Are you working in data engineering but tired of all the jargon and acronyms? Say no more, here is a list of 50 to get you started! — The world of Data Engineering has evolved significantly over the past decade. From a once humble data warehousing set-up to an entire discipline. This includes everything from data pipelines to building data lakes. The popularity of this field has increased, as has the jargon.

Lessons I wish I knew about before embarking on data migration journeys — "Life is like a box of chocolates; you never know what you're gonna get" — if only Tom Hanks knew about the perils of Data Migration (DM). …

In a rush for the modern data stack, don’t forget about legacy — I have read and written many Modern Data Stack (MDS) articles, and I am so glad that, as an industry, we are taking the challenge of Big Data with its even bigger problems head-on. …

Core Lessons That Will Ultimately Help You in Your Learning Journey — At the start of any new project/venture, you should reflect on the past and the lessons learned. Having delivered multiple complex data warehouses, data lakes and analytics projects, I learned a thing or two about what to do and what to avoid. The key to any successful project is the…

Is It All Just Hype, or Are There Learnings Too? — I remember developing a new data management strategy for a client four years ago, focusing on their existing governance, hierarchies and on-premise data stack. Although the strategy has aged like a fine wine, some elements and assumptions have been proven incorrect. The key to robust strategies is to avoid running…

Steps That Will Ultimately Help You Improve the Health of Your Data — Recently, I have been asked the question: what is even more important than the data quality? And I realised I might be providing an incomplete narrative in my Data Quality (DQ) blogs. DQ is undoubtedly an essential aspect of data; however, there are many more facets than simply improving its…

Tips and Tricks in Dealing with Common Data Quality Issues — Imagine having clean, good-quality data for all your analytics, machine learning and decision-making. Yes — I can’t imagine it either! The inherent characteristic of data is its quality, which will deteriorate even with the most robust controls. 100% accuracy and completeness don’t exist, which is also not the point. …

Different Ways of Monetising Your Data without Making Huge Investments — Every organisation I know of wants to treat their data as an asset. But they lack the imagination of what that means? It can be interpreted as having robust data governance. Or it could mean improving data literacy across the organisation. We are collecting and storing zettabytes of data. But…

Improving Data Quality Is a Balancing Act between Time and Cost — When I was running a data quality training session, an attendee asked about the frequency of Data Quality (DQ) checks. I realised it was not a straightforward answer. All data is not created equal; hence, DQ checks vary based on criticality, time, and cost. Although it would be excellent to…