Data quality framework overview
So many organizations are struggling with maintaining the quality of their data. They may have multiple data sources producing duplicate sets of data, creating consistency issues, or there may be anomalies that are impacting the data’s accuracy.
Eliminating these issues and achieving a high level of data quality is vital for every aspect of your business that relies on data to inform their activities and decision-making.
One of the essential elements to achieving this is to implement a data quality framework. A truly effective framework like this can reduce the risks that low-quality data poses to your organization and improve the quality of data-based decision-making.
Data quality frameworks are often built for data lakes. Data lakes are centralized places where massive amounts of data are stored and later analyzed. It’s crucial that you have a plan for how to keep your data secure and consistent throughout your pipeline and into your destination, wherever it may be.
One of the best ways to maintain this quality is to use an observability solution like Acceldata. With Torch from Acceldata, you can automate data quality and reliability at scale throughout your entire data pipeline.
Using advanced machine learning and AI, Acceldata automatically recommends the rules and actions you need to take in order to improve your data quality. This ensures that your data remains consistent, accurate, valid, and complete from the source all the way to the destination.
One of the ways you can get started with learning about these strategies is to read a data quality framework implementation guide. These guides can help you understand how a framework can help you achieve your quality goals and why it is so important.
At the end of the day, a data quality framework is only as good as the template and tools that it is founded on. That’s why you need to make sure that you only work with the best.
What is a data quality framework?
What is a data quality framework? A data quality framework is a tool that you can use to measure data quality within your organization.
With a data quality framework, your business can define its data quality goals and standards as well as the activities you are going to take to meet those goals. A data quality framework template is basically a roadmap that you can use to build your own data quality strategy.
By going online and searching for “data quality framework open source” or “data quality framework table example,” you can find great references that you can use to build your own plan.
One of the other features you’ll see in any data quality framework is the best practices that your organization is committed to adhering to in order to maintain and monitor the quality of your data.
These best practices should include data security practices. Keeping your data secure and free from tampering or unauthorized access is a crucial aspect of maintaining high data quality.
Data quality is too important to just leave up to chance. If your data quality is negatively impacted, it could have massive consequences on not only the systems that rely on that data but also on the decisions you make in your business.
That’s why it makes sense to develop a practical data quality framework for your organization’s pipeline.
Github data quality framework
There are several great sources to find examples of data quality frameworks. For example, the data quality framework GitHub repository holds a large collection of papers and other written pieces on data quality and data science.
GitHub is also the place to find the Agile Lab data quality framework. This framework actually comes in the form of a program that has several pre-defined data quality rules that you can apply to a given data set.
You can also find other data quality GitHub programs and tools on the site. For example, the data-profiling GitHub tool, Data Profiler, is a python library that is specifically designed to make analyzing data easier.
If you would prefer to use Java, you can also find other data quality framework Java applications and libraries. You could also design a Spark data quality framework as well. Regardless of your preferred engine, library, or coding language, building a data quality framework is recommended.
Some frameworks come in the form of guides that list out a collection of steps to reach a certain goal. Some frameworks implement these goals in the form of a program.
Whichever you choose, you need to be setting and meeting data quality goals if you want to truly get all you can out of your data.
Data quality tools
There are many data quality tools out there. One of the best data quality tools examples is Acceldata. The fact is, your data pipeline is a massively complex series of various systems and tools that carry your data from one place to another.
At any point along this journey, your data’s quality could be compromised. In order to truly ensure that your data quality remains high, you need an observability solution that monitors your data at each of the key points in your data pipeline. That is exactly what Acceldata is designed to do.
One of the great benefits of this kind of monitoring is that you can eliminate issues that impact reliability. This means that you can remove downtime from the equation so that you always have access to your data.
If you want to see a list of the reviews of different tools, the data quality tools Gartner site is a great place to go. You should always compare ratings and read about customer experiences before choosing your preferred data quality solution.
Sometimes, you may hear about or come across open source data quality tools.
These data quality tools have pros and cons that you should be aware of. Open-source tools can sometimes have difficult-to-use interfaces and functionality. This can cost you in terms of additional time and money spent on training.
Data quality framework examples
Examples are one of the best ways to learn something. In the case of data quality frameworks, why not find a data quality framework PDF to download? Sometimes, doing research on what your competitors are doing to maintain data quality can help give you inspiration as you work on your own data quality strategy.
If you were to open the average data quality assessment handbook, you’d probably find an overall definition of data quality according to the specific organization’s needs and requirements. This is one of the primary purposes of a data quality framework - to define what good data looks like. A data quality assessment tools PDF often comes in the form of an implementation guide or manual that walks the user through the various implementation steps of the data quality tool.
There are a certain number of metrics or dimensions to data quality. You can learn more about these by looking at a data quality dimensions PDF. Each of these primary dimensions needs to be monitored and maintained in order to keep your data secure and accurate. Finally, the other place to look for a data quality framework example is in a data quality fundamentals book. Some well-written books have been published focusing on data quality and related issues.
Data quality framework tools
There are many powerful data quality framework tools available that can help you protect your data quality. The best data quality tools are scalable and easy to use. They should also be designed to be cloud-based.
Cloud-based tools allow you to scale up or down your resource usage so that you only use what you need.
Looking at the data quality tools Gartner list, the things most users look for in a data quality tool are functionality, ease of use, and integration with other data management tools and systems. When it comes to ease of use for a data quality tool, how quickly and efficiently you can implement new rules is a big deal. Users also care a great deal about security.
When it comes to data quality tools in data warehouse, you don’t want a vulnerability potentially impacting your entire amount of stored data. Acceldata makes it easy to create new rules and even automates the process by automatically recommending the best rules for the dataset using its own machine learning engine.
We have already discussed the pros and cons of data quality tool open source options. Gartner provides some of the best research when it comes to data quality frameworks. If you are a client of Gartner’s, you can visit their website and download the data quality tools Gartner magic quadrant.
One of the big takeaways from the study was that artificial intelligence is one of the key driving elements of the modern data quality market.
Data quality dimensions
One of the core data quality best practices is the data quality dimensions. There are six critical data quality dimensions:
- Accuracy – does the data correspond to reality?
- Completeness – is something missing from the data?
- Consistency – is the data consistent with the rest of the data, or is it an outlier?
- Freshness – how old is the data?
- Validity – is the data being stored in a valid and useful format?
- Uniqueness – is the datapoint under consideration completely unique within the database, or are there duplicates?
Maintaining each of these dimensions is the job of data quality analysts using data quality tools. With the right tools (like Acceldata), this job can be much easier.
When building your data quality deck, it’s important to explain how to keep high ratings for all of these metrics. The importance of data quality is clear. By keeping high levels of data quality, you can ensure that you are always making accurate, insight-driven decisions.