Machine learning is a complex analytics system powered by algorithms that identify trends in data to produce actionable business intelligence and enable organizations to make fast, accurate predictions of internal processes, market conditions, consumer sentiment and more.
Its capacity to provide forward-looking, data-driven information not only supports the ability of business leaders and front-line managers to answer the question: What will happen next? (predictive analytics). It also drives autonomous decision-making (artificial intelligence).
Intel describes machine learning as a system of analytics in which algorithms mine large historical data sets for hidden insights and relationships and optimize themselves to provide increasingly accurate and reliable results as it captures new data.
“Applying machine learning and analytics more widely lets you respond more quickly to dynamic situations and get greater value from your fast-growing troves of data,” Intel predicts.
As businesses of all sizes begin realizing the value of machine learning, demand will continue to grow for data professionals with the knowledge and advanced skills to inform strategies, recommendations and decisions. A Master of Science (M.S.) in Business Analytics will equip professionals with the abilities to thrive in the world of data-driven machine learning technologies.
What Competitive Advantages Does Machine Learning Provide?
Machine learning enables companies to reduce costs, streamline operations and manage risks by automating time-consuming, repetitive tasks. That, in turn, drives productivity as employees have more time for high-investment, expertise-intensive tasks.
Beyond internal processes, machine learning provides an increasingly deeper understanding of market forces such as customer behavior, supply chain and logistics operations and cybersecurity.
How Does Machine Learning Optimize Customer Experience?
Companies with mature predictive analytics capabilities no longer rely on customer surveys, polling and focus groups to gauge brand loyalty, shopping preferences and product demand. Instead, machine learning analyzes consumers’ market interactions as they accelerate the response.
Machine learning gives companies a granular understanding of the customer experience. It opens the doors to effective customer experience models ranging from airlines delivering immediate compensation for flight delays to insurers being proactive in helping customers settle claims.
“The advantages will be substantial for companies that start building the capabilities, talent, and organizational structure needed for this transition,” McKinsey & Company notes, while “those that stick with the traditional systems will be forced to play catch-up in the years to come.”
How Can Predictive Analytics Improve Supply Chain Performance?
Government attempts to control the spread of COVID-19 demolished global supply chains, forcing companies to explore new models, including shifting from “just-in-time” management to establishing broader, multi-sourced supplier networks.
That may prevent pandemic-sized disruptions by enabling buyers to acquire materials from another source if one is cut off, but it complicates an already complex system for moving things from one place to another.
Infoworld predicts the ability to mine large and constantly growing data sets of supply chain seller and buyer behavior can deliver significant benefits and risk-reduction opportunities.
“Behavioral analytics technology can monitor the activity of billions of ships, examine ports, and study global shipping patterns to help experts solve these issues,” it says.
In a specific case, CIO reports on the value UPS realizes from machine learning models it devised to mine one billion data points daily for insights that cut waste, increase productivity and reduce risk.
During the 2021 ice storms that shut down the entire state of Texas for weeks, “UPS was able to continue to run … rebalance the network … move packages around the impact area … make a very quick recovery … and get operations back to normal,” CIO reported.
What Role Does Predictive Analytics Have in Cybersecurity?
Protecting enterprise data, technology and systems from malicious actors requires cybersecurity operations to sift through vendor alerts and identify patterns and attack vectors, consuming vast amounts of time and effort.
As attackers become more sophisticated — launching zero-day, targeted and advanced persistent threats, for instance — detection becomes both more critical and elusive.
Microsoft has developed a cybersecurity package that uses machine learning to set a baseline for network activity, then learns as new data is captured to make predictions and recommendations on identified activity.
The system, Microsoft says, can “identify anomalous activity and help you determine if an asset has been compromised, as well as gauge the potential impact on data and technology assets, identify similar systems and “evaluate the potential impact of any given compromised asset (its ‘blast radius.’)”
What Are Career Outlooks for Business Analytics Professionals?
An advanced degree in Business Analytics offered online by the University of North Carolina Wilmington (UNCW) equips graduates with the knowledge, insights and skills for these high-demand positions through coursework that includes exploration of predictive analytics as well as:
- Descriptive, prescriptive and predictive analytics
- Programming and application development
- Statistical reporting
- Data visualization
The U.S. Bureau of Labor Statistics predicts demand for data-literate business professionals will grow by 25% through 2030, adding an estimated 25,600 jobs annually. Moreover, LinkedIn lists business analytics and analytical reasoning among the top 10 hard skills companies need most and ranked demand for professionals with data skills among the fastest-growing career opportunities.
Learn more about the University of North Carolina Wilmington’s online M.S. Business Analytics program.
