A data quality tool can be defined as a device that helps in the process of ensuring high-quality of data and delivery of services. If selected and used properly, these tools can greatly assist in the assessment of customer requirements by transforming a simple set of data into useful information.
Before moving on to different types of data quality tools available online, you should first understand what data quality is and how to measure it?
Today, many businesses recognize the importance of high-quality data for their success, but there are still enterprises, which are unaware of data quality. These companies end up focusing only on gathering data without paying attention to its quality management. There are businesses, which have heard the term but never tried to understand it properly and its impact on their business.
Data quality is an oft-used term in the data world. Experienced data analysts often talk about data quality to derive value from data. In simple words, data quality is the ability of a set of data to serve the intended objective of helping in a marketing campaign, understanding customer behavior etc. Only high-quality data can help your organization achieve its goals.
Let’s understand it through an example. A company has a list of 1000 customers along with the phone numbers and addresses. Now, this huge data has high chances of containing errors like spelling mistakes in names, wrong address due to change in it or multiple entries of the same customer with minor changes in name. Now correcting these errors is not possible until you use data quality tools.
How to measure data quality?
Measuring and ensuring the quality of data in organizations with so much of data across different areas is a bit difficult, but not impossible. By making a checklist, you can ensure that you have got the highest quality of data possible for the success of your business. Below given are the data quality dimensions you can include in your checklist to measure the quality of your data.
Completeness: Make sure that the critical data like customer name, contact details etc are completed first as this has a high percentage of the completeness of the data.
Validity: Check the standards and make sure the data conforms to the set standards in your organization.
Uniqueness: Make sure there is only one entry of one person or entity in the database when measured against other data sets.
Accuracy: Though 100% accuracy in a large database is not possible all the time, you should check how well it reflects the real-world person identified by it.
Timeliness: In case of sales, new product launch and another campaign to create awareness among customers, date and time have a huge impact on the data. So make sure the information can be relied upon for accuracy over a period of time.
Role of data quality tools in the maintenance of data:
Demand for easy-to-use data quality tools has increased over time. The advanced tools not just make data error-free but also enable data insight, support in contact data validation, provide a complete customer view and help in data preparation. The tools support users in maintaining the quality of database throughout their life cycle. Moreover, they also offer standard reports to help management make better decisions. Tools have following features, which make data management hassle-free for users.
The main objective of the validation process is to regularly authenticate and verify records throughout their life cycle against given reference standards. A tool to maintain data quality should automate the validation process in its entirety.
In order to support data administers, a quality tool must envisage the results of the validation process. It should show the information that is required to be changed in the dataset to get the preferred data quality.
The only responsibility for the data quality assessment is not to support the data administrators or managers with their jobs but also to offer the management an overview of the status of data maintenance. The management should be aware of the present state of data quality and the scope of improvements in it for better results.
If you want that the tools help you speed up your business process by preventing you from the tedious task of regularly monitoring and analyzing data, you should check for the above-mentioned features in the data quality tools. Carefully selected tools for data quality management can help your businesses processes throughout the lifecycle of your product.
Tips to choose the best data quality tools
To ensure that you get maximum benefits from your investment in the tools for management of data quality, you should choose them wisely. Here are some tips to help you find the most suitable tools for your business and database.
Data cleansing feature: It helps in improving the entire data management process. That’s why you should look for a data quality management tool with the feature of data cleansing.
Data Monitoring and auditing: To ensure that you get the best results in business process, look for quality management tool with data monitoring and auditing feature. If used properly, this facility can take data quality to the next level.
Vendor stability: Buy data quality management tool only from a reliable and experienced vendor. They should have a strong presence in the market along with effective customer support system.
Cost: No doubt money matters a lot; even when you have to buy data quality software. Make sure the money charged for the tools is justified through their features.
There is no “one size fits all” approach to data quality management. Different business might need varied data quality tools for database management. With big data’s appetite for information growing with each passing day, it has got essential for the businesses to maintain the quality of the data through advanced tools. Data hygiene tools allow computers to crunch numbers and provide you the best information for superior customer services, higher conversion rate, customer loyalty and better customer experience.
Author: Sohel Ather