Purple people: The heart of cognitive systems engineering

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Cognitive technologies will change the way we do business. And purple people—those who possess a mix of business and technology skills—have a big role to play. Able to speak the language of both business and technology, they will serve as translators between those worlds, focusing on making cognitive systems useful in a business context.

I think that Wayne Eckerson was the first to define the “purple person” in a 2010 blog post—someone with the mix of business and technology skills that is present in many successful business intelligence and analytics people. The same idea also came up—independently, I believe—at insurance company XL Catlin. Jim Wilson, a lead data engineer at the company, was chatting with his boss, Kimberly Holmes, about the people issues the company’s “strategic analytics” group faced every day. As Holmes describes the situation, Wilson used the “purple people” analogy to describe a particular problem:

“The business people, the actuaries, know what data they need and can define requirements, but typically don’t have the skill set to design a data architecture that gives them the data they need. Technology people typically don’t understand the business requirements, but they can design the data architectures. It’s like the people in IT speak blue, the people in business speak red, but we need people who speak purple in order to create an appropriate solution.”

The name stuck at XL; so Holmes seeks “purple people” to translate the needs of the business for data and analytical systems into the high-level designs for those systems. Other people may actually develop the models and write the code, but the systems couldn’t exist without those who speak purple.

The purple people idea may have been developed to describe business intelligence intermediaries, but these people have an important role in cognitive technologies as well. I’ve referred to them in another article as people who “step in” and help to create, monitor, and modify cognitive systems within organizations. They’re at the core of the idea that humans can augment such systems, rather than be automated by them. They can bridge the business and organizational requirements for automated systems with the capabilities of technology. They’re not intimidated by automated systems and are willing to jump into the “belly of the beast” and do whatever is necessary to make them work. They’re capable in technology, but in most cases their focus is on making them useful in a business or organizational context.

There have probably always been people who bridged technical and business environments. As long as there have been complex technologies, there have been people who learned to understand them and to help apply them to solving business and organizational problems. In the Industrial Revolution, mechanics and technicians invented or improved industrial machinery to make textile mills more effective.

For example, Paul Moody, a weaver and mechanic who worked in the Massachusetts textile industry in the early 19 century, had no compunctions about stepping into that form of technology. He co-invented the power loom, invented the filling frame, bettered the “double speeder,” and improved upon the mechanism for powering the machinery. Instead of letting his weaving skills be automated by these machines, he invented and optimized new technical capabilities. His industrialist boss Francis Cabot Lowell got most of the credit (and had a mill town in Massachusetts named after him), but it was Paul Moody who made these new approaches to work successful.

Professor James Bessen of Boston University notes in his book Learning by Doing that progress in the textile industry of the time—the Silicon Valley of its day—was not just a function of new, more automated textile technologies. To make those technologies hum, a cadre of experienced people had to emerge. These people—of whom Paul Moody was certainly one—made the technologies work in context.

Bessen cites an account by Henry Lyman, a successful cotton manufacturer from Rhode Island, of the early use of the power loom and other textile machinery in New England. An unnamed weaver steps in and saves the day:

“The company had no one, at first, to start the machinery; they began to grow discouraged. The warper worked badly, the dresser worse, and the loom would not run at all. In this dilemma an intelligent though intemperate Englishman, by trade a hand weaver, came to see the machinery. After observing the miserable operation, he said the fault was not in the machinery, and he thought he could make it work; he was employed. Discouragement ceased; it was an experiment no longer. Manufacturers from all directions came to see the wonder.”

It is certainly true that similar step-in roles were necessary with other new technologies. During the difficult and expensive implementations of large, complex enterprise systems (from vendors like SAP and Oracle), many organizations sung the praises of “power users” who could connect business requirements and technological capabilities. They were the “purple people” of their generation. Their role in business intelligence and analytics is a more recent version, but they are playing the same bridging role.

Today, those who step in work not with textile machinery, but with systems and analytics that make automated decisions. In order to do that, they still have to function at the intersection of the technology and the business needs that the technology addresses. Stepping in means that these people must understand not only the technology, but also the business process into which the technology fits. Like their predecessors, these people have to be “purple,” speaking the language of both business and technology, and serving as a translator between those worlds.

We tend to assume that cognitive systems will replace people, and indeed there will probably be some disruptions in employment. But purple people will probably be safe. They will be the ones who learn how cognitive systems work, monitor their performance, and step in to make them better. They’ll probably be able to make some changes to the systems on their own, but if they can’t, they can describe the problem to a technologist. They will be the go-to folks for business executives who want to know how cognitive technologies will change their businesses.

The good news is that purple people have a future in a world of smart machines. That suggests that if you want an occupational future, you might want to make yourself more purple. If you’re a businessperson, you may want to bone up on what new technologies can do for your business domain. If you’re a hard-core technologist, you might want to think about becoming less hard-core. Programming, for example, may ultimately be automated, but monitoring whether automated programming is meeting a business need will still be a human function.

Both business and technology skills are valuable in themselves, but they are most valuable when combined. Cognitive technologies will change a lot, but they won’t change that. The future belongs to the purple—just as the past did.