Alkis Vazacopoulos had already launched a successful business analytics and optimization career when he caught the teaching bug. In a recent interview, he explained that he was “inspired by a desire to share my real-world experiences with students and contribute to developing the next generation of analytics professionals… As someone who had implemented optimization solutions in companies across various sectors, I could help students understand not just the mathematical concepts, but their practical applications and limitations in real business settings.”
Vazacopoulos served as president at Dash Optimization before joining FICO Research in 2008. In both roles, he worked closely with end-users, consulting companies and OEMs/ISVs in developing optimization solutions. His pioneering work in parallel programming for solving large-scale optimization problems for several verticals, particularly in banking and retail industries, continue to define the state of the art years after their development.
Students in the Online Master of Business Administration at Stevens learn from Vazacopoulos and other renowned management experts. The interview below indicates some of the benefits Stevens MBA students derive when they study with Vazacopoulos.
A Career Spanning Business and Academia
“My academic and industry roles have complemented each other in meaningful ways throughout my career.
“My industry experience, particularly in leadership roles at Dash Optimization and as Vice President of Research at FICO, has provided me with real-world applications for the theoretical concepts I teach in academia. Dash Optimization was at the forefront of optimization technology in the 2000s and beyond, allowing me to interact with many companies that wanted to implement optimization solutions. When I teach courses like Supply Chain Analytics or Management of AI at Stevens Institute of Technology, I can draw on concrete examples from these interactions with companies such as AstraZeneca, Pfizer and Target.
“Conversely, my academic work has enhanced my consulting practice by keeping me at the forefront of emerging methodologies and research. The time spent developing courses and mentoring students has helped me refine my ability to explain complex concepts to diverse audiences — a valuable skill when working with clients who may not have technical backgrounds.
“This dual perspective has been particularly valuable in developing case studies, such as the recent series on tennis analytics I created for students. These teaching materials benefit from both theoretical rigor and practical relevance.”
Inspired to Teach
“My decision to teach was inspired by a desire to share my real-world experiences with students and contribute to developing the next generation of analytics professionals. After years in leadership positions at companies like FICO and Dash Optimization, I recognized there was often a gap between theoretical knowledge taught in universities and practical skills needed in industry.
“The turning point came when I realized I could make a unique contribution by bringing my industry perspective into the classroom. As someone who had implemented optimization solutions in companies across various sectors, I could help students understand not just the mathematical concepts, but their practical applications and limitations in real business settings.”
Balancing Business and Academic Responsibilities
“Balancing both worlds has been both challenging and rewarding. I maintain my consulting practice through Optimization Direct while teaching at Stevens Institute of Technology. This dual role creates a positive feedback loop — my consulting work provides fresh case studies and real-world problems for my courses, while academic research keeps me connected to theoretical advancements. It allows me to bridge theory and practice in ways that benefit both my students and clients.
“I’ve structured my teaching schedule to allow dedicated time for consulting projects, and I often involve students in research that has practical applications. My courses in Big Data Technologies, Supply Chain Analytics and Applied Analytics are directly informed by current industry challenges. The tennis analytics case studies I’ve developed with Sofia Savchuk illustrate how I bring real-world applications into the classroom.
“The key to this balance has been finding synergies between academic and industry work rather than treating them as separate pursuits. When I guide a student’s research, I’m often exploring ideas with potential industry applications.”
Mentoring
“The key to effective mentoring in business analytics lies in creating a balanced approach that addresses both technical expertise and professional development.
“First, I believe in meeting students where they are. Every mentee comes with unique backgrounds, strengths and goals. Rather than applying a one-size-fits-all approach, I take time to understand their specific needs and tailor guidance accordingly. This might mean emphasizing programming skills for one student while focusing on business acumen for another.
“Second, I’ve found that real-world application accelerates learning. In my mentoring, I integrate authentic business problems that require analytics solutions. For example, I have designed projects where students apply optimization techniques to real-world supply chain challenges or develop predictive models using actual company data. These experiences help bridge the gap between theoretical knowledge and practical application.
“Third, I emphasize the importance of interdisciplinary thinking. Business analytics sits at the intersection of multiple fields — statistics, computer science, business strategy and domain expertise. I encourage mentees to develop T-shaped skills, which involve deep expertise in core analytics while maintaining breadth across related domains.
“Ultimately, I firmly believe in cultivating both technical and interpersonal skills. The most successful analytics professionals aren’t just skilled with data — they can effectively communicate insights, collaborate across teams and influence decision-makers. I create opportunities for mentees to practice translating complex findings into business recommendations and presenting to diverse audiences.
“Throughout this process, I maintain an open-door policy and provide consistent feedback, recognizing that the mentoring relationship must evolve as the mentee grows in capability and confidence.”
Business Analytics in the Real World
“Businesses can successfully integrate decision analytics into their strategic planning processes through a structured approach that blends data, technology and organizational culture. Based on my experience both teaching analytics and consulting with companies, I recommend focusing on these key areas.
