In 2016, between 60% and 73% of all analytics within a company went unused. The overwhelming surge of information in the digital age has left many businesses struggling to aggregate, analyze and accurately respond to the science behind the data.
This issue is not limited to a single industry. As of 2018, of the companies in manufacturing, aviation, energy, automotive, transportation and logistics, a full 85% failed to use the information sourced from trillions of data points. Besides significant financial cost for a company, this represents resources that could have been reallocated to other areas of the business. Furthermore, according to IBM’s 2016 estimates, businesses in the United States lose $3.1 trillion annually due to poor quality of data.
Therefore, with the volume and value of data increasing rapidly, the demand for qualified data scientists and data analysts has grown exponentially. How much demand? LinkedIn ranked “machine learning engineer”, “data scientist”, and “big data developer” among the top emerging jobs. Data scientist roles have grown over 650% since 2012, machine learning engineers by more than 980%, and big data developers by 550%. According to IBM, demand for data scientists will increase 28% by 2020, representing 2.7 million job listings. Additionally, the United States Bureau of Labor Statistics estimates the employment of all computer and information research scientists is expected to increase 18% by 2028, which is faster than the average for all professions. With competitive salaries and potential for long-term growth—especially given the need for highly-qualified individuals—the field of data analytics and data-mining services represents unique professional opportunities for sustainable and successful careers.
To better understand the requirements for a career in data analysis, we will explore the variety of jobs in data analytics, the opportunities around the world in data science, the skills necessary for a career in data analysis, and step-by-step guidance for aspiring data analysts.
Types of Jobs in Data Analytics
As technology makes data more easily available and more expansive in its usage, industries requiring data analysts have grown just as quickly. While nearly every company now benefits from the collection, evaluation and utilization of data, some industries are, historically, more dependent on data analytics. Healthcare, retail, banking and manufacturing are among the industries that benefit from predictive analytics and the ability to make organizational decisions based on models predicting future events and their resulting impacts.
Data Analysts in Healthcare Science
Data analysts in healthcare science are employed in a variety of settings, from non-profits to private facilities. Their skills must include leveraging data to analyze complex medical and pharmaceutical trends, and then communicating these findings effectively to management and clients. Healthcare data analysis must be alert to opportunities for refining analytics and lowering costs. The abilities to provide actionable client analytics and to improve an organization’s data platforms are vital skills in an industry where clarity and easy access to information are precious commodities.
The requirements listed for data analysts in healthcare are a bachelor’s or master’s degree, preferably in a quantitative or healthcare-related field. Job seekers are also expected to have experience using Microsoft Excel to build models and to have experience with data visualization tools (e.g. Tableau, Qlik). Beyond this fundamental understanding of analytical tools and systems, the ideal candidate for these positions also has some healthcare knowledge and experience. For example, experience analyzing medical insurance claims with SAS and/or SQL, understanding of medical coding systems (CPT, ICD-9, DRG, etc.), and knowledge of the healthcare industry and payer systems.
Data Analysts in Retail
For data analysts In the retail industry, while the job title may be the same as the healthcare field, the skill set required to succeed is slightly different. In retail, the analyst will be tasked with constructing algorithms to optimize efficiency of the in-store experience, developing market tests to calculate and evaluate the impact of in-store marketing efforts, and creating dashboards and other visualization tools to help managers and other decision-makers within the company. With these advanced analytics, retailers can discover trends, solve problems and make well-informed decisions.
An example of a retailer using analytics is Walmart, who in 2017 introduced five ways in which big data was installed to enhance the consumer experience and improve operations:
- make the Walmart pharmacies more efficient,
- improve store checkout,
- better manage the steps of the supply chain,
- optimize product assortment, and
- personalize the shopping experience.
In the words of Walmart, “whether it’s analyzing the transportation route for a supply chain or using data to optimize pricing, big data analytics will continue to be a key way for Walmart to enhance the customer experience.” This is just one example of a retailer using the capabilities and strategic advantages provided through data analytics; proof that the rise of data analytics jobs will only continue within the retail industry.
In retail, there’s a heavier focus on the ability to handle large data sets and inventory management. Therefore, the job seeker in this industry should have a background in computer science, statistics or mathematics. They should also possess the ability to use visualization software, like Looker or Tableau. Candidates should be fluent in writing SQL, Python or R to manipulate data sets, answer questions and drive end-to-end complete analysis that helps accelerate the business and discovery inefficiencies in the operational process.
Data Analysts in Banking and Finance
For data analysts in banking and finance, there has always been a need for talented data analysts. But though they have often been thought of as simply quantitative consultants, data analysts now have greater responsibilities than ever before. Still expected to organize and analyze large amounts of data to draw meaningful insights, a data analyst is also expected to be proactive in developing modeling techniques to make the business processes smarter, and to be more aware of future trends. Additionally, analysts within finance are expected to communicate these trends—along with other data-driven discoveries about performance using predictive analytics—to stakeholders who in turn expect the financial firm to adjust according to the data’s directives. Data analysis within the financial sector has new opportunities to expand thanks to blockchain and data mining; exciting new frontiers of data-driven technology.
The financial industry uses data sets similar in size and scope to retail. However, banking analysts are expected to be more advanced in their knowledge and technological background. They will use more programming languages, be expected to understand machine learning techniques and algorithms and have substantial knowledge in applied statistics like distributions, statistical testing and regression. Additional responsibilities for data scientists in finance include using big data frameworks (like Apache Hadoop, Apache Spark, etc.), building and optimizing “big data” data pipelines, and utilizing data visualization tools. Also, job seekers should have proficiency in using query languages such as SQL, Hive and Impala. While these programs may be foreign to many students and young professionals, knowledge of these tools can be obtained through a variety of advanced education programs.
