Data quality shouldn’t be a case of waiting for an issue to occur in production and then scrambling to fix it. Data should be constantly tested, wherever it lives, against an ever-expanding pool ...

As such, today’s data teams need clear, comprehensive workflows to find, analyse and resolve data quality and reliability issues from ingestion through to consumption.

While companies may share common ground when it comes to their data quality problems, data quality tools and strategies are not one-size-fits-all solutions to the problem. Each company should ...

As bad as these issues can be for data quality, they are preventable, and Lee says the best way to avoid problems is to have preventative measures in place. “It all starts from how the data is ...

Analyst Robert Kramer continues his series on enterprise data technology with a detailed discussion of why data quality is so important, and how companies can achieve it.

Data quality in healthcare can directly affect patient outcomes, physicians’ decision-making abilities and more. Unfortunately, there are many examples of data quality issues running rampant in ...

As organizations increasingly adopt AI, with foundation models having become commoditized off-the-shelf entities, an obsession with data quality will make all the difference.

Big-data observability startup Monte Carlo Data Inc. is the latest technology company to jump on the agentic artificial intelligence craze, launching a suite of Observability Agents designed to automa ...

We talk to Cody David of Syniti about how to ensure data quality in datasets for AI, why a ‘data-first’ attitude is key, and the quick wins an organisation can gain in data quality.