The top three data quality developments in data management today are modern data hubs, data warehouse modernization and machine learning algorithms. Using machine learning algorithm companies are changing the way they look at their customers’ behaviors. For instance, the manufacturing industry can optimize the scrubbing process through their database with an output of useful information regarding purchasing patterns and production process.


Warehouses and modern data hubs provide organizations with rapid data access, facilitated security and ethical adherence, and establish a solid base to build on as data collection grows to enable more statistical or prescriptive research. This is one of the powerful advantages of implementing these trends: organizations will step back from looking at data rows and now look into trends and output to decide how to drive the company strategically and responsibly.


In some situations, organizations will see data stories that they were entirely unaware of, and that will transform future income and consumer interactions substantially.


Speaking of the future, the worldwide demand for data quality software is expected to rise by and the evolving conditions that sustain this development make it important for companies in this field to keep up with the market’s changing pulse. 


If you need help, iData is here! You can also check out the Getting Started with Data Quality  and Advancing Your Career in Data courses from the iData Quality Academy.

Blog Author: Kate Strachnyi

Your Data Quality Primer: Everything you need to know

Welcome to your Data Quality Primer from the iData Quality Academy – in this useful guide you’ll find everything you need to know to improve your understanding of data quality, assorted into useful categories for you. You don’t have to read through it in order, you can jump right to the section you’re interested in!

Your Data Quality Primer: Everything you need to know

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