Category Archive: Data warehousing

Data warehousing

Understanding the role of the CDO

Given the growing adoption of artificial intelligence (AI), and companies across industry sectors wanting to become truly data-driven, having a Chief Data Officer (CDO) becomes non-negotiable. However, not every organisation truly understands what this role encompasses.

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The uniqueness of modern data quality management

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.

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Data quality is in the eye of the beholder

The quality of the data we work with has a significant impact on the quality of the insights we can extrapolate for the business. Following on from my recent mini-series on the evolving data-related roles, it was interesting to come across Edwin Walker’s article on Data Science Central titled: ‘How do different personas in an …

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More evolving data-related roles

Last month, I discussed the evolving roles of the Chief Data Officer (CDO), Chief Analytics Officer (CAO), and the Data Engineer. In my blog post this month, I will delve deeper into several of the other roles related to data engineering, as well as the evolution of the role of the Business Analyst.

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Conference report back: The practical ways data is transforming healthcare

I was fortunate to have been invited to attend the recent ‘Data & Analytics in Healthcare’ conference in Australia which took place at the end of March. The event was positioned as a ‘deep-dive into the data transformation taking place in healthcare across Australia – and aimed to explore efforts to transform the use of …

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Finding balance between data science and data engineering

Previously, I wrote about the two-tiered data lakehouse with an analytical sandbox and a curated data warehouse in the second layer – the one for productised BI and the other for data science work. I was therefore intrigued when coming across a Forbes article titled ‘Three Keys to a Harmonious Relationship between Data Science and …

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Finding value in the data lakehouse

As you may have gathered from my previous post, I have become very interested in cloud-based data lakehouses. It was therefore with a keen interest that I read the article ‘Five Effective Ways to Build a Robust Data Lake Architecture’ on Enterprise Talk. While initially I was hoping that the author was going to review …

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Rethinking the opportunities bubbling below the surface of data lakes

Long-time readers of my blog can likely recall my scepticism around data lakes when they first emerged. However, a lot of water has flowed into the space since then, motivating me to start investigating the area in-depth. My early criticisms against Hadoop-based data lakes were that they were too batch-oriented to cater for business analytics …

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The advantages of data fabrics

In my blog post last month, I started looking at the concept of data fabrics to get an understanding around what it is all about. This month, I continue with the discussion, focussing on the advantages of data fabrics. The points I have outlined below are based on a very good article written by Lori …

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Unlock new business insights through data fabrics

Seeing as I’m currently working at large for a federated organisation with significantly different and siloed business streams that are managed through a plethora of different systems – ranging from 30-year-old mainframes to modern in-cloud platforms – the topic of data fabrics is very interesting to me. Even more so given how I’m coming from …

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