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As artificial intelligence (AI) continues becoming an area of interest for many enterprises, more and more companies are now coming to terms with the importance of big data and having the right skills and people in place to fulfill their goals. Specifically, there has been a significant surge in demand for data scientists in many companies as AI, as well as its application in machine learning (ML) and predictive analytics, among others continue gaining momentum in organizations.

Over the past decade, there was a misconception that the existence of big data in a company can give it actionable insights that are necessary for improving business outcomes. However, as time passed, reality became quite the opposite as it became clear that the availability of big data without the correct plans, tools, skills, and people was just a recipe for more confusion than ever before. As such, the most crucial part is data scientists who would take advantage of big data and get value, that is needed by business in its operations. Since 2016, there has been a massive growth in data scientist jobs as organizations position themselves to make an impact in their business operations by leveraging data. Growth has been between 15 and 20 times between 2016 and 2019.

Despite the availability of a capable data scientist team, data engineers have also proven crucial in the current age of data. However, although you might have both of these professionals in your organizations, they must be aligned to work as a team and with one another. Increasing demand for data scientists has led to a shortage of talent across the organizations. Therefore as organizations continue to adopt AI projects where almost 80% of projects related to this area have to do with big data-related activities, organizations are now looking for more data scientists and engineers to fill these positions.

As these projects continue increasing with time, companies are looking for and fighting for scarce data scientists. As a result, salaries and bonuses for skilled data scientists continue skyrocketing, and learning institutions are now introducing data science courses due to significant demand. The question, however, is, are data scientists alone capable of turning things around? Well, even if there is a fantastic team of data scientists in your organization, you will still need to turn ideas into production. This means that you must build a team of different professionals to work with one another for your data-related insights to be turned into an opportunity.

Data scientists are individuals who are responsible for extracting new ideas from the insights they get through data that a company receives from different corners every day. They participate in understanding and manipulating data to enhance positive outcomes. For this to be accomplished, they carry out various tasks that include data mining, statistical analysis, organizing, and interpreting data for them to identify trends and other relevant information.  Unlike data engineers who dwell more on systems, data scientists concentrate more on data. They are masters of statistics, AI algorithms, probability and data analysis. They take advantage of different algorithms to make meaning out of massive piles of data.

With the rapid increase of big data and machine learning in the world today, new opportunities and challenges keep arising. The first phase of dealing with the challenges and taking advantage of the emerging opportunities in this area is to find innovative and skillful thinkers who are going to make sense out of the data. These people are the data scientists, and the reason why they are needed is to help an organization find new opportunities.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

Website: scottkoegler.me/

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