There’s no denying it: we live in the age of Big Data. It’s the natural byproduct of the computerization of pretty much every aspect of our lives. Whether we’re shopping for groceries, visiting the doctor, watching television, surfing the Internet, or simply taking a walk while carrying a smartphone, we are generating data that can be—is—useful to someone.
The implications for business are enormous. With so many aspects of business networked, every task, every transaction, every keystroke is potentially convertible into data. That data can then be used to improve market and customer intelligence, track product usage to promote customer satisfaction, discover ways to make internal processes and operations more efficient… the list is limited only by our imagination and our Big Data skills.
An advanced degree is a great way to develop and improve those skills. A number of academic disciplines address business applications of Big Data; some are offered through business programs while others are offered through universities’ departments of computer science, mathematics, or engineering, or through a subject-specific institute. Which degree in which discipline is right for you? We’ll consider the different disciplines first, then the types of degrees.
Business intelligence is the process of collecting, filtering, and calculating business data to create an accurate description of a problem. It focuses on determining the appropriate and most accurate measurements of a problem and then reporting the results of data aggregation in the clearest and most useful means. BI professionals are critical to such processes as enterprise recording, data mining, process mining, and benchmarking. They also attack process problems like how to integrate a business’ data systems in order to aggregate data most efficiently.
Business intelligence is a great field for those who are most passionate about the mathematics and computer science aspects of Big Data business applications. It’s a field where practitioners figuratively get their hands dirty in the data and the apps used to report data. When BI creates reports, its final product is typically descriptive rather than prescriptive; prescriptive guidance is the domain of business analytics, which we will discuss next.
Business analytics applies the work of business intelligence experts to predictive models, simulations, and other analytic formulae with the goal of forecasting likely outcomes and producing prescriptive guidance accordingly. Business analytics uses data to anticipate challenges, project the results of various strategic shifts, and predict upcoming opportunities. As Pat Roche, vice-president of engineering at Magnitude Software, explains: “Business intelligence is needed to run the business, while business analytics are needed to change the business.”
Business analytics is a good fit for those interested in formulating policy. Like all Big Data disciplines, it requires well-developed skills in computer science and mathematics, but analytics also demands a solid grounding in business administration, including soft skills such as communication. That’s why many master’s level students in analytics opt for a combined MS/MBA; it provides optimal training in both the STEM and business components of the field.
As their names imply, business intelligence and business analytics focus exclusively on business-related problems. Data science programs teach broader principles that can be applied more widely, e.g. to scientific research. Data science programs typically emphasize coding, technology, and mathematics over business skills, and as such are closer to business intelligence programs than business analytics programs. Should you need to pivot later in your career, a data science degree should give you a bit more flexibility than would a business intelligence degree. This degree is sometimes offered as a computer science degree with a concentration in data science.
Statistics, like data science, is more business intelligence than business analytics. These degrees are often designated in combination with another term: statistics and data analytics; probability and statistics; managerial statistics, etc. Whether a school offers a degree in data science or statistics is often a matter of where the university’s faculty is strongest in Big Data. If it’s the computer science department, it’s a data science degree; if mathematics, it’s a statistics degree. The curriculum of a statistics program, accordingly, will likely be weighted more toward advanced math while a data science program will likely be weighted more toward computer programming . Otherwise, these are similar degrees.
Operations researchers use Big Data to solve operational problems, such as managing overhead, enhancing productivity, monitoring regulation and compliance, gauging cyber risks, improving supply chains, and managing cash flow. Experts in operations research use data mining to study how operations function on a daily basis.
Through the application of software, probability calculations, mathematical formulas, and network analyses, operations researchers create solutions to the problem, then create projection models for their solutions to predict a range of possible outcomes.
The goal is to use quantifiable evidence to reach, as nearly as possible, objective, scientifically supported guidance. Operations researchers rely heavily on a mathematical process called optimization, which uses Big Data to determine optimal solutions.
The National Association of Colleges and Employers predicted an average starting salary for 2019 MBA graduates of $84,580—provided those graduates found jobs in computer science, engineering, science, or business. (
Students considering an MBA or graduate business degree can choose from varied career paths, including those focused on financial management, data analytics, market research, healthcare management, and operations management. The analytical skills and problem-solving techniques gained from graduate level business degrees are in high demand across business sectors. ( )
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Certificate programs are less expensive and take less time to complete than do graduate degrees, and they can be useful additions to your résumé. Certificate programs are best in areas that aren’t substantial enough to support a full graduate curriculum, such as mastering a particular advanced software or operating system.
When it comes to Big Data fields, certificate programs are helpful for managers who need to work closely with and understand Big Data specialists, but they are probably not in-depth enough to launch your career in Big Data aggregation and analytics.
If you aspire to a career in business intelligence, business analytics, or operations research, you should probably consider an advanced degree such as an MS, MBA, or DBA. It’s worth noting that some MS programs will credit some or all of your certificate coursework toward an MS degree, thereby increasing the value of the certificate.
An MBA with a concentration in a Big Data field will give you a solid general education in all business functions—finance, operations, supply chain—and business skills—communication, strategy, quantitative analysis—along with a selection (often three courses) of deeper dives into a Big Data-related field. You won’t go as in-depth as you would in a Big Data MS program, but you will learn a lot more about business as a whole.
An MS is a more specialized, more technical degree than the MBA. All your coursework will focus on the mathematics, computer science, and engineering practices and principles used in Big Data business functions, and as a result you’ll take a deeper dive into the STEM aspects of Big Data than you would in an MBA program.
In short: the MBA focuses more on the why Big Data applications, while the MS emphasizes both the why and the how, a luxury afforded by not having to expend degree credits on marketing, operations, finance, and other functions. Both the MBA and the MS are useful, and distinct, degrees, which is why some students choose to pursue both.
The PhD is designed for those seeking careers in academics and/or pure research. The program includes advanced coursework followed by a dissertation, a paper or project you work on for years and then defend before a panel of experts. It’s as grueling as it sounds but if your dream is to be a leader in Big Data research, the PhD is the tried and true path to that goal. PhD programs typically require students’ full-time participation, at least during the coursework phase.
The DBA, like the PhD, is a doctoral level degree, but it is designed for working professionals rather than for future academics. The DBA trains you in advanced research, which presumably you will then apply to whichever business you work in. The degree is designed for high-level working professionals; students typically participate on a part-time basis. Some programs allow students to complete a work-related project or submit numerous journal articles in lieu of a dissertation.
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