Ensuring data quality is essential for accurate analysis and obtaining helpful insights. There are various tools for data quality that you can leverage for the purpose. You can automate most of these tools, which is more convenient than conducting manual data quality checks.

What are data quality tools?

Data quality tools are stand-alone or integrated tools that you can use to check and measure the quality of the data you gather from multiple sources. Aside from enhancing data quality, these tools can handle various data management functions. You can use the built-in features to automate processes rather than configure everything manually. You can get multiple tools to perform different functions with the raw data in your system. However, you need a central quality assessment strategy to avoid compatibility issues and errors.

You can buy existing tools or build your own data quality system. Custom-building your system can enable you to get tools that are a good fit for your business purposes, but it can be time-consuming and expensive. It will be more cost-effective to buy existing tools that you scale up as and when required.

How to choose the right data quality tools?

You can consider the following tool features to choose the right data quality tools:

Find out if you can buy the tools with a one-time fee or must sign up for a subscription system and if you must pay extra for any add-ons you need.

Check if you can scale up the tools to get new features that you might need as your data sources and data requirements grow.

Make sure that the data tools are compatible with and can integrate with all the data sources you use for your regular business activities.

Determine if you need on-premise or cloud-based tool options. The second choice might be more convenient if you are a small business with limited hardware resources.

Check if the tools for data quality have batch processing capabilities to improve the execution of processes. They can perform bulk operations to standardize datasets and remove noise and duplicates. They can enhance work productivity by saving time, effort, and resources.

Check if the tools for data quality come with inbuilt templates for improved pattern recognition.

Check if the tools can facilitate the following functionalities:

• Data ingestion: To gather data from various sources and support different data formats.

• Data profiling: To get a clear overview of the data quality by identifying patterns, data types, data values, and statistics

• Data parsing: To analyze long strings and check their essential components against accurate values. The tools must also be able to configure data matches, remove duplicate data, merge data records, and export data to different destination sources.

• Data cleansing: To remove irrelevant and unwanted data from the dataset.

• Data standardization: To identify patterns and transforming patterns to get a standardized view of all data. The data quality tools must also be able to configure data matches, remove duplicate data, merge data records, and export data to different destination sources.