Judith S. Hurwitz is president and CEO of Hurwitz & Associates, a research and consulting firm focused on emerging technology including big data, cloud computing, service management, software development, computing management, and security. She is a technology strategist, and author of books such as Smart or Lucky? How Technology Leaders Turn Chance into Success and several “For Dummies” books.
You've been in the IT industry for a while. What changes have you seen?
There have clearly been a lot of changes. When the industry first started, computing was relegated to a small number of people who had either an understanding or an ability to do something with it. It was the beginning of a revolution.
The biggest change and benefit has been the democratization of technology, where technology has gone from being something that could only be used by a few people to something where millions and millions of people can use it to do significant things.
I would also call the “abstraction of complexity” one of the greatest changes. Technology used to be so complicated that nobody could understand it. A lot of that complexity is still there, but it’s now at such a low level that you never have to come in contact with it.
Your 2011 book was called Smart or Lucky. For IT managers, what do you see as the distinction between being smart and being lucky, and how should they strive to be either?
You see companies that say the reason they’re successful is they hired the smartest people. Others say they were at the right place at the right time.
If you look historically at the technology industry, the ideas coming out of Google or Amazon today are the same ideas people tried to commercialize 20 years ago, but the technology wasn’t mature enough to support the ideas yet. Companies that had the same ideas 20 years later got really lucky and they were successful.
Once you’ve become successful is when you have to get smart. You have to be able to take what you’ve learned, take what’s made you successful, and sometimes destroy it and move beyond it to something brand new. It’s really hard. You’re successful, you start a marketing plan, you convince the market, and you convince yourself that you’re the best thing the world has ever seen. You start to believe your own mythology.
And that’s where companies that have been successful because they were at the right place at the right time get in trouble, because they don’t change. They believe they’re invincible and nobody will take them on. Companies that have nice little cash-cow products hold onto them for dear life because they make money.
On the other hand, smart companies say, while that product has done well for us, the marketplace is changing and if we don’t move from that product to something brand-new, someone will do it for us. That’s one of the most difficult things that companies have to overcome. Companies must be willing to say we’re going to move away from something that’s made us successful in the past, but we have to deal with a changing marketplace.
Which emerging technologies most intrigue you right now and which ones show the highest potential for return?
Right now, I’m working on a new book on the topic of cognitive computing. In a traditional application, you have to know ahead of time what all the data is and how it can answer questions for you in terms of your business. Cognitive computing takes a lot of data, maybe from a lot of different areas, and begins to learn from the data and create applications that have the ability to anticipate change and help predict and understand the future.
For example, cognitive computing could figure out diagnoses for new treatments of cancer. Right now, an individual doctor has a certain amount of knowledge based on practicing for a long time. He may have read some interesting journal articles over the past few months. So when he goes to treat the patient, he will apply that knowledge. But there’s more new research than any one individual could ever access.
In a cognitive system, you would feed the system tons of data from journal articles, textbooks, and research, and put them together and analyze them and continue to learn from that data. This would allow doctors to discover new treatments that they didn’t know about yet or that may be too new for anyone to understand the implication.
You could apply this to how you understand traffic patterns in the city. You create a travel site where instead of telling it, “I want to go to Kansas Thursday and come back Saturday and stay in a hotel downtown,” you tell it “I want to go someplace that’s fun, has good activities for children, it’s warm, and I can go anytime in the next three months.” There’s no travel site where you can say “Give me some suggestions.” It’s a machine that learns and transforms itself based on those learnings and what you tell it.