{"id":573,"date":"2017-12-26T00:57:50","date_gmt":"2017-12-25T21:57:50","guid":{"rendered":"http:\/\/kusuaks7\/?p=178"},"modified":"2025-04-04T09:10:23","modified_gmt":"2025-04-04T09:10:23","slug":"machine-learning-data-analysts-seizing-the-opportunity-in-2018","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/machine-learning-data-analysts-seizing-the-opportunity-in-2018\/","title":{"rendered":"Machine Learning & Data Analysts: Seizing the Opportunity in 2018"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Data Science? Browse courses<\/a>\u00a0like\u00a0Data Science Training and Certification<\/a> \u00a0developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tUndoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI). As ML and AI become increasingly ubiquitous in many industries, so does the proof that advanced analytics significantly improve day-to-day operations and drive more revenue for businesses.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tYes, it\u2019s true \u2013 enterprises worldwide have shown us time and time again that there is major potential for industry change with ML and AI. But in order to bring about that change, there must be strategy involved. It\u2019s not enough to assemble a large data team and expect the results to come; in fact, becoming a truly data-driven enterprise at the core has proven to be more of an organizational challenge than a technical one. Becoming successful with data requires collaboration across teams, placing new concepts \u2013 such as reusability and reproducibility of data models \u2013 at the heart of the business.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Data-Driven Organizations - The German Example<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhile these technological and organizational evolutions are happening across the globe, there are several areas in which they are gaining particular momentum. One is in Germany, where the previous Minister for Economic Affairs, Sigmar Gabriel, announced in 2016 that \u201cdata is the commodity driving our digital age.\u201d As dawn breaks on a new calendar year, it seems truer than ever. According to a recent study by\u00a0Bitkom Research and KPMG<\/a>, approximately 60 percent of German companies have already managed to either reduce risk, reduce costs or increase revenue through the use of data science (including ML and AI). The Mittelstand is slowly but surely understanding its value, and big data technologies are inching towards widespread adoption.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tYet, despite this revolution, 77 percent of German companies still rely on small data tools (like Excel and Access) for ad-hoc data analysis. There\u2019s still a long way to go, but this figure is down 10 percent compared to 2015, which shows promise. Businesses that were ill-equipped in the past to manage large amounts of data are in the process of \u201cgearing up.\u201d This trend coincides with a rise in the adoption of data science platforms.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhat are data science platforms, exactly? Well, in order to scale, data teams need staff, structure, efficiency, automation and a deployment strategy; data science tools facilitate these requirements. On top of scalability, however, they provide the tools necessary for a data team to easily manage ever-increasing volumes of data, innovate in a competitive market, move away from error-prone ad-hoc methodology, easily reproduce processes and data projects, and \u2013 perhaps most importantly with the coming of the EU General Data Protection Regulation (GDPR) \u2013 have proper data governance and permanence in place.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The Optimal Use of Data<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBusinesses are learning firsthand the famous adage whereby 80 percent of a typical data science project is sourcing, cleaning and preparing the data, while the remaining 20 percent is actual data analysis. They are now taking steps to bring their organizations to a more optimal 50\/50 balance. Many see data science platforms as the answer and the future, realizing that having the right tools for the right job is critical to success.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tData processes often span multiple roles, from business to compliance, risk management to data science itself. So having the right tools and staff members for complex systems ensures efficient collaboration and also, very importantly, data protection (we said it once and we\u2019ll say it again:\u00a0GDPR is on its way<\/a>). As the amount of data grows exponentially, setting up a unified data strategy to scale within the company is becoming high priority.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The Importance of Data Analysis<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tData analysts play a key role in the midst of all this frenzy to help shape data-driven decision-making. Market demand for analysts and machine learning expert positions is rapidly increasing. Since 2015, there has been a\u00a0five-fold increase<\/a>\u00a0in the number of job postings advertising data science. New types of data, tools and analytical methods are all pushing the jobs of both data scientists and analysts into pioneering, exciting directions. Data analysts are moving more and more into the space of machine learning.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis is an incredible opportunity for analysts to hone and develop their skills. In order to help data analysts hop into the realm of machine learning,\u00a0Dataiku<\/a>, which provides a collaborative data science platform software, has put together\u00a0a free, illustrated guide<\/a>. Analysts can expect various content from the guide, such as:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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