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Neelima Misra07 okt 20192 min read

Cloud data quality: Big challenges with cool solutions

If you watched Iron Man series you will realize that the challenge faced by Iron Man gets more and more complex with each new movie, to counter that every time Iron Man gets equipped with new set additional tools, gadgets. The cloud data world is no different from Iron Man’s world.

Organizations moving to cloud platforms are facing new challenges but at the same time equipped with extremely capable tools to solve it. To get the right value from your data platform you need to carefully consider tools that will work effectively for you and your organization.

We get to hear the following questions around data quality very frequently.

  • -What about GDPR and PII data? What is our strategy to deal with the same?
  • -Now that we are dealing with 100 times larger volume of data, does that also mean our data quality challenges increased by same proportion?
  • -Our cloud services are so dynamic, and are constantly updated. How can we govern our data in such a dynamic environment?


Answers to these questions are not straight forward nor one solution can solve all challenges. To find an answer and to provide right resolution for your organization, you need to embark in a journey of continuously analyzing your business situation against your data strategy. In this article I attempting to list few cool tools that may make data quality management much more interesting and fun to work with. Consider these for your current and future cloud data strategy.   

  • Intelligent data profiling

    • Cloud brings intelligence to your doorstep. If you are overloaded with hundreds or thousands of unknown data sets and afraid of the sheer amount of manual configuration it will take to start working with your data? Employ smart, automated metadata discovery algorithms instead.
    • Also, turn your attention to unstructured data. Use platforms to prepare, profile, and explore your text-based data content, from CRM notes to social media, emails, and call transcriptions.

  • Automated data quality checks

    • Cognitive automation solution which combines machine learning capabilities and traditional RPA capabilities can be a potent solution which will enable organizations for a faster remediation of data quality issues and reduce the need for manual intervention to a great extent.
    • The machine learning can enable to make smarter data quality checks possible even if new data elements are introduced.

  • Smart data quality solution on your integration layer

    • Smart data quality solutions on your integration layer can enable data creation (e.g. auto-filling values in forms, automatic extraction of data) and data enrichment, maintenance (g. reactive: data correction; proactive: business rules) and data unification (matching and de-duplication) and data retirement processes with the help machine learning.

  • Smart components for on PII and data masking

    • Modern cloud data platforms come with in-build functionality to identify and mask PII data whenever necessary. Knowing how to leverage these cool functionalities inbuilt into your cloud data platform will make your data architecture more robust and sustainable.


In summary, the data world has exploded, and we will see many more data quality challenges in future. At the same time machine learning, automation and AI has enabled multiple platforms with smart tool kits which will help to deal with these issues in a smarter way. Planning to leverage these solutions very early in your data journey will set the right tone for you data driven strategy .


Neelima Misra

AI, Analytics and RPA Lead