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Data wrangling is often used to describe what data engineers do. However, a universally accepted definition of the concept has proven difficult to find. It was therefore fortuitous that I came across this piece on Simplilearn titled “What Is Data Wrangling? Overview, Importance, Benefits, and Future.” It shares some interesting and insightful points and so …
Last month, I explored the challenges created by data silos and how pragmatic approaches like a data lakehouse architecture and data steering teams can assist in breaking down the silos. In this month’s post, my attention on the topic turns to more strategic drives that can help achieve better enterprise-wide data sharing and overcome the …
Data silos are pervasive in organisations around the world. At many of the companies where I have worked, these data silos have made it difficult to ensure system integration, data governance, and effective reporting. Following an insightful industry piece I recently came across, for my blog article this month, I decided to focus on and …
Last month, I discussed the challenges and opportunities of self-service BI as an approach to enable non-tech-savvy business users to directly access data and explore it on their own. In this blog, the focus turns to some of the reasons why self-service has failed and understanding the approaches that have worked. For reference, I use …
The concept of ‘self-service business intelligence (BI)’ started gaining momentum in the early 2000s. More than two decades later, a survey by Yellowfin has found that the majority of respondents (61%) say that less than 20% of their business users have access to self-service BI tools. Perhaps more concerning, 58% of those surveyed said less …
I recently had the privilege to attend the Data and Analytics in Healthcare conference hosted by Corinium Intelligence in Australia. Following this, I wanted to use my blog piece this month to discuss some of the key lessons and insights shared during the event. I’ll jump straight in. Data governance and ethics Of course, it …
Do you know where your food comes from? Did the farmer use pesticides? Did the transport company spray preservative chemicals over your food? Did they keep it appropriately refrigerated? Would you eat food from sources you don’t trust? The same applies to data. Do you know what the lifecycle of your data entails? Was it …
Despite the hype surrounding generative Artificial Intelligence (GenAI), I am finding in my industry reading that many industry analysts are predicting that data quality (one of my favourite topics) will remain a key priority for this year – especially when it comes to data management and governance.
We have approached that time of year when it’s always interesting to delve deeper into what the analysts see in their crystal balls when it comes to the trends and technologies to keep an eye on for the new year. I have reviewed several industry articles as resources around this, and let’s just say that …
Last month I addressed how data quality is perceived by different specialists inside the organisation. This month, I turn the spotlight onto what makes modern data quality management different from traditional approaches. Edwin Walker’s Data Science Central article ‘Difference between modern and traditional data quality’ provides an excellent starting point.
