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In today's world, stories about organizations achieving remarkable success with AI solutions dominate our news feeds, social media platforms, and marketing campaigns. These seemingly magical AI systems promise to revolutionize our lives by solving complex problems. However, the reality is far more nuanced. Amid the success stories, numerous companies find themselves trapped in nightmarish situations, struggling to make AI work for them. What separates the winners from the losers? The answer lies in one key factor: data quality.

As data quality can make or break the effectiveness of data-driven decision-making processes, data quality remains a top priority for any organization. So, let’s kick off the new year with an overview of the key data quality trends that will shape how you manage and leverage the quality of your data.

Great Expectations, an open-source Python library, provides an excellent framework to kickstart your Data Quality by Design projects, creating visibility for data quality issues, and triggering calls to action for everyone involved.

We polled our Collibra consultant Evert on Collibra DQ, the newest extension to the Collibra data intelligence platform. How does Evert see the future of Collibra DQ and Data Quality in general? And what are the most significant opportunities for Collibra DQ, in his opinion?