Data science is one of the most lucrative practices for organizations, but companies must take note of these five factors moving forward.

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As one of the top tech jobs with the best career opportunities, data scientists have become one of the most coveted jobs across industries in recent years. Taking the no. 1 spot on Glassdoor's Best Jobs in America list for the past four years, tech professionals are scrambling to land this sought after job position.

SEE: Building an effective data science team: A guide for business and tech leaders (free PDF) (TechRepublic)

Data science is relevant and important to any business that is churning out high volumes of data, which has lead to the rapid growth of artificial intelligence (AI) and machine learning adoption, said Ryohei Fujimaki, founder and CEO of dotData, a leading company focused on data science automation for the enterprise.

"Whether it's a financial services company that wants to mitigate risk, a retailer attempting to predict customer purchasing behavior or a software company attempting to mitigate customer churn, the use-case for AI and machine learning in the world of enterprise are predicated on an effective data science strategy," Fujimaki said.

Understanding data science means recognizing the limitations that often come with an effective data science practice, Fujimaki noted. "Chief among these is the lack of expert talent that is caused by the combination of high level of complexity of data science and the high demand for skilled data science experts," he added.

Some of the top skills necessary for data scientists include coding, big data analysis, statistics, machine learning, natural language processing, data manipulation, exploratory data analysis, and more, reported TechRepublic's Alison DeNisco Rayome.

In terms of soft skills, effective communication, collaboration, and a strong educational background are also often necessary to succeed as a data scientist. However, like everything in the tech world, data science is changing and evolving.



To help the enterprise prepare for the future of data science, Fujimaki outlined the following five key factors shaping the data science industry.

1. Making data actionable for data science Poorly prepared data is one of the biggest obstacles to data science success. In order to accelerate data science projects and reduce failures, CIOs and CDOs must focus on improving the quality of data and in providing data to data science teams that is relevant to projects at hand and is actionable 2. Shortage of data science talent While data science remains one of the areas of highest growth for new graduates, the need far surpasses available supply. The solution is to continue to accelerate hiring, while also looking at alternative means of accelerating the data science process and democratizing access to data science for other skilled professionals in areas like BI and analytics. This is where automation in data science can have the biggest impact. 3. Accelerating "time to value" Data science is an iterative process. It involves creating a "hypothesis" and then testing it. This back and forth approach involves a number of experts—ranging from data scientists to subject matter experts and data analysts. Enterprises must find ways of accelerating the data science process to make this "try, test repeat" process faster and more predictable. 4. Transparency for business users One of the biggest barriers to adoption for data science applications is the lack of trust on the part of business users. While machine learning models can be very useful, many business users don't trust processes that they don't understand. Data science must find ways of making ML models easier to explain to business users and easier for business users to trust. 5. Improving operationalization One of the other barriers to the growth of data science adoption is how hard it can be too operationalize. Models that often work well in the lab don't work as well in production environments. Even when models are deployed successfully, continuing growth and changes in production data can negatively impact models over time. This means that having an effective way of "fine tuning" ML models —even after they are in production—is a critical part of the process.

For more, check out How to fail as a data scientist: 3 common mistakes on TechRepublic.

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