Selecting the best Data Quality Monitoring tool for your organization is a critical decision. With countless options available in the market, it can be daunting to make the right choice.
To help fast-track your Data Quality journey, we’ve compiled a list of 10 essential questions to ask when choosing a Data Quality Monitoring tool.
Whether you’re a data analyst, data engineer, data scientist, IT professional, or a business leader, these questions will help guide you through the initial process of evaluating and picking a Data Quality Monitoring platform to meet your organization’s specific requirements.
We’ll explore the key considerations, features, and capabilities that should be on your radar as you search for the best Data Quality Monitoring solution for your company.
With this list of 10 questions, you’ll be well-equipped to choose the ideal Data Quality Monitoring platform that aligns with your organization’s key goals and objectives.
1. Do we need to monitor our pipelines, data, or both?
If you’re just getting started, monitoring your pipeline is a good first step.
This allows you to answer questions such as:
- Is my table growing as expected?
- When was the table last updated?
However, you may quickly find that isn’t enough.
How so?
Your table is growing as expected. Great.
But, what if the rows populating it are (mistakenly) duplicates of last month’s data, instead of newly reported data? That won’t be caught by pipeline monitoring.
Your table was also last updated an hour ago, as expected. You should have fresh data, right?
Hopefully…
The only way to know for sure is to check the timestamp of the rows that were added. But that can’t be done with pipeline monitoring, alone.
If your goal is to simply get a performance overview of how your pipeline is updating your tables, a tool that checks metadata will answer your questions.
But, if you want to track deep data metrics to understand the health of the data inside your pipelines, you’ll need a Data Quality Monitoring tool that analyzes the actual data.
2. Do we have data consumers that understand the business context of the data and need answers to specific questions?
If so, you’ll need a Data Quality Monitoring tool that lets you easily apply their business-specific questions to the data.
If your data consumers aren’t advanced technical coders, but understand the fundamentals of SQL logic, consider a no-code/low-code Data Quality Monitoring platform that will enable them to create Data Quality Checks themselves.
Otherwise, if you have a very small team and “they don’t know what they don’t know,” you may want a Data Quality Monitoring solution that automatically detects a set of common problems.
Remember, there are pros and cons for every tool:
- A highly automated plug-and-go solution may analyze your Data Quality with little to no upfront work, but it may not let you create customized, business-specific queries when you need to go deeper.
- A product that’s focused on enabling your data consumers to write their own Data Quality Checks will offer the ultimate flexibility to dive deep into business-specific queries, but may have less plug-and-go automation.
There is no right or wrong answer.
Everything depends on your team, your data, and your goals.
3. Would our team be more effective in a no-code, low-code, or heavy code environment?
Every team is different. Some want to write Python or SQL (hand-code). Others want to select options in drop-down menus (low-code). Some just want to click a button (no-code).
Identifying how your team works best is necessary before choosing a Data Quality Monitoring platform.
4. What type of seasonality does our data have?
If your data has seasonality, you’ll need a Data Quality Monitoring solution that can understand your type of seasonality and adjust accordingly.
Imagine you’re analyzing sales.
Your tool sets thresholds where sales are expected to fall.
- Will those thresholds work on holidays?
- If your business is gradually increasing or decreasing, will those thresholds adjust over time?
While an initial hard coded or automatically defined threshold may get you going, that same threshold will soon give incorrect results as soon as seasonality impacts your data.
5. Is Anomaly Detection required for data confidence?
Some Data Quality Monitoring platforms only provide anomaly detection for their basic built-in Data Quality Checks.
If your team needs to create unique, business-specific Data Quality Checks, make sure the Data Quality Monitoring tool supports anomaly detection of incidents on those customized checks.
Otherwise, creating anomaly detection for incidents yourself in SQL or Python would be very challenging and time-consuming.
6. Do we need a Data Quality Monitoring platform with Cloud scalability?
If your organization has small data volumes, legacy Data Quality tools that extract and copy data from your datasources to run full table scans may serve your purposes.
However, that traditional approach won’t scale if you have petabytes of data.
If you need to scale hundreds or thousands of Data Quality Checks across huge data volumes, you’ll need a Data Quality Monitoring platform with a modern pushdown architecture that sends efficient queries directly to your datasources for in-place processing — without extracting and copying data for full table scans.
7. What type of data sources need Data Quality Checks?
Before moving your data into a Data Warehouse or Data Lake, your data may be stored in a more economical Object Store like Amazon S3 or Azure Blob in a CSV or Parquet format.
If you want to perform Data Quality analysis early in your pipeline, before your data is loaded into your Data Warehouse or Data Lake, you’ll want a Data Quality Monitoring solution that specifically supports running checks on Object Store data.
8. Do we have a pipeline that performs a variety of transformations on our data?
ETL flows generally start out simple. Gradually, they become more and more complex. Ultimately, a large variety of transformations are performed on your data.
If this describes your ETL flow, you’ll want a Data Quality Monitoring platform that supports data reconciliation checks.
Why?
Data reconciliation checks verify the integrity of your data as it travels through your ETL pipeline. They allow you to compare data from your source to your target, ensuring that the final data is correct and matches the original data.
9. Do we need Data Profiling capabilities?
A data profile provides a static analysis of your data, typically over some time period. There are a variety of tools available that will generate a data profile.
Some Data Quality Monitoring tools also include data profiling capabilities, providing an easy way to understand your data.
10. Does the team work better when assets and owners are in relevant groups? Or does the team work better when everyone has access to every asset?
In this modern era of democratizing Data Quality, organizations may be concerned with giving too much power and information to data citizens. This underlying fear of distributing Data Quality Check authoring across departments can manifest in potential challenges, such as:
- User adoption issues
- Overwhelming alert fatigue
If Data Quality is administered and owned by a centralized team, separating assets is useful for navigation and context, but not required.
Ideally, organizations must first strategize how they want to drive their Data Quality and Data Governance initiatives. By aligning their goals and desired outcomes with an integrated solution that meets their requirements, organizations can balance Data Quality democratization and centralization to foster a data-driven ecosystem that thrives on cross-departmental collaboration and operational efficiency.
Make Data Your Most Trusted Asset with Modern Data Quality Monitoring
In the fast-paced world of data, where accuracy and reliability are non-negotiable, choosing the right Data Quality Monitoring solution could mean the difference between success and failure. That’s why we’ve taken the guesswork out, providing 10 key questions to ask, so you can find the best Data Quality Monitoring solution for your organization.
While there’s no one-size-fits-all solution when it comes to Data Quality Monitoring, your choice should align with your organization’s unique needs, goals, and data landscape.
It’s a decision that requires careful consideration, but it’s also a decision that can significantly improve your Data Quality, leading to:
- Better decision-making
- Improved operational efficiency
- Certified data assets
- A competitive edge in your industry
As you progress through your Data Quality journey with these questions and insights, we hope you’ll make a well-informed decision that paves the way for a future where your data is your most trusted asset.
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