Share with your friends


Analytics Magazine

ANALYZE THIS!: Mathematical modeling gives way to interesting analytics topics

Author comes face-to-face with an uncomfortable but undeniable fact: There is probably not a whole lot more complex technical work in his future.

Vijay MehrotraBy Vijay Mehrotra

One sunny Sunday morning this past October, I realized that it had been exactly 25 years since the day I completed and turned in my dissertation, a not-very-interesting treatise on closed multiclass non-Markovian queueing networks. Impulsively pulling the hardbound volume off of my bookshelf for the first time in years, I found myself thumbing through its pages, struggling to make sense of the notation and equations and proofs. Though parts of the dusty old tome seemed vaguely familiar, on the whole it still seemed to have been written by a complete stranger, someone far more intelligent, disciplined, motivated and passionate about analytical work than the middle-aged man who was trying his best to slog through it.

As it happened, a few days later I found myself attending Homecoming Weekend at Stanford University, where I had been a graduate student. This campus visit provided me with an opportunity to catch up with Dr. Chin Fang, an old friend from graduate school days. Chin is the founder and CEO of Zettar, a start-up company focused on creating high-performance data transfer tools to address what Chin calls “hyperscale data distribution.”

Though I had previously never given this kind of thing too much thought, the problem seemed obvious once Chin had described it to me: When a large amount of data is captured in one location and needs to be stored somewhere at another site, there is a fundamental challenge associated with getting these records – usually distributed across multiple files in various types of data structures – from one place to the next, often across large physical distances. The particular use case that we spent most of our time discussing was a series of experiments in which Zettar and a team of scientists at the Stanford Linear Accelerator (SLAC) used Zettar’s ZX software to transfer petabytes of data across existing network infrastructure at unprecedented speed [1].

But beyond complex field experiments (for example, think about oceanic oil and gas exploration projects, in which data is captured in the field before needing to be quickly transferred to remote on-shore locations for storage and analysis), one can also imagine many similar applications in the increasingly data-intensive and geographically dispersed world of corporate computing. Indeed, the rapid growth in network traffic from both cloud-based software and data-intensive analytics applications makes the hyperscale data distribution problem an increasingly important one. In addition, because of the substantial cost of network capacity (roughly $400,000 per month for a 100-gigabyte connection), making efficient use of this capacity is also a business imperative.

How does Zettar manage to move data at faster speeds while also more effectively utilizing available network bandwidth? From what I could glean from my one-hour meeting with Chin, Zettar’s solution features a modern systems architecture (clustered nodes, new data transfer protocols) that leverages a variety of smart algorithms and design principles. Moreover, even as Chin sought to keep our technical conversation at a high level, it quickly became clear to me that Zettar’s solution has evolved over many long years of experimenting, iterating and learning, the same way my dissertation had.

My meeting with Chin seemed to exacerbate something that had been nagging at me throughout my recent sabbatical from my faculty job. We live in the golden age of the applied mathematician/statistician/data scientist/analyst, who are developing and utilizing tools to create sophisticated technical solutions that are changing the world at an incredibly rapid rate. Because I live and work in the San Francisco Bay Area, at the very heart of the analytics revolution, there is always a certain implicit pressure to be pushing the technical envelope in some way. To stay current, both in research and in industry, there is a never-ending stream of new topics to learn about, including programming languages and packages, modern machine learning methods and advanced optimization algorithms.

Given all of this, I harbored some abstract sense that I should spend my time away on sabbatical “getting more technical.” But that’s not exactly how it worked out. Along the way, I did manage to improve my R and Python skills and get a better handle on basic machine learning concepts, all things that I hadn’t had the chance to learn as a student. But more importantly, I also came face-to-face with an uncomfortable but undeniable fact: There is probably not a whole lot more complex technical work in my future.

Why? Well, to be blunt, it has become increasingly clear that I lack the passion to continue to pursue the types of technical challenges that friends of mine like Chin love to tackle. This is a somewhat painful realization, in large part because my years at Stanford and in Silicon Valley have left me with an implicit value system in which technical work is worthwhile/significant, and that the kind of professional activities that I enjoy – like teaching data literacy and fundamental (read: simple) technical tools and data-driven storytelling skills to non-technical business students – are trivial/frivolous. As such, marching to the beat of my own drummer leaves me feeling increasingly out of step with the noisy parade around me.

