Overview of data quality software
Your data’s accuracy, consistency, completeness, and reliability are all crucial factors to consider when making insights-driven decisions. Collectively, these attributes make up what is often referred to as data quality. If your data’s quality is not maintained, the quality of your decision-making will suffer. In order to truly succeed as a data-powered organization, you need to take specific actions to ensure that these primary attributes are kept constant. Poor-quality data has been behind some of the biggest mistakes and financial disasters for large enterprises. In fact, IBM calculated that the annual cost of data quality issues in the U.S. amounted to $3.1 trillion in 2016. This is an astounding number that your business can’t afford to ignore.
Maintaining data quality is not easy. Most organizations have hundreds of different sources producing thousands of data points. This data is then often carried through an organization by a complicated data pipeline, at any point at which errors could be introduced. Finally, this data is then stored for analysis by end-users. It’s not only the complicated nature of the modern data pipeline that makes data quality difficult, but it’s also the amount of data that needs to be managed. According to the International Data Corporation (IDC), the data sphere is now over 64 zettabytes in scale. To put that in perspective, that is about 1 billion terabytes of data.
Managing all that data manually is not an option, which is why many organizations are looking for effective data quality software to streamline the process. SQL server data quality services and data quality tools in data warehouse applications can be incredibly useful for their ability to automate tedious tasks and enhance your scalability.
Open source data quality tools
If your team is on a tight budget, you might consider looking for a data quality tool open source option. These tools are often very affordable or even free and can provide a great deal of functionality. One great example of a tool like this is Apache Griffin. Apache Griffin is a data quality application designed to address data quality issues at the big data level. However, if you watch an Apache Griffin tutorial, you’ll see that it is primarily a data quality tool rather than an observability solution. This limits its functionality in a couple of key ways. A data observability solution like Acceldata has the power to automate data quality and goes much further, helping you address all the attributes that comprise good data quality.
In order to maintain good data quality in data warehouse settings, you need to understand what it is. First of all, high-quality data is accurate. Data accuracy refers to whether or not the given data is correct and corresponds to the reality of the situation. Inaccurate data needs to be corrected or removed from the data set so that it does not end up skewing your results. Secondly, good quality data is consistent. Data from different sources measuring the same attributes or events should report identical results. By checking the data from different sources, you can verify whether or not it is consistent. If it is not, the data’s quality comes into question and the problem will need to be addressed before you can move forward. The third parameter of data quality is completeness.
It’s important that your data is comprehensive and tells the whole story. You don’t want business leaders having to make decisions based on an incomplete picture of the situation. The fourth and final attribute of data quality that we’ll discuss here is data reliability. Reliability refers to the validity, completeness, and uniqueness of the data that ensures that it can be trusted. There is also an integrity component to reliability that ensures that the data has not been tampered with. There are several other aspects that make up good data quality, but these main attributes should serve as a general definition.
Best data quality software
Understanding what the best data quality software is, comes down to a variety of factors. First of all, we recommend looking for reviews from companies in a similar situation to yours. This can give you a better idea of whether or not a potential solution would be able to suit your needs. In recent years, many young companies have entered the market promising the “best data quality tools for big data”, but don’t get fooled by the marketing. It’s always important to do your own research. One great place to look is the data quality tools Gartner Magic Quadrant. This report from Gartner details the leading data quality tools on the market today and gives a variety of data points on these companies that can help you decide which option is best for you. Furthermore, if you are considering an open source data quality tools comparison, looking for similar reviews and reports is advised. This kind of data can be crucial to understanding more about what these tools can actually do for you. Finally, we also recommend browsing the websites of your top selections. You’ll often find blog posts and articles that explain who the company is and what kinds of services they provide. This can be another great way to learn more about them.
Best data quality tools
The best data quality tools introduce the power of automation into your big data analysis. Data quality practices such as profiling can be tedious and take too much time away from your team. However, with a powerful data observability tool like Acceldata, you can take control of your data and gain 24/7 visibility into every aspect of your data pipeline. ASW data quality tools such as Deequ (an open source tool developed and used by Amazon) have the reliability of a major enterprise behind them. Data quality tools for big data need to be reliable, and they need to be secure. As soon as your data passes into a tool, that tool needs to secure it, or it will be vulnerable to an attack. According to the data quality tools, Gartner Magic Quadrant for 2021, Talend, IBM, and Informatica were named as some of the leaders in the space. The data quality tools Gartner report is a great place to start when comparing the best data quality tools. Microsoft data quality tools are another option, but it’s important to remember that comprehensive data quality management is about more than just monitoring for errors. It’s about true data observability.
Data quality tools in Azure
There are several different data quality tools in Azure, a widely-used variety of database software. Furthermore, there are also several external platforms and tools that are compatible with the Azure solution. Azure Data Factory is a data integration service that provides several data quality and observability functions. The Azure Purview data quality tool is another example of a tool that provides these kinds of functions and can help you manage your data quality across your organization.
Unfortunately, Azure Purview has extremely limited functionalities and does not support data profiling, automation, or quality assessments. The Azure Synapse data quality tool is yet another data governance and reliability tool designed to complement an Azure database. The tool receives a great rating on Gartner and also possesses security features to help keep data safe. Doing tasks like data profiling in Azure Data Factory or data quality checks in Azure Data Factory are not simple tasks. Furthermore, there is no automation support provided to eliminate the tedium these tasks create. Overall, although setting up data quality in Azure Data Factory is possible, there are better and easier ways to achieve this goal than through the manual tools Microsoft provides.
List of data quality tools
When looking at any data quality tools list, we recommend keeping the main parameters of data quality in mind: accuracy, consistency, completeness, and reliability. We could also add a fifth parameter to this list: freshness. Outdated data is poor quality data and is going to leave your business struggling to keep up with your competitors. Any list of data quality and data profiling tools should also show customer reviews to help you decide what tool is best for you.
Finally, we’d encourage you to consider that data quality tools alone are not enough to truly manage your data quality. For example, Acceldata is a comprehensive data observability platform that goes beyond just monitoring for errors and helps you make sense of your big data. Pulse from Acceldata can eliminate unplanned outages, easily scale up as you do, and can save you millions of dollars by streamlining your data pipeline.