- Start with defining clear business objectives: Before implementing analytics tools, companies must explicitly define what strategic questions they need to answer. Analytics initiatives fail when they’re technology-driven rather than problem-driven. The goal isn’t to use sophisticated tools, but to make better decisions.
- Build a data infrastructure that supports decision-making: Integrate data sources across departments to create a unified view. Many organizations struggle with siloed data that prevents holistic analysis. Modern cloud platforms and data warehousing solutions have made this more accessible even for mid-sized companies.
- Develop appropriate analytics models: Match the analytical technique to the business problem. Some strategic questions require predictive models, while others need optimization or simulation approaches. The right technique depends on whether you’re trying to forecast demand, optimize resource allocation or analyze potential scenarios.
- Integrate analytics into existing planning processes: Analytics shouldn’t be a separate activity but embedded in how strategic decisions are routinely made. This means adjusting planning cycles, decision protocols and meeting structures to incorporate data-driven insights at the right moments.
- Create feedback mechanisms: Establish processes to track decisions and outcomes, creating a learning loop that continuously improves your models and decision frameworks. This builds organizational confidence in analytics over time.
- Develop analytics talent and literacy: Success requires both specialists who can build models and business leaders who can interpret results. This often means training existing staff while strategically hiring specialists.
- Start with high-impact projects: Begin with strategic questions where improved decisions would significantly impact business outcomes. Early wins build momentum and organizational buy-in.
“The most successful implementations I’ve seen maintain a balance between analytical sophistication and practical business application, recognizing that the ultimate goal is better decisions, not more complex models.”
Real-World Examples of Optimization Challenges
“Having worked with companies like BASF, Procter & Gamble and Honeywell during my time at Dash Optimization and later FICO, I’ve encountered fascinating optimization challenges across various industries.
“In the early days at Dash, we were limited in our ability to solve truly large-scale optimization problems. Our Xpress-MP solver technology could handle modest-sized linear and mixed-integer problems, but many real-world business challenges simply exceeded computational capabilities.
“One particularly intriguing project involved developing combinatorial auction optimization for a major telecommunications company bidding on spectrum licenses. The complexity arose from package bidding, where the value of a bundle of licenses exceeded the sum of individual values due to geographic synergies. The computational challenge was enormous, with thousands of possible package combinations and complex bid constraints. We had to develop specialized branch-and-cut algorithms with custom cutting planes to make the problem tractable.
“Another challenging domain was energy unit commitment problems for power utilities. These models determined which generators to turn on/off and their production levels across planning horizons. The mixed-integer formulations grew enormously complex with features like minimum up/down times, ramping constraints and transmission limitations. Early solvers simply couldn’t handle realistic instances, forcing us to use Lagrangian relaxation techniques.
“A particularly memorable project in the early 2000s was working on the NFL scheduling problem. The challenge involved scheduling 256 games over a 17-week season while satisfying hundreds of constraints — from stadium availability and travel considerations to competitive balance and broadcast requirements. The combinatorial explosion of possible schedules made this problem notoriously difficult. Our branch-and-bound implementations initially struggled with the problem size, requiring sophisticated branching strategies and valid inequalities to effectively prune the search tree. We developed specialized symmetry-breaking constraints and heuristics to accelerate the branch-and-bound process toward feasible solutions.
“As computing power advanced and our algorithms improved at FICO, we began solving problems that were previously impossible. The parallel processing capabilities we introduced to Xpress allowed us to distribute computational tasks across multiple cores and even clusters, dramatically expanding the scope of solvable problems. Our enhanced branch-and-bound techniques could now tackle problems with billions of potential solutions by intelligently exploring only the most promising branches of the search tree.
“Perhaps most interesting was watching the evolution from batch optimization to real-time decision systems. Early models ran overnight, providing next-day recommendations. Today’s systems optimize decisions in milliseconds — enabling dynamic pricing, real-time fraud detection and personalized marketing optimization.
“The journey from computational constraints to today’s capabilities has been remarkable, though each new capability seems to uncover even more complex business challenges waiting to be solved.”
Emerging Trends in Business Data Mining
“Based on my industry and academic experience, here are the emerging trends in data mining that business leaders should be watching.
- Generative AI integration with traditional analytics: Beyond standalone applications, generative AI is being incorporated into established analytics platforms to enhance data interpretation and visualization. This creates more accessible interfaces for business users to interact with complex data.
- Agentic AI systems: AI systems that can autonomously plan and execute sequences of actions are emerging as powerful tools for data mining. These agents can continuously monitor data streams, identify patterns and initiate appropriate responses without constant human supervision.
- Automated feature engineering and model selection: As data complexity increases, automated systems that can identify relevant variables and select appropriate models are becoming essential. This reduces the technical expertise needed to derive value from data mining initiatives.
