When explaining what Data Observability Driven Development (DODD) is and why it should be a best practice in any data ecosystem, using food traceability as an analogy can be helpful. The purpose of food traceability is to be able to know exactly where food products or ingredients came from and what their state is at each moment in the supply chain. It is a standard practice in many countries, and it applies to almost every type of food product.

“Without clean data, or clean enough data, your data science is worthless.” Michael Stonebraker, adjunct professor, MIT AI is one of the fastest-growing and most popular data-driven technologies in use. Nine in ten of Fortune 1000 companies currently have ongoing investments in AI. So you may be wondering: how could there possibly be another AI winter?

Almost every organization is a data organization. Yet, as data proliferates, data ecosystems have become increasingly complex, making it harder for organizations to control and prevent data quality issues from occurring. Data Observability gives organizations the ability to monitor data usage throughout the data ecosystem.

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The Data Mesh is a fascinating approach to designing and developing data architectures – and it’s generating a lot of attention and discussion in the data world. The concept, introduced by Zhamak Dehghani, challenges the status quo idea of a centralized, monolithic data architecture. Instead, advocating for a decentralized domain-driven design.