Significance and Consequence of data quality
Most organizations are recognizing the importance of investing in a robust and scalable data management architecture to get good ROI on data spends. Whether it is via DataOps or Data Mesh, you need to identify your unique selling point by placing trust in the most important asset - DATA.
Whether you collate data, or buy it, you know your output is only as good as the confidence you have in the input - Garbage In, Garbage Out. The quality of data is quite significant in a data-driven organization, and to identify data anomalies at the foundation of the pipeline step, will ensure all downstream consumption of data will accurately provide the results that can significantly boost your business.
How do we define data quality? Simply put, is the data accurate, in the right format, reliable and consistent. Just by analyzing the condition of data on some parameters can isolate the problem areas, thus forcing you to evaluate at the origin rather than at the end of the data lifecycle. There are many ways of identifying data anomalies, thereby improving data quality, such as data monitoring and observability, and we’ve described that in great detail in our post.
Here, I would like to stress on 3 of the many reasons why quality plays a big role in a data-driven organization.
Sure, good data does have a great impact, but it's often times taken for granted, because the opposite is more obvious - untrustworthy data can have tremendous consequences -
Telm.ai can help identify anomalies and inaccuracies in your data, saving you time, effort and money. It seamlessly injects into your pipeline step, becoming an integral part of your data architecture.
#dataquality #dataanomalydetection #dataobservability #datamonitoring
About the Author
Harsha Bipin, Technical Marketing @ Telm.ai, Software Engineer and a big proponent of mindful living :)
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