You may think this crazy but I posed this very question to a panel of thought-leaders at the Cloud and Cyber Security Expo yesterday and not only did the panel support the idea, the audience were in unanimous agreement. The panel was formed of Richard Freeman of JustGiving, Alejandro Saucedo of The Institute for Ethical AI and Machine Learning, Mohammad Shokoohi-Yekta of Stanford University, Claus Bendtsen of AstraZeneca and Krish Panesar of Diabetes Digital Media and they presented their case for investing in skills for machine learning success which was fascinating. Once the talk was over, the panel opened the room for questions and I couldn’t help but ask what they thought of the current landscape within Data Science. Today we have more jobs than suitable candidates and more requirements than skills. We have current ‘scientists’ who were developers, guerrillas or engineers before the trend-cycle who are not remotely equipped to do all of the things now required of them. While I don’t think there’s a quick fix for the shrinking talent pool, I am an advocate for devising a list of more specific mandates under an umbrella term of Data Scientist that cater for each branch of ‘Data Science’ as we know it. Be that a Data Engineer, Data Analyst or Researcher, a scientist’s job description is surely a combination of all three and more? I have recently produced a comprehensive white paper on this very topic which offers insight, opinion and statistics on the evolution of Data Science and includes an on-page debate between thought-leaders on whether it’s time for the term to go. I would love to hear your views on the matter and see if you think it’s time to scrap the term and if you would like a copy of our white paper – please request a copy below. Request a copy of our white paper
14 Mar 2019
Data Science was coined in 2001 by William Cleveland who wrote ‘Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics’ but while history paints it at almost a decade old, the phrase was popularised several years later by D.J. Patil and Jeff Hammerbacher who described themselves as Data Scientists at LinkedIn and Facebook. Over the past decade, the role has been elevated in what has been widely described as the third industrial revolution. It has been coined the sexiest job of the 21st century and every organisation wants one in the move to Big Data but has the role evolved past its meaning and is it time we replaced it? Patil famously described his role as ‘making big data small’ but as the role has come to incorporate so much more; with expectations rocketing and a talent pool shrinking, I explore whether it’s time to scrap the term Data Scientist altogether and replace it with a list of more specific mandates. I meet thought-leaders from across the world and ask them this very question and to my; and perhaps your, surprise, the movement is gaining momentum with experts claiming the term Data Scientist has become muddied, misunderstood, devalued and essentially, redundant. In this insight paper we will look back at Data Science before it became fashionable, discuss its vast achievements and see what the future holds. We will also inspect the people challenges facing the data space and follow its evolution to try and find a solution for the forever-shrinking pool of technological talent. Request a copy of our white paper
01 Mar 2019
I recently asked my network if the term Data Scientist had had it's day and while I thought some would agree, I didn't think it would be quite as many. 28% of professionals in my network believe it's time to eradicate the term Data Science but with the so-called sexiest job of the 21st century facing disenchantment from the people who are most qualified for the job, I caught up with some thought-leaders to try and find out why. Over the last three months I have been asking industry-experts around the world why they think it's time to scrap the term Data Scientist and thought I'd share some of the responses with you below. One professional in my network said; "This is such a relevant debate as the term Data Scientist has become muddied, a term that is so misunderstood and overused it makes me wonder if the perfect Data Scientist exists. I don’t even like to use the term anymore because actually, I have no idea what it means. It’s like a unicorn with so many required skills and specialisms that they can’t possibly exist." Another Data Science professional in my network said: “I don't like the term Data Scientist - for me; along with Big Data, it's something that's bandied about but has a different meaning for everybody - or no meaning at all - It somehow manages to be both too broad and too vague at the same time.” My white paper brings the debate to life and features more than 15 professionals, thought-leaders and industry experts who petition to keep the term Data Science or prove it's a thing of the past. To receive a copy of the exclusive paper once published, please do get in touch but in the meantime join the debate - is it time to scrap the term Data Scientist?
