July 2, 2015
I was recently on a panel at a packed PyData conference to talk about data scientists and tech in Europe. Besides buzzwords like Python, Hadoop, R, and Machine Learning, the first question on everyone’s mind was defining Data Science.
According to Vasant Dhar, Professor of Business analytics at NYU Stern, Data Science is the extraction of knowledge from large volumes of data and based on that ensuring new knowledge is actionable for predictive insights and not just to explain the past. Vice President of big data products at IBM, Anjul Bhambhri, says the data scientist role is “part analyst, part artist.” She continues – data scientists represent an evolution from the business or data analyst role. There have certainly been debates about data scientists and the evolution of the term. Famed Economist Nate Silver called it a “sexed-up term for a statistician”.
While big data and data science is a little ahead in the US, it’s largely developmental in Europe. Salaries shed some insight. KD Nuggets ran a poll in 2014 that shed light on salaries. The average data scientist’s salary in the US according to the survey is $137K. In Europe, it’s $78K. While there were limitations in the data, (only 20% response rate in Europe), what was interesting in the data is the lift in salary between the data analyst and the data scientist. Its a 77% jump for the title change in the US while in Europe its 22%.
To conjecture about the variation between both continents, it is important to note that, comparatively, big data is still a novelty in Europe. Navigating strict government regulation and data privacy in countries like Germany and the UK has made it a challenge. In fields like ecommerce, using data science to uncover trends is increasingly popular. Felix Wick from Warburg Pincus backed Blue Yonder cites some of the largest German ecommerce giants like Tengelmann use its data mining BI software.
Capgemini‘s big data report which includes a poll of CEOs and IT leaders underscores the point. The top ten biggest challenges globally include lack of clarity of big data tools and technology, absence of a clear business case, lack of sponsorship from top management, legacy systems and a lack of big data analysis skills.
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