The skills gap in Big Data analytics

Because of the era of ubiquitous information that smartphones and technology rely on, our daily life has changed forever. We will leave the data when traveling, shopping, driving, blogging and voting. All of these activities will leave our unique digital signature and we can predict our next move if we do the analysis. Likewise, companies, researchers, and the World Wide Web are also creating massive amounts of data every day. According to government estimates, electronic data of about 2 Zettabytes (250 billion DVD) is generated every year from underground physics experiments and telescopes to retail trading and Twitter postings (Mervis 22).

Finding people who know how to use big data is more important than ever as many companies are hoping to use big data analysis. This has proven to be a remarkable task, especially because the gap of data skills is large. Companies have high demand for people with analytical skills, but the actual number of people with these skills is small. This is a problem that will not disappear soon. However, finding a person with a talent for big data may not be enough; in some cases, officers and executives may spend more time looking for different talents. The key to better utilizing Big Data analysis and having more diverse workforce is the exercise capacity of analysis.

Demand for "big data" skills is dramatically increasing, and many employees lack the analytical skills necessary to handle such complex data and training. As employees rush through the desire for organization and diversity, we need to increase resources for career development skills. Many people lack the basics for writing good resume and clever interview skills required through career. We will focus on communication skills, critical thinking, collaboration, and creativity. All of these are for improving the long-term productivity of our employees.

Indeed.com has revealed that demand for big data skills has increased by 225,000% since 2009. Dr. Data scientists with computer science, statistics, analysis, and current machine learning skills are as rare as unicorns, so gaps with experts as well as filling tasks are unrealistic. Instead, I believe the industry needs to deal with this gap in a variety of ways that can create a variety of skilled experts outside the data scientist that can handle a variety of big data projects. Instead of classifying everything as a "data scientist", a broader classification method is needed to explain the various roles that a successful big data project really needs. You need to take a targeted approach to creating these more specific big data skills and actively use the skills necessary to succeed and reduce the training required for success.