Embed Or Centralise? Where To Build Your Analytical Capabilities Paid Members Public
When a business reaches the point when it decides it needs dedicated analyst or data science capabilities, one of the first questions that gets asked is ‘where should we put it’? There is no universal answer but there is another question that serves as a good starting point; should the
When Not to Use Analytics Paid Members Public
In my fifty weekly posts so far this year, almost every single one has advocated for the application of analytics and artificial intelligence (AI). While this is often the most sensible course of action, it is just as important to consider when not to use data. As they say: if
Data Product Managers, Not Just Project Managers Paid Members Public
As we reviewed last week [https://qstar.ai/elements-of-an-effective-data-analytics-function/], building an impactful data, analytics, and AI capability requires a range of new roles and competencies. While the scope and responsibilites of roles such as data quality analyst or data architect are usually easy for the business to grasp, data product
Elements of an Effective Data & Analytics Function Paid Members Public
What does it take for an organisation to be able to perform analytics in a scalable (read: beyond Excel) manner? When I worked at Google, my instinctive answer would have been that you need analysts and a platform for them to work with. Easy. Yet working in the real world
Why We Also Need To Talk About Analytics Ethics Paid Members Public
Over the last few years there has been an increased focus on data ethics. People are waking up to the fact that in many cases it is their data and that organisations should respect this ownership. This means asking for consent to collect, store, and utilise this data, and similarly,
CDO? Sadly, Often the CCO is the Real Data Authority Paid Members Public
I am not a seasoned writer. If I was I would save the punchline for the last paragraph, forcing you to wade through another six hundred odd words. Instead, I will given those of you who want to close this tab the opportunity to do so. When I refer to
Analysts & Data Scientists, How to Choose Your Next Role (Part 4 of 4) Paid Members Public
Part One [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-1-of-4/] | Part Two [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-2-of-4/] | Part Three [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-3-of-4/] | Part Four Welcome to the final part of our four-part series on choosing your next data science or analytics role. We have covered a lot of ground so far: from choosing the
Analysts & Data Scientists, How to Choose Your Next Role (Part 3 of 4) Paid Members Public
Part One [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-1-of-4/] | Part Two [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-2-of-4/] | Part Three | Part Four [https://qstar.ai/analysts-data-scientists-how-to-choose-your-next-role-part-4-of-4/] Welcome to the penultimate installment of our four-part series on choosing your next analytics or data science role. So far we examined Location & Timing in Part One [https://qstar.