The 3 Pillars of AI Capabilities
AI adoption should not be seen as one single capability. In this article, I share my perspective on the 3 Pillars of AI Capabilities for Organisations. Each pillar has a different purpose, different use cases, and different ways of creating value.
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From Best Practice to Best Practical: A Fisherman’s View of Data Engineering
Why Best Practical often delivers better Data & AI outcomes than rigid Best Practice, using a fisherman vs data engineer analogy to make data architecture simple, practical, and business-focused.
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Data Governance: How Do We Make People Care?
I would like to share an “out of the box” discussion I had with a friend about how to engage stakeholders in Data Governance initiatives. For context, this was only a casual conversation. It was not a formal consultation. We were just talking during brunch.
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Building Your-Own Data Quality Process
Data quality issues often start with simple questions: why is the report delayed, why are values missing, or why do numbers not match? This article explores a structured approach to data quality, covering common causes, key metrics, architecture, remediation methods, and why data quality should be treated as a business capability, not just a technical task.
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Data Analytic Aspects
There are three aspects that need to be considered as guidance when implementing data analytics. These aspects can help an organization to assess its data analytic capabilities.
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