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Mind the Gap: Assessing Data Quality Readiness

This is the second in a two-part series exploring data quality and the ISO 25000 standard.  OK. You’re with the program. You recognize that having quality data is important for accurate AI models. You would even concede that it’s important for other stuff too, but whatever, because AI is getting all the attention right now. […]

Mind the Gap: R.I.P. Data Governance

This is the first in an ongoing series exploring reimagining data governance. Happy New Year, and welcome back to Mind the Gap. OK, enough pleasantries. Recently, I’ve been thinking about the following observation:  Many, if not most, organizations have been struggling with the same information management and data governance challenges for decades. Certainly, we’ve seen traction in some […]

Mind the Gap: Data Quality Is Not “Fit for Purpose”

Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the rise of the data product. This month, we’ll look at data quality vs. data fitness. Everybody likes a pithy definition. Marketers describe […]

Mind the Gap: Analytics Architecture Stuck in the 1990s

Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the data chasm. This month, we’ll look at analytics architecture. From day one, data warehouses and their offspring – data marts, operational […]

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