Paradoxically, though each passing year finds me less and less technical than I once was, I am also more and more appreciative of my graduate school experience. In part, this is because of the many bright and motivated people like Dr. Chin Fang that I got a chance to meet while I was a student. In addition, in the process of finding a meaty mathematical modeling topic and tackling it in depth, I somehow developed a knack for unearthing and exploring a wide variety of interesting topics within the world of analytics. These are the kinds of stories that I try to share in these columns – and I am somewhat stunned to realize that I have now been doing so for a full 20 years.

Whether you just started reading this column recently or have been faithfully following along since the OR/MS Today days, thanks for joining me on this ride. And who knows what the future holds? To paraphrase Buzz Lightyear [2] from “Toy Story,” “To hyperscale data distribution – and beyond!”

Vijay Mehrotra ( is a professor in the Department of Business Analytics and Information Systems at the University of San Francisco’s School of Management and a longtime member of INFORMS.


  1. For more details, see the recent SLAC technical report at

Analytics data science news articles

Related Posts

  • 70
    The CUNY School of Professional Studies is offering a new online master of science degree in data analytics. The program prepares its graduates for high-demand and fast-growing careers as data analysts, data specialists, business intelligence analysts, information analysts and data engineers in such fields as business, operations, marketing, social media,…
    Tags: data, analytics, business
  • 67
    Benjamin Franklin offered this sage advice in the 18th century, but he left one key question unanswered: How? How do you successfully drive a business? More specifically, how do you develop the business strategy drivers that incite a business to grow and thrive? The 21st-century solution has proven to be…
    Tags: data, analytics, business
  • 66
    “Drive thy business or it will drive thee.” Benjamin Franklin offered this sage advice in the 18th century, but he left one key question unanswered: How? How do you successfully drive a business? More specifically, how do you develop the business strategy drivers that incite a business to grow and…
    Tags: data, business, analytics
  • 57
    Most business leaders today believe in the value of using data and analytics (D&A) throughout their organizations, but say they lack confidence in their ability to measure the effectiveness and impact of D&A, and mistrust the analytics used to help drive decision-making, according to a new survey from KPMG International.
    Tags: analytics, data, business
  • 57
    January/February 2018 Forum: Anxiety over Artificial Intelligence The rise of self-service analytics Advanced analytics at Kroger Deep dive into edge IoT analytics Get to know your data November/December 2017 Eyes on the road, not dashboards Basic Sales Analysis Survey sampling Healthcare and data governance Simulation Software Survey September/October 2017 Visualizing…
    Tags: analytics, data


Study: The magic of animated movies not tied to latest technology

In the nearly 60 years between the 1939 release of Hollywood’s first full-length animated movie, “Snow White and the Seven Dwarfs” and modern hits like “Toy Story,” “Shrek” and more, advances in animation technology have revolutionized not only animation techniques, but moviemaking as a whole. However, a new study in the INFORMS journal Organization Science found that employing the latest technology doesn’t always ensure creative success for a film. Read more →

Six finalists named for Edelman Award

INFORMS selected a diverse group of six finalists for the 47th annual Franz Edelman Award for Achievements in Operations Research and Management Science, the world’s most prestigious award for achievement in the practice of analytics and O.R. The 2018 finalists, who will present their work before a panel of judges at the INFORMS Conference on Analytics & Operations Research in Baltimore on April 15-17, included innovative applications in broadcasting, healthcare, communication, inventory management, vehicle fleet management and alternative energy. Read more →

Are Super Bowl ads worth it? New research suggests benefits persist

On Feb. 4, more than 40 percent of U.S. households will watch the 2018 Super Bowl game on TV. Advertisers will pay up to $4 million for a 30-second spot during the telecast. Is the high cost of advertising worth it? A new study finds that the benefits from Super Bowl ads persist well into the year with increased sales during other sporting events. Further, the research finds that the gains in sales are much more substantial when the advertiser is the sole advertiser from its market category or niche in a particular event. Read more →



2018 INFORMS Conference on Business Analytics and Operations Research
April 15-17, 2018, Baltimore


CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:

For more information, go to