The global economy generates trillions of bytes of data daily, and enterprises of all sizes are accelerating the adoption of advanced analytics to convert the tsunami of digital information into competitive advantages. Machine learning (ML) is on the leading edge of the transformation. The technology uses mathematical instructions to optimize its processes in real-time as new data is introduced and provide decision-makers with easy-to-understand analyses of complex datasets.

In an overview of machine learning and its value, SAS reports that it’s now “possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale.”

How Do Business Professionals Future-Proof Their Careers?

Successful candidates for executive and senior-management roles will have analytical and visualization knowledge that helps bridge the gap between data science teams and business leadership in the fiercely competitive, data-driven environment. Graduates of Longwood University’s online Master of Business Administration (MBA) with track in Data Analytics program possess high-demand data-driven forecasting skills. The program’s curriculum offers insight into data management for strategic, data-driven decision-making.

The program explores real-world applications of decision analysis, statistical inference, regression and linear programming. Learning objectives include expanding students’ understanding of data analytics and visualization, probability, confidence levels and linear regression analysis. The program prepares professionals to use data analytics in order to understand market trends and dynamics and make better strategic decisions.

The U.S. Bureau of Labor Statistics (BLS) predicts employers will add over 95,000 management analyst roles annually through 2032. The projected growth rate of employment demand is 10%, much higher than that for all other career tracks.

What Is Machine Learning?

ML is a subset of artificial intelligence (AI), a broad term that refers to computer capabilities that simulate human intelligence, reasoning, problem-solving and perception. ML is an AI application that uses algorithms to enable computers to train themselves as they receive additional data. The result: Computers can extract knowledge from new data and constantly update their analytic abilities without human programming. “The more data used, the better the model will get,” Google notes.

Deep learning (DL) is another related technology. DL is a specialized subset of ML that uses layers of computational models to simulate the human brain’s neural networks’ ability to discover patterns and representations. DL applications include image and speech recognition and natural language processing.

Both technologies provide significant benefits to organizations. After reviewing ML and DL use studies from nine business functions in 400 organizations across 19 industries, McKinsey & Company concluded: “Based on our analysis, we believe that nearly any industry can benefit from machine and deep learning.”

How Is Machine Learning Used To Support Business Analytics?

ML is “a game-changer for business analysis,” according to the International Institute of Business Analysis. It notes that ML is “poised to revolutionize how companies gather insights, make informed decisions, and drive growth.” Machine learning’s algorithm-powered capability to identify patterns and trends in the continuous flow of data delivers precise and timely insights that enable the following:

  • Predictive analytics: supports forecasting demand patterns, market trends and investment opportunities
  • Personalized customer experiences: provides decision-makers with insights into user preferences, behaviors and demographics, which support meaningful, relational engagement
  • Automated decision-making: helps business leaders by optimizing routine, time-consuming tasks such as fraud detection, credit scoring and supply chain optimization

“It is important to note,” the organization warns, “that while automation can streamline operations, human oversight remains critical to ensure ethical and fair decision-making.”

Longwood’s program exposes students to various business, data-related, technology and information science topics, and graduates emerge from the program as professionals with critical foundational knowledge. Graduates gain related skills in areas like primary data analysis and visualization of data results that equips leadership with information and forecasts to inform decisions.

What Are Some Common Machine Learning Algorithms?

Writing for InfoWorld, Martin Heller explains algorithms as instructions that tell the computer what to do. It uses the example of sorting algorithms, which order datasets by defined criteria. Data scientists use dozens of statistical processes to design ML algorithms, and some of the most common include:

  • Linear regression: Predict outcomes based on mathematical representations of how input data and output change relative to one another
  • Logistic regression: Indicate if something is true or false based on given data, like whether an email is spam or not based on its content
  • Decision trees: Produce visualizations to make choices based on conditions
  • Random forests: Groups of related decision trees that provide a deeper understanding of insights extracted from datasets

“Which kind of algorithm works best … depends on the kind of problem you’re solving, the computing resources available, and the nature of the data,” Heller advises.

Through courses like Data Analytics, students in Longwood University’s online MBA with a track in Data Analytics program learn foundational knowledge in ML and data management that aid leadership teams in strategic decision making.

Learn more about Longwood University’s online Master of Business Administration with track in Data Analytics program.