DECEMBER - 2019CIOAPPLICATIONS.COM8QUO VADIS DATA SCIENCE?ou have probably experienced it yourself. One of the most frequent questions people ask when they meet you for the first time is "What do you do?" Not so long ago, I have struggled to precisely define my profession. Is it data analytics? Data science? Big Data? Machine Learning? Data Mining? Predictive modeling? Even Artificial Intelligence (AI)? Or is it just simple statistics or data analysis?The most recent excitement about AI due to increase in computational power and the successful application of deep learning in computer vision and natural language processing has made explaining my profession much easier. It is no surprise that all the other aforementioned data science areas (let's use this term in the lack of better word) also received a lot of attention in recent years, so people could finally relate to some things I was saying. At the same time, this created a great deal of noise when lots of people started to discuss these areas without fully realizing what they really entail. You can find proliferation of articles, online courses, Master programs, job postings, news, videos about all these fields. You can't go to any major conference in any field without hearing about Big Data, Machine Learning or AI from a keynote or other speakers.There were even a few movies that promoted popularity of these fields as well, with MoneyBall, Her, ExMachina, Margin Call, The Big Short probably being the most popular. So, are we in a bubble?it is nowhere near as bad as housing or the Internet bubble not so long ago, since you still cannot hear a cashier at the supermarket or a gas station attendant telling you it is a smart investment to startan analytics CIO InsightsCXO Insightsin my opinionALEKSANDAR LAZAREVIC, VP OF ADVANCED ANALYTICS & DATA ENGINEERING, STANLEY BLACK & DECKER, INC. [NYSE: SWK]Ycompany or invest in a cloud provider. We are probably in the fifth inning of the bubble, and we all know that most of the money is made in the later stages of these bubbles.Despite all the hysteria, there is unfortunately no standard what data science actually is and what responsibilities a data scientist's job should include. If you go to Linkedin, you will find over 1 million of people in a data science related profession, but their backgrounds, skills and responsibilities differ quite a bit. Variety among job postings for the same level of data science related professions is even more prominent. Furthermore, there are currently over 250 programs in US that offer graduate degrees in data science or analytics but their curricula are quite different.Feeling all the publicity and hype about data science as well as the peer pressure or expectations imposed by Wall Street and shareholders to invest in these technologies, executives started to talk about the value that could come out of Big Data technologies and they jumped on this bandwagon without fully recognizing what it takes to run successful analytics. Although some executives exercise all due diligence and try to properly educate themselves about the world of AI/Machine Learning/Data Science and other Big Data technologies, most of them read only a few HBR, Forbes, WSJ articles and they start believing they understand what it all means. Unfortunately, the truth is quite opposite. Indeed, Initiative for Analytics and Data Science Standards (IADSS) states that the number one reason of all the data science confusion, noise and disappointment is lack of mentorship, where 71% of direct data science managers lack knowledge to help technical development. Surrounded by all these factors, executives typically start by trying to hire a few experienced data scientists from Big data-driven tech companies and a lot of people with PhD / Master degrees in technical fields.However, due to a growing gap between demand for these people and actual number of professionals in these fields, they may simply not get the best people. According to a few recent studies by Quantum Crunch, KPMG, McKinsey and IBM, Big Data Analytics skills are scarcest to find and there is estimated current shortage of 150K data science jobs.Another big problem is huge difference between data driven culture in big tech companies and the rest of the pack. While Big-Tech companies have data in their blood and most of their employees are analytics savvy, the remaining companies are simply not in the good position to successfully leverage data they have due to several challenges:1. their data typically sits is multiple legacy systems and Aleksandar Lazarevic
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