Acceldata Acquires AI Leader Bewgle to Deepen Data Observability Capabilities for AI | Learn More

What is Data Quality Control?

What it means, why it matters, and how it works. This article provides definitions and the business use cases for data quality control.

In this article:
What is a cloud data platform?

What is Data Quality Control?

Data quality control is essential to maintaining data and ensuring accuracy and alignment across your organization. There are many factors to account for when assessing data quality, such as correct formatting and invalid values. That is why it can be helpful to create a data quality checklist to make sure you don’t skip over anything important. You might also look at various data quality checks examples to get a better idea of what you should be looking for in terms of quality control. While each data quality framework is unique, there are certain things you should be sure to look for, regardless of your process, and making a checklist is a great way to stay on top of things.

Data quality standards differ from industry to industry. Generally speaking, data is considered to be of high quality if it’s accurate, usable, and fit for its intended purpose in decision-making and operations. It’s also representative of real-world constructs and is consistent across the board. By adhering to these data quality standards best practices, you can use your data effectively and make the best possible business decisions based on the data that’s available to you. For more information on the types of data quality checks or how to ensure data quality, you might consider viewing a guide or tutorial on how quality control is performed.

Acceldata offers multidimensional data observability software, including solutions for quality and reliability. Now what is data observability? Data Observability is the process by which enterprise can monitor the health and usefulness of their data.With Acceldata, you can improve the quality of your data and gain valuable insights into your wider data system. Data observability doesn’t just lead to higher quality data, but 20% faster application development and 90% fewer incidents related to data. By monitoring your data holistically, you can improve data quality, make better use of that data, and accurately predict operational issues so that you can resolve them before they negatively impact your business.

Examples of Data Quality Control

Viewing data quality control examples can give you a better idea of how data quality is assured. For instance, a system may check for missing information, errors made during transfer, or duplicate items. Staying updated on data quality best practices is the best way to ensure that your data remains usable—you don’t want to waste time and money handling worthless data. This is why it’s important to have quality control measures in place to mitigate risk and promote more efficient processes throughout your organization.

Your data quality management process will depend on the system and software you use. If you use SQL, then you can implement data quality checks in SQL. Whatever tools or systems you use, it’s important to regularly monitor data so that you can spot issues before they spiral out of control. Acceldata’s data observability platform allows businesses to gain full visibility into their data systems, correlate events, and prevent unreliable data that can lead to disastrous business outcomes. Acceldata provides everything that you need to manage data quality control and ensure the proper management of your data so that you can modernize your system for greater efficiency.

Data Quality Control in Research

Data quality control in research is critical to maintaining the integrity of data captured through research. In most cases, quality control in data collection or research is carried out in multiple stages—this is to ensure that the proper protocols are being followed throughout the data management process. Data is checked for completeness, duplication, statistical anomalies, and other criteria that could indicate trouble. Viewing examples of quality control in research is a great way to learn more about what goes into this process and what is required to ensure data validity.

There are different types of quality control in research, and understanding these is critical to maintaining quality data. You can access a quality control research paper to learn more about how this process works in research and why it’s vital to data integrity. You might also look at the quality control in research proposals for a better understanding of quality control projects and their significance to research as a whole. Businesses rely on quality research to make important decisions, so ensuring that data is accurate and usable is crucial to both your revenue and your reputation.

Data Quality Reporting

A data quality report details findings related to data quality. These reports are useful for determining whether or not certain quality control measures have been met. Your data quality assessment might contain a checklist ticking off each required item and noting any data quality issues you encountered. If you need help writing a report, you can access a data quality report sample. You can also look for resources specific to the programs you use. For instance, if you use Python, then you should search for data quality report Python tools for help writing Python data reports.

But why is data quality important? Good data quality allows for better business decision-making. It can also help ensure accuracy across various systems. If something goes wrong with a single data point, it can compromise the integrity of your whole data system. As such, it’s important to ensure validity and reliability across the board. Data quality reports provide helpful information on the current state of data and how to improve data quality going forward. So much is dependent on having access to good, reliable data, which is why smart companies continually check for data quality and make  changes to their systems when necessary to improve things.

Examples of Data Quality

Some of the most monitored data quality examples include consistency and completeness. The more consistent and complete the data, the more reliable it is. It can also be helpful to study bad data quality examples and the data quality characteristics that define them for a better understanding of what low-quality data looks like and how you can avoid it. For instance, you can work to uncover hidden data by breaking down silos in your system and connecting data to get a big-picture look at how it’s all related.

Completeness, accuracy, and validity are among the most critical data quality metrics, but there are others you should watch for as well, depending on the type of data you’re working with and what you’re hoping to get out of it. Understanding data quality dimensions can enable you to manage the process more effectively. You can also check out a data quality report example to learn more about reports, or data quality standards examples to see what is expected in terms of data quality. It’s important to see how data quality processes are performed in order to implement similar practices in your own business.

Data Quality Control in Qualitative Research

Performing data quality control in qualitative research is critical to business success. There are a variety of data quality control methods you can leverage to achieve best results, so you should do your homework and determine your unique business needs to find the solution that’s right for your organization. You can browse examples of quality control in research to learn more about what quality control entails. This can be especially helpful if you’re just getting started with data management and have no prior experience enacting control measures.

Quality control in research methodology is performed to ensure the quality of data being collected using a chosen methodology. Data quality control in qualitative research is critical to achieving accurate results. If you aren’t working with reliable data, your whole process will suffer, and so researchers pay particular attention to the data they’re using and whether or not it’s actually reliable. There are many ways to perform quality control, and not all methods are right for all businesses, so you should consider your unique needs, as well as your ultimate purpose for the data. The more critical your data, the more important it is to have the proper control measures in place.

Data Quality Tools

There are a variety of data quality management tools for businesses and departments of all types. For example, if you work in marketing, you can find a plethora of data quality tools for marketing. Likewise, if you work with Azure, you can find a host of data quality tools in Azure. Data quality tools Python software is also available, as is data quality tools SQL server software. If you’re looking for something that allows for greater flexibility and the ability to modify the source code, you can search for data quality tools open source platforms. To learn more about the different data tools that are out there, you can browse a data quality tools list.

Acceldata enables users to eliminate unplanned outages, scale their workloads, and reduce business costs by offloading unnecessary software. Acceldata offers various solutions, such as Pulse, so that you can ensure data quality without having to pull multiple tools from disparate systems. In fact, Pulse integrates with a number of data systems, allowing users to streamline their operations. While maintaining data quality is no easy feat, platforms like Acceldata make it easier to manage your data and learn how it can be improved. Acceldata’s multidimensional data observability platform offers comprehensive visibility into your data so that you can do more with it.

Ready to start your data observability journey?

Request a demo and chat with one of our experts.