Colm O'Grada

This is not an article about how to be a good leader; there are many great examples and perspectives on that broader topic elsewhere. This is an article outlining what I believe are the qualities that make a good data leader; both good for their team and good for their

The global coronavirus pandemic has led to unprecedented scenarios in almost all aspects of life, throughout the world. A battle to reduce the spread of infection, keep mortality low and maintain both economic activity and public order requires critical decision making in highly ambiguous spaces. This pandemic is also the

With the explosion of interest in AI across businesses of all kinds, particularly in traditional industries, the emergence of new platforms and solutions have helped make AI more accessible. The latest versions of frameworks like TensorFlow and PyTorch have made model building more straightforward with clever abstraction hiding unnecessary technical

At the heart of it, the purpose of analysis is to drive better actions through informing better decision making. In this article, I will focus on one of the most important parts of this process; making sure the output lands well and drives action. When is the right time to

A few months back, we shared a series of articles to help analysts and data scientists choose their next role (parts 1, 2, 3 and 4). Here, I expand on that to look at analytics and data scientist leadership roles. Whilst many of the same principles apply (I encourage you

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

Part One | Part Two | Part Three | 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 right location and time in Part One, the right industry and company

Part One | Part Two | Part Three | Part Four 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, and Industry & Company in Part Two. In Part Three, we shift our focus

Part One | Part Two | Part Three | Part Four Welcome to Part Two in our four-part series on choosing your next data science or analytics role. In Part One, we looked at the question of role Location & Timing. While these can be the biggest deal breakers, this time around we

Part One | Part Two | Part Three | Part Four While jobs in analytics and AI can be both exciting and lucrative, employee attrition in these roles tends to be high. It is not uncommon to see data scientists move on after only 12 to 18 months. Often a lack of data

We like to think that this blog, while born out of a trivial New Year’s resolution, has managed to become a reasonably serious outlet. That said, we fully appreciate that today’s post might read like satire, so unbelievable is the topic. It concerns a study published in the

In a quote frequently misattributed to W. Edwards Deming, one of the fathers of process improvement, ‘In God we trust. All others must bring data.’. But what if someone brings data and we don’t like what the data tells us? Dealing with the results of analysis when it doesn’

I was in Tokyo a few years ago on business and grabbed a beer with a former colleague of mine. He had moved to Japan from the UK a few months previously and was settling into this new environment. I was curious to learn more about the business culture differences,

Have you ever tried to build a strategy around a difficult topic and found yourself overwhelmed by the decisions in front of you? Perhaps it seemed like there was no way forward without either causing pain, dropping something important, or backtracking on previous gains you had made. Did any complex

To transform your business with data, you need build your capabilities and resources carefully. The hardest part is making sure you have the right people onboard. If you’ve worked in the analytics space for a while, you’ll have come across characters you’ve learned to avoid. I call

In his book ‘Drive’, Daniel Pink discusses the key drivers of motivation in the workplace. In many ways, the three drivers he identifies also represent the reasons employees stay longer in their roles: Autonomy: Our desire to be self directed and have ownership over our work.Mastery: The urge to

With even the most traditional businesses embracing analytics, an explosion in data volume has left many decision makers swamped by information. It’s hard to drive the right actions when you’re drowning in a sea of useless metrics. Defining these metrics and how they get used can be complicated,

Wrapping up a life sciences PhD is no joke; it commits you to an all-or-nothing minimum four years with little income and tough hours in the lab. When you’re deep in the middle of a PhD program, it can be hard to appreciate the transferable skills and experience you’

Do not let your data lake become a data swamp! This timely warning has been heard frequently over the past years as companies have invested heavily in novel ways of capturing and collating data. While it might feel like one buzzword accosting another, it cannot be denied that some organisations