In recent episodes, the industry-influencing Hadooponomics podcast—produced by Arcadia Data and Blue Hill Research—has featured interviews with Big Data thought leaders discussing the mandate for diversity in the data-driven enterprise. Their insight has touched on everything from team makeup to data interpretation approaches.
In the modern enterprise, business users are bringing new perspectives to data-driven decision-making. And the more perspectives, the better. The challenge is hearing, consuming, and integrating objective influence from the right voices. That’s especially true in the world of Big Data, whether we’re identifying deployment success criteria, establishing tangible performance metric benchmarks, idealizing value-chain workflows, or just interpreting data.
In this article, we’ll explore some of the ways diverse perspectives can enhance the Big Data industry.
Cornelia Davis, CTO for Pivotal’s Transformation Practice, recently noted that we all carry inherent biases, particularly with regard to gender. What’s most important is ensuring—well, ultimately ensuring—that those predispositions we carry don’t get in the way of doing what’s right.
“Every one of us has biases that are just ingrained in us because we, as human beings, we get so much data coming in that we need to categorize,” explains Davis. “And when we categorize, we risk including biases in those categorizations. And so every one of us has implicit biases. I had colleagues of mine come up… and say, ‘You know what, I signed my son up for coding camp this summer. I never thought to sign up my daughter.’ To which I always respond, ‘You’re signing her up tomorrow, right?’ … ‘Yep, signing her up tomorrow!’”
Now picture the reality of working in an enterprise tech development environment that hasn’t always presented itself as a shining example of diversity in the workplace. Davis cites a telling example of gender bias in software development.
“Voice-recognition systems—initially—were engineered by men, and they were designed, and they were tested by men,” says Davis. “So when the first voice-recognition systems came out they couldn’t recognize female voices at all. They simply couldn’t hear them. Female voices could not be heard because none of the data that they used for development or testing was data of women speaking.”
Noted techno-ethicist and Chair of Ireland’s Open Data Governance Board Emer Coleman acknowledges the gender-inequity challenge in tech, and calls out the industry’s undeserved exceptionalism: The way to make things better is to ensure everyone is able to contribute.
“I don’t think technology should be indulged in some special way,” said Coleman. “That what, it’s so special that it’s only for men? And because the world is now being created by technology and by software, that needs to represent the voices of both sexes.”
So how do we as Big Data stakeholders get past the potentially success-constraining limitations of our own narrow minds? It all starts with perspective. Well, that and maybe some instruction in good behavior.
“Just like we would not put a medical doctor out into the world without having had his or her education in ethics, we need to do the same for software engineers,” explained Coleman. “As much as they are looking at their technology, I would want them to be understanding of the socioeconomic consequences of what their work is doing.”
Diversity Across Business Departments
Bob Hayes, director of content at Appuri and also a management consultant, recently conducted research into data-analysis best practices, surveying nearly 500 data scientists. Among several important conclusions about fostering diverse perspectives, his findings suggest that—perhaps commonsensically—when it comes to team efficacy, bigger is better.
“I think data science is more of a team sport,” commented Hayes in a recent Hadooponomics interview. “[In my research,] bigger teams had better outcomes in their data science projects. When you consider, if you have more people on the team you have different perspectives, you have diverse skills bringing to bear on your problem.”
It’s not just the diverse team of data scientists that benefit the data-driven enterprise. The new paradigm is about business people…business leaders making decisions with—and about—data.
In Big Data development, technical innovation can drive technology adoption, with business-model rationalization sometimes taking a back seat. But that trend is flipping: The road to insight can’t be reached via a cool technology searching for purpose. Ensuring enterprise data-initiative success requires buy-in from the consumers of that data…the line-of-business leads making the data-driven decisions.
Giving business users direct data access isn’t a new idea: Look no further than the rise of self-service analytics solutions designed to empower business users by reducing their dependence on data analysts. Those business influencers (yes IT, I called them “influencers”—get used to it) must bring the diversity of their own points of view and skillsets to bear on data value delivery. And even when those diverse business-users’ perspectives are defiantly non-technical, they still must be considered.
“I try to be very pragmatic,” said Dr. John Johnson, founder and CEO of Edgeworth Economics. “I’m not trying to turn the world into a bunch of statisticians that can run derivatives in their head. But for more of an everyday audience, there’s an awful lot of basic skills and approaches which involve awareness, thinking, developing some intuition, and not being afraid of some basic details that could actually be really informative.”
In Big Data, tech providers always pitch that bigger is better, as long as everything scales accordingly. And that’s true when it comes to diversity in the Big Data enterprise and technology development environment: We succeed—with new technology, with new data interpretation, with new data-driven delivery—when contributions come from disparate viewpoints. Bigger is better, especially when we’re talking about a table that scales to accommodate a widening breadth of perspectives.
Toph Whitmore is a Blue Hill Research principal analyst covering the Big Data & Analytics technology space. His research interests include technology adoption criteria, data-driven decision-making in the enterprise, customer-journey analytics, and enterprise data-integration models. Before joining Blue Hill Research, Toph spent four years providing management consulting services to Microsoft, delivering strategic marketing project management leadership. More recently, he served as a marketing executive with cloud infrastructure and Big Data software technology firms. A former journalist, Toph’s writing has appeared in GigaOM, DevOps Angle, and The Huffington Post, among other media. Toph resides in greater Vancouver, British Columbia, Canada, where he is active in the local tech startup community as an angel investor and corporate mentor.
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