A new month, a new theme for our content. This time we are going to focus on an analyst role.
To kick us off, I’m delighted to share this post from our latest new guest blogger, Martin Squires.
Martin is the Global Lead on Customer Intelligence for Boots, or Walgreens Boots Alliance as they are now.
Advice from one of the UK’s top Analytics Practitioner Leaders
For over a decade, Martin has built a strong analytics capability there in Nottingham. Taking the Boots Advantage Card to one of the premier loyalty & personalised marketing schemes in the UK.
Like me, Martin has ‘been around the block’ with regards to data & analytics. Prior to Boots, he led the customer intelligence team at M&S Money and has wider analytics experience across FS. He has achieved good progress with both recruiting & developing analysts, as well as gender balance in his teams.
Whilst in Barcelona, where Martin was a keynote speaker (as summarised previously), we got talking. I’m hoping Martin will blog for us in future of gender balance in analytics teams. But I was keen to share this post too. At Data Insight Leaders Summit, Martin shared the analogy of surgeons & GPs. His post expands on that concept.
Over to Martin to share his advice, on being a data scientist or ‘just‘ an analyst, by getting all medical…
A great proxy from the world of medicine
I came across this post today, which I thought was a great proxy summary, of something which has been niggling at me for a while. Just how familiar are the frustrations, Dr Penny Wilson lays out here, in proud defence of the talents of GPs? Familiar to analysts who seem to have become fair game for accusations of somehow being “fake data scientists?”
I read the article and replaced specialist with data scientist and GP with an analyst. It really did bring home how we’re in danger, of undervaluing the bedrock of great insight/analysis teams, and their skills. I’ve edited Dr Wilsons’ words in the next few paragraphs. I think it makes an interesting read…
If Dr Wilson was an analyst, she would be saying
“The ongoing debate about data science skills seems to imply that ‘analyst’ and ‘data scientist’ are two diametrically opposed alternatives and that analyst is the lesser of the two. If you’re smart, ambitious, passionate and successful you become a data scientist. If you can’t get into anything else, or if you want the easy option, you become an analyst. It’s seen as a back-up option, not as a worthwhile career in itself.
The data scientist vs. ‘just an analyst’ dichotomy also perpetuates the idea that analysts are not ‘experts’ in their own right. That analysts are the amateur statisticians, who do the easy bits and then refer on when it gets too complicated. Analysts ARE experts.
So where do all these negative attitudes come from? Unfortunately, some commentators perpetuate these views. They refer to analysts dismissively as ‘fake data scientists’. I have heard some loudly criticise analysts for not knowing everything about a specific field. They are apparently unable to appreciate the enormous breadth of knowledge the analyst has, in other areas.
The reality is that analysis is an enormously rewarding, challenging and varied career and that no two days are ever the same. Analysts often have no idea what is going to walk up to their desk next and every business problem comes with added layers of complexity. This comes from stakeholders personality factors, business circumstances, political situations, expectations, prior knowledge of data/analysis etc.
Not only do they have to be able to manage every single business issue imaginable. They also have to be able to do it without a full suite of analysis tools/data and always to deadlines way short of the ideal. What’s more, they are not managing that stakeholder just for that issue — they are helping them in the short, medium and long-term.
Don’t get me wrong. I have a huge appreciation for data scientist/specialists. In particular, I appreciate the depth of knowledge and skills that they have. I know that they, too, want the best outcomes and that the system works best when everyone works together. However, I’d really like for people to stop asking, ‘Are you going to be a data scientist or just an analyst?’ and instead to ask, ‘What type of data scientist are you going to be?’
It’s up to all of us to change the conversation. To give analysts the respect and prestige they deserve, so that all data career choices can be seen to be of equal value.”
How many analysts do you need, compared to data scientists?
My own view is that for every specialist we need in machine learning, AI, deep learning, NLP etc, most insight/analysis teams probably need 9-10 “general practice analysts“. That is based on the estimated skills shortages from the original McKinsey Big Data report. Recruiting great analysts isn’t helped if we talk about the role as somehow not being the equivalent of a “proper data scientist.”
The analysts rallying cry
I think Dr Wilson may have accidentally written the analysts rallying cry!
“No, I’m not “just” an analyst. I’m a broadly-skilled, sub-specialised, expert data professional, providing a damn fine analysis service to my stakeholders and my business. And I absolutely love it.”
Here is that original article, to read Dr Wilsons’ own words:
“Are you going to be a specialist? Or just a GP?” As a medical student and junior doctor in my hospital training years, I was often asked this question by friends, senior doctors and well-meaning patients. It really grated on me, that one little word: “just.” I always thought I’d become a general practitioner.
What is your experience in building analytics teams?
For my part, I agree with Martin. My own experience, of creating and leading analytics teams that made a difference in businesses, is that they needed more analysts.
I’ve also talked with too many leaders, who have a form of ‘buyers regret‘ that they assumed they needed data scientists. So, that rallying cry works for me. Together with the need to develop softer skills in analysts, so they can deliver such a broad service.
What about you? Do you agree or have a different perspective on the need for analysts & data scientists to coexist?
Please do join this conversation & share your advice in the comment boxes below, or via social media. We look forward to hearing from you.