Big data is immense and ever-growing. Businesses are integrating machine learning (ML) and other technologies rooted in AI advancements across their enterprises to manage the collection, analysis, and utilization of big data and the insights it offers. A comprehensive business background, supported by fluency in data management, is rapidly becoming a baseline requirement as organizations adopt ML technologies to support operations ranging from recruiting, retaining and training talent to optimizing the customer experience and cybersecurity.
The online Master of Business Administration (MBA) with a concentration in Business Analytics program offered by Florida Gulf Coast University (FGCU) equips graduates with the knowledge and insights to lead data-driven business operations. The STEM-integrated program’s AACSB-accredited curriculum examines business analytics processes that are vital to collecting and analyzing large, complex data sets and devising strategies to leverage data analytics for informed, accelerated decision-making. The knowledge and skills graduates gain prove invaluable in the evolving, data-centric business environment.
The Growth of Machine Learning and the Democratization of Data
Some estimates predict aggregate business investment in ML will exceed $309 billion by 2032 as organizations race to adopt or expand leading-edge digital innovations, representing a staggering 30.5% compound annual growth rate in the ML market. However, technology is only as good as the people who use it, so businesses are placing a premium on business analytics professionals and executives who can leverage ML analytics outputs to make smarter decisions faster and with greater confidence.
Machine learning underpins many of the advancements in AI-driven technologies that are reshaping the business world. Yet, along with related technological advances in areas like natural language processing, neural networks and large language models, many expect the potential applications of ML will continue growing exponentially, as evidenced by projected market growth figures.
For instance, combined with statistical techniques like predictive modeling and data mining, ML is a fundamental component of predictive analytics. As AI-driven predictive analytics advances rapidly, more and more businesses and people have access to the insights it offers. Analytical tools like AI-powered data visualization and creative or interactive tools like generative AI are changing how users can engage with and learn from data. This democratization of big data insights and advanced AI technologies will continue to transform commerce across the economic spectrum.
What Is ML’s Role in Managing and Analyzing the “Three Vs” of Big Data?
Data professionals highlight three main data challenges driving the rapid adoption of ML capacities to manage complicated datasets: volume, velocity and variety. These are known as the “Three Vs” of big data, and can be defined as follows:
- Volume refers to the tsunami of data that businesses must manage. Estimates suggest nearly 150 zettabytes of data was created, captured and stored in 2024 alone. As data is received, ML constantly rewrites its complex mathematical equations without human intervention to identify key patterns, trends and anomalies in near-real time, overcoming computing power limitations of previous technologies.
- Velocity not only describes the speed at which data is received but, critically, how fast it can be analyzed and distributed to decision-makers. Data has a shelf life, and ML automation ensures that value (meaning) can be derived from data quickly enough to leverage it for real-time use.
- Variety may be the most complicated of the Three Vs. ML breaks data out of the silos of source formats, such as text, databases, images, natural language, GPS locations, click streams, social media and much more. All these data formats require different means of extracting, processing and deriving value. Modern AI-driven technologies provide the computing power necessary to accomplish this, aggregate data from such disparate sources, analyze it and provide cohesive insights.
How Does ML Enhance Decision-Making to Improve Customer Engagement?
ML and AI data processes can automate decision-making to enrich the customer experience and drive sales. Amazon, for instance, reports that as much as 35% of its sales come from recommendations based on AI analysis of customer browsing and buying histories. The social media giant Instagram uses ML to track and analyze users’ views to recommend images, sponsored ads and profile suggestions that are relevant to individuals, keeping the experience fresh, personalized and engaging.
Additionally, ML, natural language processing and large language models drive the integration of generative AI in customer service. AI chatbots like ChatGPT, for instance, mimic human engagement with customers and can interact in increasingly complex ways. McKinsey & Company describes AI-enabled customer service as the new “frontier of customer engagement” that will “unlock significant value for the business — creating a virtuous circle of better service, higher satisfaction, and increasing customer engagement.”
How Does ML-driven Decision-Making Improve Operations and Efficiency?
ML applications that immediately spot trends and patterns from underperforming benchmarked operations provide data-fluent managers and executives with business intelligence insights. Those insights enable decisions that can drive efficiency, effectiveness, experience and the evolution of business itself, as TechTarget maintains. For instance, ML can improve operations and efficiencies by doing the following:
- Automating analytics that detect anomalies in historical data trends, making for a powerful tool in cybersecurity
- Running simulations based on predictive analytics, enabling business leaders to bench test solutions and leverage optimization opportunities across the enterprise
- Evaluating new information and multiple business scenarios at scale to provide decision-makers with the costs and benefits of each
Develop Business Analytics Expertise to Lead in the ML Era
Graduates of FGCU’s MBA in Business Analytics online program are equipped to leverage data and ML to drive informed business decisions. Through the program’s specialized courses like Data Management, Predictive Analytics and ML, AI/ML Tools for Businesses, and BI and Visualization Tools, students gain foundational knowledge in the theories, practices, tools and techniques essential for modern business analytics.
As organizations increasingly rely on ML and AI-driven technologies to maintain competitive advantages, the demand for professionals who can translate complex data insights into strategic business actions continues to grow. FGCU’s program positions graduates to meet this demand, equipping them with the analytical capabilities, business acumen and leadership skills necessary to thrive in today’s data-driven business world.
Learn more about Florida Gulf Coast University’s online MBA in Business Analytics program.
