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.

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…

Improve Quality of Your Data by Applying These Checks in Your Journey — Imagine you have just delivered a fancy new Data Lake with some cool Data Pipelines that bring data from all over your organisation. Now imagine your dismay when your business teams realise the data in the Lake is all garbage. Remember the adage: garbage in, garbage out. This isn't necessarily…

Improve Poor Performance of Your Data Warehouse by Applying These Steps — Poor performance is one of the key reasons why Data Warehouse (DWH) get a bad reputation. A performance improvement strategy should be in place whenever a DWH is delivered. This ensures that DWH stays relevant and meets the business end-user requirements on time. Often, slow DWH has multiple downstream impacts…

The 5 Important Steps You Will Need in Any Data Warehousing Project — Having been part of numerous complex data transformation projects, I can confirm the statistic that 85% of data projects fail. The reasons for these failures are: Changing data stacks Lack of preparedness Not having the right team Impatient stakeholders Overzealous management Lack of investment in culture change, etc. The projects…

Tired of All the Jargon and Acronyms, Here Are 25 to Get You Started — Ever sat in a meeting with technical and business stakeholders and thought, what on earth are they talking about? Acronyms like EDW, MDM or jargon like Sandboxing a solution are being thrown from one side of the room to another. Fear not! You have come to the right place to…

Top Tips and Tricks to Help You Curb Your Phone Addiction — A few years ago, “smartphone pinky” was plastered all over the news and social media. People found a dent on their pinky finger (predominantly on the middle bone) from excessive phone usage. You may do, too. I certainly have a smartphone pinky from holding my phone. It was reported that…

Old Data Quality Practices Are Paving Way for Data Observability — I feel lazy today, so I won’t Google a statistic to tell you how poor quality data is costing companies billions and gazillions. Hopefully, I am preaching to the converted— that having good quality data should be a priority for the organisation that wants to succeed in its data-driven journey. …