Melissa AU Team | Australia, Data Quality, Singapore | forecast 2028, global data quality tools, industry trends, market

Data quality has always been a hurdle for IT departments and as smart devices become more accessible to users, these departments are facing a data explosion. In 2020, the Data Quality Tools Market size was estimated to be worth $932.8 million. By 2028, this evaluation is expected to reach $3673.2 million.

With data types and formats becoming more and more complex, the Data Quality Software Market has been tasked with evolving and expanding to help data managers understand and evaluate the knowledge that can be gleaned from their data pools.

Global Data Quality Software Market Definition

Data quality software is aimed at analyzing data sets and transforming them into meaningful information. It can identify issues related to data quality such as incorrect information, invalid data, duplicate records, etc. and resolve them.

Data quality software also works on standardizing formats and enriching data by appending the available data with additional information from reliable third-party databases. For this, they are equipped with a range of functionalities including data cleansing, data matching, data profiling, data standardization and data monitoring.

They address information resources management across myriad applications including BI, CRM and ERP for sectors such as telecommunications, retail and eCommerce, BFSI, healthcare, manufacturing and education.

Changes in the Data Quality Software Market

Data Quality Software is evolving in eight key areas, audience, data diversity, governance, latency, analytics, intelligence, deployment and pricing. For example, data quality is no longer being considered solely an IT responsibility. Today, the audience has expanded to include business people as agents of information governance. Data has become more diverse as have the data sources.  Let’s take a closer look at some of the key changes.

• Whole-System Fixes

Organizations are seeing how data flow is connected between departments and hence need data quality software that addresses the entire system rather than individual department databases. Thus, the quantity of data the software can handle at a time has increased exponentially. Data quality software has also automated several fixes such as adding country codes to contact numbers to maintain a standard format and reduce the risk of duplicate records.

• Easier Handling

Since data is being created, used and managed by many people who may not have specialized technical skills, data quality software is changing its interfaces to make it more democratic. Software is being designed such that an average non-technical user can access information without always having to involve IT. User-friendly drag and drop interfaces are becoming more common. Simple features such as autocomplete for address fields have helped reduce incorrect data entries considerably.

• Automation

Data quality software no longer depends on manual scheduling. Once-a-year overhauls have been replaced with automated data cleaning on a more regular basis. This has led to better quality data and compounded time savings.

Automated data fitness score assignments are also becoming more commonplace. Data quality has become more objective and rather than simply categorizing it as good and bad, data quality software can measure quality criteria such as is all the data present, how accurate is it, is it relevant to the task, etc. The software can then use these criteria to automate data health and data fitness scoring.

• Increased Use Of Metadata

Data quality software was earlier focused on how data is mined. Today, the attention has shifted to how it is being managed and used. With this in mind, data quality software has increased the usage of metadata to identify, categorize and gain insights. Metadata provides answers to the what, who, why, when, where and how of data required to categorize data. This has helped improve results and give organizations more confidence to make data-driven decisions.

• Availability Of Comprehensive Solutions

While the data quality software market is still moderately fragmented, mergers and acquisitions are on the rise. The aim is to offer customers a comprehensive set of solutions for increased market traction by merging data quality and data integration tools. For example, data quality software is looking at being able to handle cloud and on-premise environments from the same account. In addition to data cleaning, data profiling; standardization, de-duplication, enrichment, etc are all now being offered together to keep outdated, incomplete and incorrect data from entering the system.

In Conclusion

The Data Quality Software industry is growing at an explosive rate driven mainly by changing value propositions and innovations in technology as well as the need to comply with global data privacy regulations. It has become a market with one of the fastest innovation cycles.

Companies that invest in new products and services through their own R&D or acquisitions are seeing sustainable growth. Any organization that collects data needs a data quality software solution to increase the reliability of data for strategic decision-making. There are infinite opportunities for growth and the data quality software market has only just begun the climb.