04 Feb 2019
We often ask our network to describe their ideal place of work from the salary to location to benefits and culture. We ask everything and anything we can to gather a better insight into a candidate’s perfect place to work – however weird that may be. We find ourselves comparing start-ups to corporates, the tech world to the Financial Services space, the wants of millennials and the needs of their elders but is anyone truly honest about what they want? Today I received an InMail that was so refreshingly candid and witty that I wanted to share it with you to remind you to go after your vision, however idyllic. I was approached by a Data Scientist with a phenomenal resume behind him. He started his message by writing: ‘I hope this isn’t too blunt, but it helps whittle down opportunities quite quickly’ – He had my attention. He then went on to summarise his perfect role in a 9-point checklist which has honestly made my Friday, presented an exciting challenge and presented a reminder to go after that dream job. This is his vision, possible yours and perhaps mine, minus the Star Wars t-shirt: 1. A workplace where I can wear a t-shirt (probably Star Wars related) and jeans/ shorts every day. 2. I can work from home as often as needed, typically I don’t but it’s nice to have the flexibility. 3. Table football and table tennis. 4. Good engineering support to get models into production. 5. A team of at least 10 Data Scientists. 6. Regular freebies – be it food, drinks or anything else. 7. No tube travel. 8. A salary of £60k (plus an additional £1,000 for every minute additional commute door to door, which is currently 25 minutes, and it will take me 15 minutes to cycle to the station.) 9. Plus, an additional £10,000 for every requirement from 1-8 that isn’t met. While I might not find a company willing to pay £1,000 a minute for not meeting demands; if I do I might not give it away, I have a pretty good chance of ticking the majority of the boxes on the list and there’s no reason why you wouldn’t be able to find your ‘perfect’ either. I’d love to hear from you – what does your ideal company look like?
29 Jan 2019
I have been exploring the idea of the Ideal Data Scientist and have surveyed more than 1,000 people in my network to see; in their eyes, what they think that person might look like. I meet with organisations daily who are all on a quest to improve their technology offering and find their very own version of the perfect Data Scientist but with each presenting a completely unique picture of who that might be, I'm definitely questioning if the ideal Data Scientist even exists. Some require a professional with a PhD and further post-doctorate while others prefer a person without a degree or commercial experience but while the requirements change massively in terms of education and social behaviour, I was keen to explore what they believed a Data Scientist should do to stand out from the crowd. The answer predominantly lays within upskilling with 83% of respondents stating the ideal Data Scientist must train themselves in how to use new programmes, software or learning. A further 46% expect professionals to speak at relevant events and another 44% claim they should contribute to content or white papers. Others believe the perfect professional uses their knowledge for the greater good by educating their colleagues on the data products they develop to promote awareness, usage and future responsibility while some expect them to be expert in relevant social media accounts. While I am starting to paint a picture of who the ideal Data Scientist might be, I’d like to hear from you and find out what you think they should do to stand out from the crowd. So, what should a Data Scientist do to get noticed? Join the conversation and request a copy of our insight paper focused on the future of Data Science.
22 Jan 2019
I have spent the last three months unpicking the profile of the ideal data scientist, finding out where they want to work and debating whether we should scrap the term altogether but now I am exploring another issue and that involves where Data Scientists should sit. In my quest to discover what the ideal employer looks like, I came across a huge obstacle that was presenting itself to my candidate network which I believe is contributing to the high turnover of data professionals and a forever-shrinking talent pool. More and more data scientists are being mis-sold a job. This is primarily due to a lack of understanding of the industry with organisations ready to join the Data Science party without reading the invite. It’s also because companies are overselling a role as technical, exciting and hands-on when it’s a simple IT role with a trendy title. Data Scientists longing for self-development are being sat on the IT desk of a company claiming to be data-driven and quite simply, they’re bored. This has made me wonder if the turnover of Data Science professionals is really linked to where they sit in a company. If we were allowing them to sit on a desk where they will learn, develop and progress as a tech professional will they be more inclined to stay on board? Are we stunting their growth by sitting them next to people they will never technically learn from? I’d like to hear from you. Where’s the best place for a Data Scientist to sit and does it really help retain talent? Please spare 60-seconds to take my anonymous survey – all results will be used in my upcoming insight paper focused on the future of Data Science.
08 Jan 2019
I recently asked my network to describe the ideal data scientist; but, the truth of the matter is - the ideal data scientist doesn’t exist. Not because of the candidate-short market we find ourselves in or the lack of professionals old enough to hold both a MSc and PhD in Data Science or relevant field but because of the wide range of requirements organisations have for Data Science professionals. Shaun McGirr is Head of Data Science & Business Intelligence at Cox Automotive. Despite holding two BA degrees and a PhD in Political Science; on the Half Stack Data Science Podcast, he urges businesses to understand whether a PhD with multiple post-doctorates is really necessary for their needs. He believes that for 99% of them, it will be counter-productive. On the other side of the spectrum sits a company I met with recently who required a Machine Learning specialist with a PhD, post-doctorate and further doctorate from a better post-doctorate group. The person must also have no prior commercial experience as they believe you are so far from the cutting edge in a commercial environment - you essentially de-skill. There is such a contrast from company to company that there is no single definition belonging to a Data Scientist causing a whole heap of trouble for the organisations looking to find them. A perfect professional with an MSc, PhD and further post-doctorate may be flawless for one organisation but they would be deemed over-qualified and out-of-touch by others. The term ‘Data Scientist’ seems to incorporate so many varied roles and responsibilities, is it time to create more specific job titles – each with their own unique requirements?
