#data catalogue

A well-implemented data catalog is a game-changer for large organisations struggling to make sense of data. It empowers users to find, understand, and trust the information they need to make informed decisions. But diving headfirst into a full-blown catalogue rollout can be a recipe for disaster. Here’s why phasing your data catalog implementation is crucial for success. Focus on Quality…

When it comes to populating your data catalogue, should you prioritize sheer quantity of entries, or meticulously focus on quality? ask Neil Burge on LinkedIn. While I have seen both strategies taken, my experience firmly leans towards quality being the crown jewel. I’ve seen first hand the pitfalls of prioritizing quantity. Users overwhelmed by wading through a vast data dictionary brimming…

Introduction In a recent poll, I was asked (seriously) whether I felt that data governance needed to be rebranded. The inference: In spite of its importance, data governance, struggles to get traction due to the name. The issue, in my judgment, is more a question of implementation rather than branding. Data governance, often regarded as the framework that ensures data is reliable, accessible,…

As a data management specialist, you understand the importance of efficient data organization and accessibility. Data catalogs play a crucial role in enabling organizations to maximize the value of their data assets. In this article, we will delve into the advanced features of data catalogues, highlighting their significance and benefits in today’s data-driven world. Table of…

Introduction A data catalog is a powerful tool that allows businesses to effectively manage, organize, and unlock the true potential of their data. With more and more data being generated every day, businesses can struggle to make sense of their data assets, missing opportunities to deliver data-driven insights and new data products and exposing themselves to data risks. This is where data…

Data Scientists only add 30 days of value (on average) before changing jobs.

A recent post by Alastair Adam on Linkedin discusses the disastrously high churn rate of data scientists. He asserts that most data scientists change jobs every 19 months, on average. Around 12 months is spent getting up to speed – getting to know the data landscape, build relationships with key stakeholders and build some understanding of available data. And then, adds Niel Burge – “And in…

Data quality remains a bug-bear for data analysts

The other day I came across an entertaining pair of posts written last year by Megan Dibble describing her Life as a Data Analyst. Megan takes a slightly tongue-in-cheek look at the challenges facing data analysts including struggles with statistics and coding, and the ubiquitous politics between IT and business. The bulk of her challenges are related to data integrity. Based on conversations…