- Ethical AI and responsible mining frameworks: With increasing regulation around data usage, companies need robust governance frameworks for their data mining activities. This includes explainability mechanisms and bias detection tools integrated into mining processes.
- Edge computing for real-time analytics: Processing data closer to where it’s generated allows for faster decision-making. This is particularly valuable in manufacturing, logistics and other environments where immediate insights drive operational efficiencies.
- Domain-specific pre-trained models: Rather than building analytics capabilities from scratch, companies are leveraging industry-specific pre-trained models that can be fine-tuned for particular business contexts, dramatically reducing implementation time.
- Federated learning and privacy-preserving techniques: These approaches allow organizations to gain insights from data across multiple sources without centralizing sensitive information, addressing both privacy concerns and data silos.
- Natural language interfaces for data exploration: Beyond traditional dashboards, conversational interfaces are making data mining more accessible to non-technical stakeholders, democratizing access to insights.
“The most successful organizations will be those that view these trends not as isolated technical initiatives but as capabilities to be integrated into their strategic decision-making processes. The key challenge remains bridging the gap between advanced analytics capabilities and practical business applications.”
Proudest Achievements
“I’m most proud of how I’ve been able to bring together my academic training and industry experience throughout my career. During my time at the Graduate School of Industrial Administration at Carnegie Mellon, I learned the critical importance of collaboration between disciplines and how to incorporate ideas from different fields into my work. This interdisciplinary perspective has shaped my approach to problem-solving ever since.
“I’m particularly proud that our work on scheduling algorithms continues to be the state-of-the-art in solution methodology. The research I conducted with Professor Egon Balas on job shop scheduling and shifting bottleneck procedures has had lasting impact in the field of optimization.
“Another significant achievement has been my involvement with Xpress, which has become the leading integer programming technology used worldwide. Through my leadership positions at Dash Optimization and ongoing consulting work, I’ve helped develop and implement optimization solutions that address complex business problems across multiple industries.
“At Stevens, I’ve been able to combine these experiences in my teaching, developing practical courses in analytics that consistently receive positive student evaluations. The case studies I’ve created with Sofia Savchuk in sports analytics, particularly focusing on tennis, allow students to work with real-world applications of data analysis and optimization techniques.
“This integration of theoretical optimization knowledge with practical business implementation represents the core of what I’ve tried to accomplish throughout my career.”
Preparing Stevens MBA Students to Harness the Power of Analytics
“In preparing Online MBA students to harness analytical tools for strategic advantage, I focus on creating a comprehensive learning journey that balances technical skills with business application.
“First, I design coursework that progressively builds analytical competencies, beginning with foundational concepts and progressing to advanced applications. For example, I introduce students to business analytics principles before moving on to specialized tools, such as predictive modeling or optimization techniques.
“Second, I emphasize real-world application through case-based learning. By analyzing how companies like Amazon and Netflix leverage data for a competitive advantage, students develop the ability to recognize patterns and identify analytical opportunities within their own organizations. I’ve found that industry-specific case studies resonate particularly well with working professionals.
“Third, I integrate hands-on experience with contemporary tools. While theoretical understanding is important, practical proficiency with platforms like Tableau, Python or SQL is essential. I design assignments that require students to manipulate actual datasets, interpret results and make recommendations.
“Finally, I prioritize the communication of analytical insights. The most sophisticated analysis provides no value if it can’t influence decision-makers. I structure assignments that require students to translate complex findings into concise executive summaries and strategic recommendations.
“Throughout this process, I create opportunities for peer collaboration that simulates the cross-functional teams students will encounter professionally. This approach ensures graduates can not only conduct analysis but can effectively champion data-driven decision-making within their organizations.“
Critical Skills for Future Business Analysts
“The most critical skill for business analytics professionals today is the ability to translate complex analytical insights into actionable business decisions. While technical proficiency in programming languages and statistical methods is essential, what truly differentiates exceptional analytics professionals is their ability to bridge the gap between data and business value.
“This requires several interconnected capabilities: strong critical thinking to identify the right business questions; technical versatility to apply appropriate methodologies; contextual understanding to interpret results within industry frameworks; and perhaps most importantly, communication skills to convey findings to stakeholders in compelling, non-technical terms.
“As analytics becomes more democratized through automated tools and AI, the true value comes not just from producing analysis but from asking the right questions, synthesizing insights across domains and driving organizational change through data-informed storytelling. The most successful analytics professionals I’ve worked with combine technical excellence with business acumen and strong interpersonal skills.“
Bolster Your Skillset With an Online MBA From Stevens
Stevens Institute of Technology is a leader in STEM education. Its Online MBA offers a tech focus that distinguishes it among business master’s programs. The school’s proximity to New York City enables it to attract faculty from the nation’s leading finance and business center, also enhancing students’ networking opportunities.
Enroll at Stevens to study with expert practitioners and academics, such as Alkis Vazacopoulos. Contact an enrollment advisor to learn more about the program and its admissions process, or, if you’re ready to apply, start your online application today.