generating a baseline in less than 10 minutes.
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Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.