The Talent Gap In Data Analytics

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The amount of data generated annually has grown tremendously over the last two decades due to increased web connectivity, as well as the ever-growing popularity of internet-enabled mobile devices. Some organizations have found it difficult to take advantage of the data at their disposal due to a shortage of data-analytics experts. Primarily, small-to-medium enterprises (SMBs) who struggle to match the salaries offered by larger businesses are the most affected. This shortage of qualified and experienced professionals is creating a unique opportunity for those looking to break into a data-analysis career. Below is some more information on this topic.

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Data-Analytics Career Outlook

Job openings for computer and research scientists are expected to grow by 11 percent from 2014 to 2024. In comparison, job openings for all occupations are projected to grow by 7 percent over the same period. Besides this, 82 percent of organizations in the US say that they are planning to advertise positions that require data-analytics expertise. This is in addition to 72 percent of organizations that have already hired talent to fill open analytics positions in the last year. However, up to 78 percent of businesses say they have experienced challenges filling open data-analytics positions over the last 12 months. In addition, 59 percent of organizations expect the number of positions requiring analytics skills to increase significantly over the next five years. It is worth noting that the number of data scientists who reported a shortage of qualified candidates in their field rose from 79 percent in 2015 to 83 percent in 2016. On the salary front, entry-level data scientists take home an average of $88,344 per year. Nationally, data scientists earn an average of $113,436 annually.

Becoming a Data Scientist

Approximately 649 four-year educational institutions offer at least one data-analytics study program. This translates to 20 percent of all institutions offering four-year programs. Currently, 92 percent of data scientists in the US have earned an advanced degree. This includes 48 percent with a PhD and 44 percent who have completed a master’s degree program. What’s more, 28 percent of data scientists have a degree in mathematics or statistics, 18 percent have an engineering degree and 17 percent have a computer science degree.

Data-Analytics Skills

The skills that data scientists require vary depending on the nature of data to be analyzed as well as the scale and scope of analytical work. Nevertheless, analytics experts require a wide range of skills to excel. For starters, data scientists say they spend up to 60 percent of their time cleaning and aggregating data. This is necessary because most of the data that organizations collect is unstructured and comes from diverse sources. Making sense of such data is challenging, because the majority of modern databases and data-analytics tools only support structured data.

Besides this, data scientists spend at least 19 percent of their time collecting data sets from different sources. For instance, collecting data related to industry-specific metrics is a common practice that tech-savvy companies undertake regularly to update their market intelligence and benchmark themselves against peers. Luckily, organizations can easily access credible data from government agencies including the US Bureau of Labor Statistics, web-based platforms that host open-source data sets, and market-research companies. Discovering discernible patterns accounts for 9 percent of the time data scientists spend at work. Other useful skills include algorithm refining/tuning (4 percent of work time) and building training data sets (3 percent of time spent at work).

Common Job Responsibilities

To start with, 69 percent of data scientists perform exploratory data-analytics tasks, which in turn form the basis for more in-depth querying. Moreover, 61 percent perform analytics with the aim of answering specific questions, 58 percent are expected to deliver actionable insights to decision-makers, and 53 percent undertake data cleaning. Additionally, 49 percent are tasked with creating data visualizations, 47 percent leverage data wrangling to identify problems that can be resolved via data-driven processes, and 43 percent perform feature extraction, while 43 percent have the responsibility of developing data-based prototype models.

In-demand Programming-Language Skills

In-depth understanding of SQL is a key requirement cited in 56 percent of job listings for data scientists. Other leading programming-language skills include Hadoop (49 percent of job listings), Python (39 percent), Java (36 percent), and R (32 percent).

The Big-Data Revolution

The big-data revolution witnessed in the last few years has changed the way businesses operate substantially. In fact, 78 percent of corporate organizations believe big data is likely to fundamentally change their operational style over the next three years, while 71 percent of businesses expect the same resource to spawn new revenue opportunities. Only 58 percent of executives believe that their employer has the capability to leverage the power of big data. Nevertheless, 53 percent of companies are planning to roll out data-driven initiatives in the next 12 months. The largest beneficiaries of the big-data era have been large, financially capable organizations, with 70 percent of Fortune 1000 companies identifying big data as key to their success compared to just 55 percent in 2014. In the same vein, just 2 percent of companies believe that big data is an inconsequential resource.

Main Employers of Data Scientists

Although data scientists can work in almost any field, the leading employers include technology (46 percent), marketing (12 percent), financial services (10 percent), corporate (8 percent), consulting (8 percent), and healthcare/pharmaceutical sectors.

Recruiting Best Practices

Data-scientist positions are plentiful; 64 percent of data-scientist job applicants report that getting a new job is very easy due to high demand for the available talent. With this in mind, organizations can partner with institutions that run data-analytics programs to implement internship or mentorship programs aimed at enticing potential hires. Other useful recruiting strategies include collaborating with recruiters/head hunters, investing in in-house talent development, maintaining flexibility when searching for talent, and partnering with service providers who already have skilled data analysts.

Companies across all industries are facing a serious shortage of experienced data scientists, which means they risk losing business opportunities to firms that have found the right talent. Common responsibilities among these professionals include developing data visualizations, collecting data, cleaning and aggregating unstructured data, and delivering actionable insights to decision-makers. Leading employers include the financial services, marketing, corporate and technology industries.

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