Data Analysts in Manufacturing
Analysts in manufacturing are studying data sets and creating models to predict trends and patterns involving customer behavior, the effects of seasonality and other threats to optimization and efficiency, similar to retail. However, working in manufacturing also requires a data analyst to develop frameworks and Kew Performance Indicator (KPI) metrics to monitor business and operational performance. They are also responsible for designing and maintaining databases to support new business models. An example of how big data is used to help manufacturing companies is IBM introducing artificial intelligence software that empowers manufacturing businesses to learn about the productivity of their operation, prepare for potential equipment maintenance using predictive tools and study the evolution of the marketplace for adjustments to the company’s business offerings and efforts.
Manufacturing companies seek individuals with a variety of skills and experiences. The desired skills listed for data analysts in manufacturing include the ability to merge mixed data from disparate sources and use that data to illustrate information stories in various media (Google Sheets, Tableau, etc.) that aid in the development of strategic initiatives and generate clear insights. Additionally, candidates should also have experience with scripting languages, such as Python or R, or visualization software, such as Tableau or Qlik.
While industries like manufacturing, finance, retail and healthcare may be the more traditional areas for data analytical jobs, the need for individuals with data analytical skills has spread to other industries.
Data Analysts in Government
Many agencies utilize data to measure and monitor economics, healthcare, education, military and security, the census, planning and budgeting, crime, environment and other aspects of society. Here are some examples of government agencies using data analytics:
- The Department of Education uses data mining and analytics from big data to enhance teaching methods. By using data analysts to collect large quantities of data and process the results quickly, schools and teachers receive feedback on the performance of their students for enhanced overall school performance.
- The Department of Homeland Security (DHS) uses big data strategies to aggregate and organize unstructured data to produce information that can help law enforcement. Plus, DHS employs data analysts to write algorithms to improve the technology that studies criminal behavior, prevents hacking and secures the communication channels between dozens of government agencies.
- The U.S. Forest Service uses data analytics to better predict weather, ground conditions and the factors that ultimately result in forest fires.
- The National Center for Atmospheric Research uses analytical tools to combine research and predictive forecasting to better understand how power is supplied and how the demand for energy changes based on a myriad of factors.
Data Analysts in Sports Management
As organizations have updated the sophistication behind statistical accumulation, sports teams are applying their findings to improve on-field performance as well as the data behind the fan experience that drives team revenue. Here are some examples of how data analytics has grown in sports:
- Made famous by the book “Moneyball”, the Oakland Athletics entered into the advanced analytical space years before it was popular amongst sports organizations. Since the team’s success using big data to better understand player performance and forecast statistical results, the rest of Major League Baseball has hired data analysts to overcome the out-dated methods of evaluation with new-age technological tools for measuring the talent and potential of a baseball player.
- The New England Patriots have used data analytics in recent years to better understand inventory of team merchandise, consumer behavior at the stadium, pricing of tickets, game-day staffing and the value of stadium improvements.
From the more traditional industries that utilize data like manufacturing and finance to the new fields just learning of data’s enormous value, the rise of jobs in data analytics will continue across all businesses in the coming decade. With data becoming more available, and yet more complex in its meaning and implementation, the importance of hiring qualified data analysts will only increase as a result. For those curious how to become a data analyst, let’s explore the career path in greater detail.
How to Become a Data Analyst
Let’s examine the difference between a data scientist and a data analyst. While both roles exist within the larger data science community, they do represent two distinctive roles and career paths.
A data scientist formulates hypotheses, tests them, and then from these results makes business and organizational insights. Data scientists use unstructured data from multiple, often disconnected sources and sorts that data to make forecasts through predictive modeling.
In comparison, a data analyst answers specific questions related to a business. For example, “What is the greatest source of revenue for the company?”, or “What will the company’s costs be in the coming year?” Furthermore, a data analyst works primarily with structured data from a single source, organizing and sorting the data to solve challenges the company faces by providing historical analysis and data-driven decision-making.
To become a data analyst, many people first earn a bachelor’s degree in a related field, such as computer science, social sciences, the physical sciences or statistics. The most common fields of study are mathematics and statistics (32%), followed by computer science (19%) and Engineering (16%). After earning a bachelor’s degree, one will then be expected to earn a master’s degree. Those who pursue a degree focused on data analytics will be expected to learn how to analyze complex data sets, as well as design, implement and evaluate analytical solutions.
The career path for a data analyst can often begin with an entry-level position as a research assistant or junior data analyst. This involves working with data sets, utilizing quantitative programs to gather qualitative data and creating data visualizations and dashboards. The average salary for a research assistant ranges from $25,000 to $65,000 and is often filled by graduate students or recent college graduates.
A common next step in the career path is to work as a software engineer. Though not a requirement, software engineering provides experience in designing, developing, maintaining, testing and evaluating computer programs that benefit long-term career growth.
The Online MS Program for Becoming a Data Analyst
At the Yeshiva University Katz School of Science and Health, individuals can earn an online MS in Data Analytics and Visualization and develop the skills needed to translate raw data into patterns and insights that guide strategic decision-making in a wide array of industries. With the ability to skillfully interpret data and present succinct findings, one can unlock exciting new career opportunities in a field that has incredible growth and long-term potential.