Adi Andrei has more than 20 years of experience building real-world intelligent systems for NASA, Phillips, Unilever, British Gas, Sixt and others. He is an expert in Machine Learning, Artificial Intelligence and Process Optimisation and has become a thought-leader in Data Science, known especially by his two Patents and three Certificates of Recognition for contributions to science from NASA. Despite his endless achievements in the field, Adi describes the mystery involved in Data Science and how it should be seen, by its human face. The human race did not stop evolving when it reached homo sapiens. Even though physically we may be identical to our ancestors from 200,000 years ago, when we look back at how much our view of the world and ourselves has changed; even in the last few millennia, there is no doubt that, as a group, we are going through a continual process of evolution in awareness of our world and of ourselves. This is reflected in our way of life, and especially nowadays in the technologies we are using. Everything we do in Data Science is not new. It has been done in different ways since the 50s in fields like operations research, industrial engineering, statistics, and artificial intelligence. What is new is that we have reached a point in time where we produce more data in a day than we had done in the whole of humanities history before. But data by itself cannot directly be used; we need to make sense of it, to understand what it means. The concept of Data Science emerged as a way to define this need and the tools and technologies used to address it. As a profession, it has appeared in a point of time in our evolution when we are moving into a new level of awareness and it is bringing with it new concepts. Data Science is as mystical as you can get in an everyday business environment and Data Scientists are sometimes being seen as magicians and revered almost as a new kind of priesthood. For full access to Adi's interviews alongside our other features and research. Download our white paper 'Identifying the Human Face of Data Science' below. Download your copy of 'Identifying the Human Face of Data Science'
04 Jul 2019
"Data quality is free. It’s not a gift, but it’s free. What costs money are the unquality things – all the actions that involve not getting data quality right the first time and all the actions to correct these data quality issues.” These words uttered by Philip Crosby carry more weight today than ever before. With the GDPR dominating the headlines, organisations across the country have had to really think about their data protection under the ‘compliance’ agenda but for me, it goes much further than regulations and box-ticking exercises. As Chief Customer Officer, I frequently get asked what the most important part of the customer journey is and I genuinely believe the vital part rests on your data quality, way before the journey even begins.You have to really care about the customer experience you create, that's where it starts, but the enabler is the data. Caring just isn't enough without good data and good process. People are always shocked by this response but what surprises me is the sheer number of people who fail to connect data quality with the customer experience as it’s what streamlines our processes, enables us to meet our commitments and increases trust from our customers. We are forever finding ways to improve the way in which we handle our own data quality and were recently awarded the Cyber Essentials PLUS certificate. This is a huge achievement for us as a business and provides further evidence that we are serious about protecting our customers data and they can trust us do to so. I’d love to hear your opinions on data quality – do you agree it is essential for a great customer experience?
24 Jun 2019
Abdel Whab Turkmani is the co-founder and CTO of Util which is on a mission to democratise impact investing, existing to support an investment community that is increasingly being assessed on both its financial and non-financial practice and performance. Abdel brings experience from the Financial Services industry and founded the organisation having graduated from the University of Oxford with a degree in Computer Science and a keen interest in Machine Learning and Data Science.He supports the idea that the term Data Scientist is both too broad and essentially, redundant. 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. How can one person be a Machine Learning Engineer, Data Engineer, Researcher and Analyst? We are hiring at Util but we don’t hire Data Scientists. We hire for three different roles within our Data Department. Different job descriptions, different requirements and different responsibilities. The term Data Scientist is so broad that it’s impossible to find someone who can do everything but organisations seem to believe they can find someone to do all three. You need Data Engineers to build the data pipelines, Machine Learning Engineers to build predictive models, and depending on how advanced your technology is you might need Researchers (Research Scientists) to constantly explore new ideas, keep on top of the state-of-the-art and evaluate the applicability of new models. In some cases, you also need the Analysts who are able to look at the output of models and understand and interpret them in a commercial context. Within those roles, you also have further specialisms. For example, you have Machine Learning Engineers who are specialists in language, image, vision, sound, reinforcement learning or others. That’s not to say that all of the individual roles set above do not intersect with one other. They integrate together tightly as anyone who joins the Data Science team will have to be cross-functional, they need to understand what everyone else does but have an edge towards one direction. Everybody has to know a little about everything but you need to know a lot about one thing. To a certain level of sophistication, you can easily transition between the different roles but once you get to an advanced stage you need the specialty and it’s highly unlikely that one single person; or rather a single Data Scientist, is that knowledgeable. The parallel would be something like Natural Science. At some point, perhaps 800 years or so ago, there would have been people who are “Natural Scientists” but today, as the field has developed, you can no longer be a Natural scientist. You have to be a physicist or a biologist, something more niche and specific. The same thing is now happening within the Data Science field as it continues to mature. The field has developed enough depth to merit specialists, and the search for the ultimate Data Scientists is increasingly impossible. For full access to all of our interviews and our white paper on Identifying The Human Face of Data Science, please download a copy below. Download our white paper
12 Jun 2019
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24 Apr 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 recently produced a white paper on the Human Face of Data Science and have decided to host an event for thought-leaders and professionals across the world to come together and discuss our research. We will take a 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. To find out more about the event please get in touch, and in the meantime - request a copy of our white paper focused on the human face, of Data Science. Download a copy here
05 Apr 2019
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. Download 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. Download 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 download 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