Three Questions To Ask About A Master’s Degree In Business Data Analytics
June 12, 2017
Enjoying quantitative analysis could indicate a good match for pursuing a master’s in business analytics or a master’s in data analytics. In the last few years, the number of graduate programs in data science has grown from only a handful to more than 170.
“Our research shows that more companies today are formalizing the role of data scientist, one specifically dedicated to generating game-changing insights from the growing mass of data in the organization,” says Michael Lock, in the April 8, 2016, article “Is a Big Data Degree Really Worth It?” on AberdeenEssentials.com. The vice president and principal analyst at the Aberdeen Group, a technology and services company, says, “Other research suggests higher overall salaries and a lucrative career path for those in the data scientist/data analyst role, as well.”
Four years ago, the Harvard Business Review called the position of data scientist “the sexiest job of the 21st century,” and roles in the field continue to explode.
As society continues to be ruled by technology, the demand for people who can interpret and manage big data will keep growing.
The analytics technology and solutions market is predicted to grow nearly 12 percent by 2020, with worldwide Big Data revenue of more than $200 billion. The U.S. business data analytics market will reach more than $95 billion by that time. By 2025, annual data created worldwide is expected to skyrocket to 180 trillion gigabytes.
The top five industries for business analytics through 2020 include banking, discrete manufacturing (production of finished products), process manufacturing, federal/central government, and professional services.
What To Ask
When considering a Master’s Degree in Business Data Analytics, here are some questions to ask:
1. What jobs will I be qualified for when I graduate, and how much can I make?
Graduates with MSBDA degrees may pursue careers as information security analysts, management analysts, program analysts, management consultants, market research analysts, operations research analysts, financial analysts, marketing analysts, software engineers, Big Data platform engineers, information systems developers, platform software engineers, data quality directors, business intelligence analysts, quantitative analysts, or pricing and revenue optimization analysts.
The newest titles in the field include citizen data scientist, IoT (Internet of Things) specialist, data hygienist, data orchestrator, paraprofessional analyst, and machine teachers.
The median annual salary for titles in the field of business data analytics ranges from about $62,000 to $112,000, according to the Bureau of Labor Statistics (BLS) in 2016. However, a 2017 guide produced by technology staffing firm Robert Half predicts significant increases in salaries for data scientists (from $116,00 to $163,500) and Big Data engineers (from $135,000 to $196,000), specifically.
2. What’s the demand for business data analytics and what industries are hiring?
By 2018, the U.S. is predicted to face a shortage of 250,000 data scientists and two to four million analysts and managers who can interpret and make big decisions using Big Data, according to a 2016 study by the McKinsey Global Institute, a private-sector think tank.
Hiring rates for predictive analytics and data science teams have continued to increase over the past few years, with 89.5 percent of quantitative teams planning to hire new employees in the first and second quarter of 2017, according to a study by executive recruiting firm Burtch Works. Industries doing the most hiring include consulting, tech/telecom/gaming, and advertising/marketing.
3. Do I want to work in technology or management?
Before beginning a career in Big Data, students should decide whether they want to follow a highly technical path or pursue a management and people-centric role.
On the technical side of big data and analytics, employees may work in database management, systems management, or application development.
In database management, individuals work with and prepare data, develop data organizational schemes, help with technical definitions of data and data relations so it can be appropriately aggregated, and keep data current and secure. Common job titles include data analyst, data modeler, database administrator (DBA), and data architect.
In systems management, employees typically have a background in engineering and/or programming, and enjoy implementing, fine-tuning, and maintaining system hardware and software, and operating systems. Common job titles include systems programmers and systems engineers.
In application development, workers use programming languages and tools to extract meaning from unstructured data. Common job titles include application developers, big data managers, and data scientists.
On the management side of Big Data and analytics, employees may work as business analysts or chief data officers (CDO).
Business analysts work with end business users in the company. They develop models that explain how data will be used, as well as study the organization’s business processes and determine when, where, and how analytics reports are delivered.
Managers of analytics departments step out of their offices to meet with end users and identify new uses for information.
Finally, CDOs coordinate corporate-wide information usage and analytics, operate as C-level officers of the company, and often report to the CEO or board of directors.
Business data analytics is an exciting, growing field that offers many diverse positions in a variety of industries. An advanced degree can open up job opportunities and a higher earning potential.
Students in the online Master’s of Science in Business Data Analytics program at Maryville University’s John E. Simon School of Business can advance their skills in data analytics, warehousing, mining, and visualization, gaining practical knowledge that can immediately be applied in the workplace. Curriculum includes data analytics, database principles, data warehousing, and forecasting and predictive modeling, among other courses.