19 Dec 2018
Take a look at what our consultants have been discussing in the last seven days: Did you know less than 10% of data breaches are physical hacking attempts? “Malware is thought to be the most dangerous threat to cyber security and as illustrated below, since 2013 it has continued to rise however over the last two years we have seen a drop in Malware data breaches and I’m curious to find out why.” - Tyler Smith Is the Private Equity Space doing enough to represent women? “Presenting a shortlist which represents all genders, ethnicities and abilities while ensuring each is highly competent and completely suitable for the job can be a challenge. You can’t sacrifice competencies in aid of diversity but you can’t neglect equalities either. While it’s often a challenge, it’s something I pride myself in being able to achieve and something that Deborah Zurkow of Alianz Global Investors said reinvested my faith that my clients appreciate the effort.” – Joanne Moses Is your workplace due a transformation in 2019?“With an emphasis on transforming workplace culture, improving diversity and retaining millennial talent – my network are busy planning their transformation projects for 2019 ensuring the New Year brings with it a new lease of life for their organisations. To help with this, I have been speaking to my network about reverse-mentoring and its ability to transform a workplace - How it can help you to improve racial, intergenerational, hierarchical and gender diversity; retain millennial talent, broaden your demographic, engage a new generation and up-skill an existing workforce.” – David Garstang
17 Dec 2018
I came across this table in the Verizon 2018 Data Breach Investigations Report and was slightly shocked myself. Just 10% of data breaches; if not less, are the result of a physical hacking attempt but perhaps more surprisingly, Malware seems to be on the decline. Malware is thought to be the most dangerous threat to cyber security and as illustrated below, since 2013 it has continued to rise however over the last two years we have seen a drop in Malware data breaches and I’m curious to find out why. Are companies becoming more sophisticated with their cyber security or is there an up and coming threat on the horizon?
11 Dec 2018
This figure is always the pinnacle of debate in the cyber industry as contradictory statistics circulate on a daily basis but I have never seen it hit the 80% mark, let alone go over it. Deadly combo; attacks are becoming more sophisticated and are companies being ignorant to the challenges they face - ill-prepared to deal with the consequences? I visited the Cyber Security Summit and Expo last week and sat through two talks with software companies who combat cyber threats which can range from accidental issues such as phishing scams to a more malicious attack to purposefully detract information and use it. It was during a speech by Aruba that I heard that 84% of data breaches came from the inside. It was a thought-provoking talk as it opened my eyes to the sheer number of breaches which can happen either accidentally or; more worryingly, internally. This is the fifth event I have visiting in the last two months as I love learning about new technology and exploring the processes that my network are interested in implementing. It’s my role as a Consultant to learn about common issues in the market and provide insight to my network of cyber professionals to try and solve them. But, with this statistic at the forefront of my mind, it is evident that companies need to do more to implement processes and protocol for a potential security breach. I’m keen to hear more about what you currently have in place at your own organisations. What happens if someone receives an email that doesn’t quite look right? Do your team know how to respond? Please do get in touch and let us know how your organisation works to stop insider threats becoming data breaches.
03 Dec 2018
It’s no secret that technology is a job rich, candidate short marketplace meaning organisations can be left feeling desperate for great people and great people don’t stick around for long. Despite the high turnover of candidates and the quest to find them in the first place, clients are not holding back on their requirements for the ideal data scientist. We surveyed our network to investigate what the ideal data scientist looks like and thought it might help you if you are seeking your next adventure or searching for a data scientist but not sure where to start. Perhaps surprisingly, the ideal data scientist must have a degree in maths, physics or statistics as well as a masters or PHD in Artificial Intelligence. They will also be known to run their own projects, constantly challenge and upskill themselves and own a resume that screams self-improvement. Do you agree that a desire for self-improvement is the most attractive quality in a data-scientist or will technical ability always prevail?
26 Nov 2018
I wanted to share this HBR article as it features a data-scientist leader from Seattle who argues that communication remains a critical part of data work and that while building and using deep-learning infrastructures are important, neither are as vital as the ‘ability to learn on the fly and communicate to stakeholders.’ I believe that while communication is important, it completely depends on your level of experience. If you work for a data-centric company; as many start-ups today are, communication isn’t going to be the key attribute needed of a tech professional. There will be a CTO or CDO who will be responsible for translating the data-work with the wider business allowing data scientists to focus on their strongpoints. If it’s a massive conglomerate however, it is likely that the company will require a chief data scientist to cement their place in the business and get buy-in from shareholders - this is where communication needs to come back into play. Do you think communication is the most important skill-set of a data scientist? You can read the full article here:https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
14 Nov 2018