An Introduction to Data Quality Tools

1 min read

Data quality software solutions are leveraged by enterprises to deliver comprehensive and precise business data. These solutions offer broad functionality, including data cleansing, parsing, profiling, enrichment, and monitoring. Organizations use data quality tools to comprehend, monitor, and standardize data throughout its lifecycle. Below are some of the key features of data quality software.

Ensures Data Accuracy

Datasets compiled from disparate sources can easily be exposed to inaccuracies—OCR tools may read information incorrectly, employees might press a wrong key while manually entering data, and application errors may cause a file to be captured in an incorrect format. Examples of incorrect entries within a dataset include customer names being misspelled or the date and time of an incident being recorded inaccurately. Solutions like Informatica Data Quality ensure data accuracy is maintained.

Addresses Missing Data

Missing information prevents companies from utilizing databases to their full potential—for instance, customers can’t be sent customized emails if their email addresses are present in the database, but their names are not. IBM InfoSphere and other data quality solutions help address missing data.

Corrects Outdated Data

In today’s digital landscape, data that is up-to-date one day may be outdated the next. Customers change mailing addresses, email addresses, and contact numbers every day. They might even change their legal names. Apart from people, other data sources frequently become outdated as well—for instance, the IP address of a server might change, or the hardware of a network switch being updated might increase the traffic that it can cater to. Outdated information can cause serious challenges to data quality if not corrected promptly—something that solutions like SAP Information Steward can take care of with ease.

Removes Redundant Data

While copying data and creating backups is easy, it is more difficult to deal with the negative effects of having multiple copies of the same data. For instance, a database containing two identical entries of the same customer might lead to them being billed twice, leading to terrible customer experience. With solutions like SAS Data Quality, redundant data is removed rapidly and without compromising the integrity of the dataset.

Fixes Inconsistent Data Formats

Different operating systems, applications, database platforms, and even different versions of the same software may store data in different formats. When businesses deal with complicated datasets, inconsistencies in formatting can hamper their ability to interpret data at the required speed. Analytics platforms expect data to be present in specific formats, and if the data sources of business have stored it in unsupported formats—and the tools required to convert the data are not present—operations may come to a standstill. Syncsort and other data quality solutions help users avoid the problems of inconsistent data formats.

Takeaway

Companies across verticals leverage vast amounts of data for everything from improving efficiency to boosting customer experience. With the right data quality tools, these companies can ensure that their operations are not interrupted due to deficiencies in data. Data quality solutions also ensure that high-quality data is always present, helping boost profitability.

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