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Analytics Magazine

Analyze This: Thoughts on collaboration and communication

January/February 2015

Vijay MehrotraBy Vijay Mehrotra

Item: I recently spent an afternoon visiting the Bay Area Advanced Manufacturing hub. Located in a long-shuttered auto manufacturing plant, BAAM brings together a dozen or so companies that are all engaged in activities that involve 3D printing, a world that has been growing rapidly for the last several years, albeit from a small base. Filling a large industrial space that has been largely empty for decades (the bottom floor was converted into a strip mall sometime in the 1980s), the BAAM hub features a variety of firms, including developers of 3D scanners, 3D design firms, companies specializing in material procurement and recycling, a 3D design software training firm, and a full service production house for complex physical items.

The lead company in this hub is Type A Machines, a company whose specialty is the development and deployment of 3D printers. Type A Machines’ CEO Espen Sivertsen gave me a very interesting take on the benefits of this type of co-location, both for his company and for its partners. Perhaps the most obvious source of synergy: In addition to selling its hardware, Type A also runs a server farm that enables other companies within the hub to manage high-demand periods by doing some or all of their 3D printing literally right down the hall (not only without any shipping costs but also without acquiring additional hardware that would likely sit idle during slower times).

In addition, by being physically close to innovators who are using its products, Type A receives constant feedback on where the reliability problems are with their leading-edge machines, what these customers are doing with 3D printing, and what their future needs might be. Similarly, by being in close proximity to Type A, the other hub companies have an awareness of its product direction, an opportunity to get access to early models of new printers, and the chance to collaboratively solve problems as they arise.

For example, Sivertsen shared an interesting story of a partner who needed to print something that was officially too big for Type A’s Series 1 printer. Through hallway discussions, the two companies came up with the idea of rotating the design by 45 degrees, after which they managed to successfully print a very valuable prototype for the partner right there in Type A’s server farm.

I had originally gone to BAAM looking for an analytics story about capturing and analyzing data to support improved product reliability, an area where I had done some research [1] in the past. However, for an industry in the very early stages of maturation, more of this type of data is clearly being transmitted in analog rather than digital format, between people who are deeply knowledgeable and passionate about their adjacent/overlapping areas of expertise.

Item:
After a long career as an operations research leader at P&G [2], Glenn Wegryn is currently a principal at Analytics Impact LLC, as well as president of the Analytics Section of INFORMS. Though I have only met him briefly once, I have been a huge fan of his for a long time. As one of the 1,000-plus members of the Analytics Section, I am thrilled to have someone of his experience and stature leading us. Given his strong track record of successful project outcomes, I definitely listen when he speaks [3].

Anyway, I recently came across an email from INFORMS containing Wegryn’s “Top 5 Analytics Predictions for 2015”. While the whole list was interesting, No. 4 was the one that caught my eye: “Collaboration and Communication (aka the soft skills) will emerge as the difference-maker….”

Item:
During the last week or so, I have seen a couple of interesting blog posts from Vincent Granville, the creator of Data Science Central (www.datasciencecentral.com), billed as “the leading social network for big data, business analytics and data science practitioners.” Granville is an active and innovative data scientist with a lot of interesting ideas, and I definitely pay attention to what he has to say.

In the first of these blog posts [4], Granville’s suggests that if you were trained as a statistician in the classical sense [as he was], very little of what you have learned is actually all that useful if you are trying to make a living as a data scientist (for the O.R. fans reading this, note that he does state that “data science uses some operations research”). Despite its intentionally provocative title (“Data science without statistics is possible, even desirable”), this was actually a very thoughtful piece that argued for a less dogmatic and more utilitarian approach to tackling data. The crux of his intellectual objection is most clearly stated near the bottom of the article when he writes that, “old statistics use a top-down approach, from model and theory to data, while new statistics or data science use a bottom-up approach, from data to model or algorithm.”

The second of these posts [5], featured a list of “10 data science predictions for 2015” originally created by the Institute for Advanced Analytics (http://iianalytics.com). This list also had a heavy emphasis on collaboration and communication.

So what? There seems to be violent agreement about the value of collaboration and communication, and yet we too often give it little more than lip service. Even the term “soft skills” is inherently pejorative, the modifier essentially mocking the noun to which it is attached.

I would suggest looking at things a bit differently. Like Sivertsen and his colleagues at Type A Machines, we as analytics professionals are constantly trying to gain acceptance for new technologies from people with different skills and capabilities than the ones who created them. To do this successfully, we must cultivate our ability to see the world through their eyes and to help them realize how we might be able to help enable them to be successful. These are skills, with no modifier needed.

But if I was required to add an adjective for these skills, the word that comes to mind is “survival.” Because we will always need to be innovating in collaboration with our allies and customers, for in the age of software we can expect solutions to be commoditized quickly. Lest you imagine otherwise, take note of the last two predictions for 2015 on the IIA list:

  • Analytics, machine learning, and cognitive computing will increasingly take over the jobs of knowledge workers.
  • Automated decision-making will come of age in 2015.

In other words, innovate or die, my friends. And good luck trying to do it successfully in isolation.


Vijay Mehrotra (vmehrotra@usfca.edu) is a professor in the Department of Business Analytics and Information Systems at the University of San Francisco’s School of Management. He is also a longtime member of INFORMS.

REFERENCES

  1. Mehrotra, V., Grossman, T. A., “O.R. Process Skills Transform an Out of Control Call Center into a Strategic Asset,” Interfaces, Vol. 39, No. 4, July-August 2009, pp. 346-352.
  2. See http://www.cbsnews.com/news/how-operations-research-drives-success-at-pg/ for
    an excellent overview of Wegryn’s work at P&G.
  3. For example, check out his excellent presentation from the Spring 2012 INFORMS Analytics Conference entitled “Driving Competitive Advantage with O.R.” at https://www.informs.org/Community/Analytics/Videos/Analytics-Process-Presentations.
  4. http://www.datasciencecentral.com/profiles/blogs/data-science-without-statistics-is-possible-even-desirable
  5. http://www.datasciencecentral.com/profiles/blogs/10-data-science-predictions-for-2015

 

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